Sep 9–12, 2019

Speakers

Hear from innovative researchers, talented CxOs, and senior developers who are doing amazing things with artificial intelligence. More speakers will be announced; please check back for updates.

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Santi Adavani is a cofounder at RocketML, where he and his team are building a superfast engine for building machine learning models. Previously, Santi was a product manager and software development lead in the Technology and Manufacturing Group at Intel. He holds a PhD in computational sciences from the University of Pennsylvania. His areas of expertise include high-performance computing, nonlinear optimization, partial differential equations, machine learning, and big data.

Presentations

Semisupervised machine learning, the next frontier in AI Session

Current deep learning approaches require large amounts of labeled data. The creation of labeled data is expensive, error prone, and time consuming. Vinay Rao and Santi Adavani walk you through an effective learning method with minimum labelled data and human intervention.

Shubham Agrawal is a lead data scientist at Visa Research, where he has made significant contribution in conceptualizing and building intelligent systems that harness the power of Visa data and enhances Visa’s product offerings. He holds a MS degree in operations research from University of Texas at Austin and has 8+ years of experience in the payment domain. Shubham has a passion for innovation through experimenting with new ideas and concepts that led him to author multiple papers and patents related to this field.

Presentations

Applying AI to secure the payments ecosystem Session

Artificial intelligence has revolutionized the way we live, work, and play. With the help of AI, electronic payments have become more secure and more convenient for consumers globally—regardless of currency or form factor. Chiranjeet and Shubham explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

Brigitte Alexander is the managing director of artificial intelligence (AI) partner programs for Intel, where she’s responsible for creating a scalable and vibrant global AI partner ecosystem on Intel AI technology by attracting, recruiting, and maintaining relationships with best-of-breed enterprise independent software vendors, system integrators, and original equipment manufacturers. Previously, Brigitte led ecosystem and global marketing for Vuforia, an augmented reality platform owned by Qualcomm and then sold to PTC, and held a variety of positions, including director of partnerships, partner marketing, and product management, at such companies as Yahoo and Infospace. Brigitte holds an MBA from the Thunderbird School of Global Management and a BA from the University of California, Santa Barbara.

Presentations

Closing Remarks AI in the Enterprise: The Intel® AI Builders Showcase Event

Closing Remarks—AI in the Enterprise: The Intel® AI Builders Showcase Event

Welcome and AIB overview and growth: Impact on AI ecosystem AI in the Enterprise: The Intel® AI Builders Showcase Event

Welcome to AI in the Enterprise: The Intel® AI Builders Showcase Event

Bob Anderson is the vice president of sales with Inspur Systems Inc., promoting technologies in compute, AI, and storage. He has over 20 years of industry experience in sales and business development of data center solutions for IT infrastructure to global enterprise customers. He’s built long-term relationships with hardware OEMs, hardware and software vendors, distributors, and resellers, domestically and internationally. His experience spans across many industry verticals including cloud, consumer products, energy, healthcare, and financial services.

Presentations

Intel-based AI solutions from cloud to edge AI in the Enterprise: The Intel® AI Builders Showcase Event

Bob Anderson showcases three Inspur and Intel collaborations: bring field-programmable gate array (FPGA) inference solutions to the cloud market, Xeon-based AI appliances, and Intel NUC with FPGA.

Alberto Andreotti is a senior data scientist on the Spark NLP team at John Snow Labs, where he implements state-of-the-art NLP algorithms on top of Spark. He has a decade of experience working for companies including Motorola, Intel, and Samsung and as a consultant, specializing in the field of machine learning. Alberto has written lots of low-level code in C/C++ and was an early Scala enthusiast and developer. A lifelong learner, he holds degrees in engineering and computer science and is working on a third in AI. Alberto was born in Argentina. He enjoys the outdoors, particularly hiking and camping in the mountains of Argentina.

Presentations

Interpreting millions of patient stories with deep learned OCR and NLP Session

Much business data still exists as challenging scanned or snapped documents. Stacy Ashworth and Alberto Andreotti explore a real-world case of reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale.

Stacy Ashworth is a registered nurse and chief clinical officer at SelectData. Stacy’s professional interests lie in the use of technology to improve the quality of care through better decision making. An accomplished speaker, she has served as a contributor to the healthcare informatics and technology track of the 2016 Business and Health Administration Association meeting, performing research regarding the evaluation of glucose monitoring technologies for cost-effective and quality control/management of diabetes. She holds a master’s degree in healthcare administration with an emphasis in informatics. Postacute care, geriatrics, and coding may be her passions, but her love is firmly centered on her family of two lively teenagers, a spouse, and a couple of schnauzers to keep things interesting.

Presentations

Interpreting millions of patient stories with deep learned OCR and NLP Session

Much business data still exists as challenging scanned or snapped documents. Stacy Ashworth and Alberto Andreotti explore a real-world case of reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale.

Bahman Bahmani is the vice president of data science and engineering at Rakuten (the seventh-largest internet company in the world), managing an AI organization with engineering and data science managers, data scientists, machine learning engineers, and data engineers globally distributed across three continents, and he’s in charge of the end-to-end AI systems behind the Rakuten Intelligence suite of products. Previously, Bahman built and managed engineering and data science teams across industry, academia, and the public sector in areas including digital advertising, consumer web, cybersecurity, and nonprofit fundraising, where he consistently delivered substantial business value. He also designed and taught courses, led an interdisciplinary research lab, and advised theses in the Computer Science Department at Stanford University, where he also did his own PhD focused on large-scale algorithms and machine learning, topics on which he’s a published author.

Presentations

A framework for human-AI integration in the enterprise Session

Developments in ML and DL provided remarkable advances in the predictive capabilities of AI. However, the black box nature of the modern models creates challenges for those looking to adopt these techniques. Bahman Bahmani examines a framework and presents design and operating principles, recommendations, and best practices for human-AI integration in enterprise workflows, products, and services.

Paige Bailey is a TensorFlow developer advocate at Google.

Presentations

Getting started with TensorFlow 2.0 Session

TensorFlow 2.0 has landed. Paige Bailey walks you through TensorFlow (TF) 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. You'll use the TF 2.0 migration tool to transition a model from TensorFlow 1.x to 2.0 and deploy an end-to-end open source machine learning model.

Peter Bailis is the founder and CEO of Sisu, a data analytics platform that helps users understand the key drivers behind critical business metrics in real time. Peter is also an assistant professor of computer science at Stanford University, where he coleads Stanford DAWN, a research project focused on making it dramatically easier to build machine learning-enabled applications. He holds a PhD from the University of California, Berkeley, for which he was awarded the ACM SIGMOD Jim Gray Doctoral Dissertation Award, and an AB from Harvard College in 2011, both in computer science.

Presentations

Executive Briefing: Usable machine learning—Lessons from Stanford and beyond Session

Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details the lessons learned in building new systems to help users leverage the data at their disposal, drawing on production experience from Facebook, Microsoft, and the Stanford DAWN project.

Mahendra Bairagi is an Artificial Intelligence (AI) and Machine Learning (ML) specialist architect for Amazon Web Services, working on all things AI, ML, Robotics, and Cloud.

Presentations

Put deep learning to work: A practical introduction using Amazon Web Services 2-Day Training

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye, Miro Enev, and Mahendra Bairag detail a practical next step in DL learning with instructions, demos, and hands-on labs.

Ashish Bansal is a senior engineering manager, recommendations, leading recommendations teams working on events and trends at Twitter. He focuses on building scalable ML and recommendation systems. Previously, he was a senior director of data science at Capital One where he used AI/ML to generate insights from vast amounts of data and build interesting B2B, B2C, and enterprise products; he cofounded GALE Partners and headed the Machine Learning Group, building AI/ML based marketing automation products. He helped the company grow from 9 to 120 in two years and set up the India office. He has over 19 years of experience in the technology industry, an MBA from the Kellogg School of Management at Northwestern University and a BTech from IIT BHU.

Presentations

Recommendation systems challenges at Twitter scale Session

Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. Ashish Bansal provides you with insight into the unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task.

Tzvika Barenholtz works at Intuit’s data science organization and Intuit Futures, leading a team dedicated to advanced machine learning out of Intuit’s office in Israel. Previously, he lead product teams at Facebook, Google, and EMC.

Presentations

Data science without seeing the data: Advanced encryption to the rescue Session

Tzvika Barenholz and Induprakas Keri detail Intuit’s efforts to deploy fully homomorphic encryption (FHE) in production, which allows models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. You'll take a sneak peak at some of the optimizations and tricks that make FHE practical.

Sina Bari is a solutions architect at iMerit and a Stanford-trained reconstructive surgeon, medical technology innovator, and leading voice in medical AI development.

Presentations

Getting through the ground truth grind (sponsored by iMerit) Session

Sina Bari explores how to overcome obstacles to creating high-quality ground truth data for ML applications.

Mayukh Bhaowal is a Senior Director of Product Management at Salesforce Einstein, working on automated machine learning and data science experience for use cases such as recommender systems, forecasting, etc. Mayukh received his Masters in Computer Science from Stanford University. Prior to Salesforce, Mayukh worked at startups in the domain of machine learning and analytics. He served as Head of Product of a ML platform startup, Scaled Inference, backed by Khosla Ventures, and led product at an ecommerce startup, Narvar, backed by Accel. He was also a Principal Product Manager at Yahoo and Oracle.

Presentations

Executive Briefing: Managing AI products Session

AI product managers (PMs) are rising. With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. Mayukh Bhaowal explores new ways to manage AI products and outlines how AI executive roles are different and what toolbox you'll need to succeed in the era of artificial intelligence.

Manish Bhide is a chief architect of IBM Watson OpenScale. He’s been with IBM for more than 17 years and has worked across different parts of IBM such as IBM Research and IBM Analytics. Manish has a passion for innovation and is credited with conceptualizing several technological innovations that have made an impact on IBM’s products and offerings. Manish is a prolific inventor and has filed more than 50 patents and has published more than 25 research papers in IEEE/ACM conferences. He holds a PhD in computer science.

Presentations

Operationalize AI at scale: From drift detection to monitoring the business impact of AI (sponsored by IBM Watson) Session

With the potential to transform businesses, AI has become a strategic imperative for most enterprises. A lot of investment is toward machine learning and deep learning models to support business applications. However, as Manish Bhide and Rohan Vaidyanathan explain, these models bring about risks and uncertainties that are difficult to manage.

Lukas Biewald is the founder and CEO of Weights & Biases, his second major contribution to advances in the machine learning field. In 2009, Lukas founded Figure Eight, formally CrowdFlower. Figure Eight was acquired by Appen in 2019. Lukas has dedicated his career to optimize ML workflows and teach ML practitioners, making machine learning more accessible to all.

Presentations

Using Keras to classify text with LSTMs and other ML techniques Tutorial

Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn.

Sarah Bird is a principle program manager at Microsoft where she leads research and emerging technology strategy for Azure AI. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations research with the best of open source and product expertise to create new tools and technologies. She leads the development of responsible AI tools in Azure Machine Learning. She’s also an active member of the Microsoft Aether committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Previously, Sarah was one of the founding researchers in the Microsoft FATE research group and worked on AI fairness in Facebook. She’s an active contributor to the open source ecosystem; she cofounded ONNX, an open source standard for machine learning models and was a leader in the PyTorch 1.0 project. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She cofounded the SysML research conference and the Learning Systems workshops. She holds a PhD in computer science from the University of California, Berkeley, advised by Dave Patterson, Krste Asanovic, and Burton Smith.

Presentations

Developing AI responsibly Keynote

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Sarah Bird outlines her perspective on some of the major challenges in responsible AI development and examines promising new tools and technologies to help enable it in practice.

Chris Butler is the chief product architect at IPsoft. Previously, Chris worked at Microsoft, KAYAK, and Waze, and he was involved in AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University. He’s created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Presentations

Design thinking for AI Tutorial

Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Paris Buttfield-Addison is a cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly and is writing Practical AI with Swift and Head First Swift. He holds a degree in medieval history and a PhD in computing. You can find him on Twitter as @parisba.

Presentations

Build a self-driving car without a car: ML problem-solving with a game engine Tutorial

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine.

Live coding a self-driving car (without a car) Session

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines.

Wei Cai is a data scientist with Cox Communications, where she’s transitioned from building statistical models and doing analysis using R and SAS to developing machine learning models and processing streaming telemetry data using Python, Scala, and Java. She successfully developed both statistical models and deep learning models for the company to build a 10-year capacity planning budget, and she’s working on building models to compute to maximize network availability. She was born in China, earned her master’s degrees in actuarial science and business analysis from Georgia State University, and she’s seeking a master’s degree with a concentration in computing and analysis from the Georgia Institute of Technology. Her major areas of research interest are building deep learning models to help proactive network management.

Presentations

Long-term real-time network traffic flow prediction using LSTM recurrent neural network Session

Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM.

Arno Candel is the chief technology officer at H2O.ai. He’s the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and master’s summa cum laude in physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH Globe in 2015. Follow him on Twitter as @ArnoCandel.

Presentations

Automatic machine learning for the enterprise with H2O.ai Driverless AI (sponsored by H2O.ai) Session

Driverless AI is H2O.ai’s latest flagship product for automatic machine learning for the enterprise. Arno Candel outlines Driverless AI, explores customer use cases, and performs a live demo with custom recipes to solve a specific machine learning problem.

Yishay Carmiel is the founder of IntelligentWire, a company that develops and implements industry-leading deep learning and AI technologies for automatic speech recognition (ASR), natural language processing (NLP), and advanced voice data extraction, and is the head of Spoken Labs, the strategic artificial intelligence and machine learning research arm of Spoken Communications. Yishay and his teams are currently working on bleeding-edge innovations that make the real-time customer experience a reality—at scale. Yishay has nearly 20 years’ experience as an algorithm scientist and technology leader building large-scale machine learning algorithms and serving as a deep learning expert.

Presentations

Future challenges in human language understanding Session

One of the most important tasks of AI has been to understand humans. People want machines to understand not only what they say but also what they mean and to take particular actions based on that information. This goal is the essence of conversational AI. Yishay Carmiel explores the latest breakthroughs and revolutions in this field and the challenges still to come.

Yue Cathy Chang is a business executive recognized for sales, business development, and product marketing in high technology.

Cathy co-founded and is currently the CEO of TutumGene, a technology company that aims to accelerate disease curing by providing solutions for gene therapy and regulation of gene expression. She was most recently with Silicon Valley Data Science, a startup (acquired by Apple) that provided business transformation consulting to enterprises and other organizations using data science- and engineering-based solutions. Prior to that, Cathy was employee #1 hired by the CEO at venture-funded software startup Rocana (acquired by Splunk), where she served as Senior Director of Business Development focusing on building and growing long-term relationships, and notably increased sales leads 2x through building and managing indirect revenue channels.

Prior to Rocana, Cathy held multiple strategic roles at blue chip software enterprise companies as well as startups, including Corporate and Business Development at FeedZai and Datameer; Senior product management, product marketing and sales at Symantec and IBM; and Strategic Sourcing Improvement Consulting at Honeywell.

Cathy holds MS and BS degrees in Electrical and Computer Engineering from Carnegie Mellon University, MBA and MS degrees as a Leaders for Global Operations (LGO) duel-degree fellow from MIT, and two patents for her early work in microprocessor logic design.

Presentations

Executive Briefing: Rigorous application of domain insights in AI projects Session

Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.

Loretta H. Cheeks is the CEO of Strong TIES, an AI expert, and data science consultant. At the nonprofit Strong TIES, she’s helped organizations gain dynamic data insights serving enterprises, governments, and nonprofits. Loretta is on a mission to create a better world with technology. She is a STEAM advocate, developing, deploying, and leading various teams within the communications, avionics, instrumentation and control, and chemical industries for Fortune 500 corporations. She was the first to identify a computational approach for the discovery of news frames in unstructured text (e.g., online news articles). She’s demonstrated a unique ability to integrate communication theory and computer science methods to inform the fields of machine learning, psychology, and mass communication. Loretta is also committed to improving higher education for underserved and underrepresented communities to follow in her scientific footsteps. She’s listed among “10 Incredible Black Women In STEM,” featured by Verizon on the International Day of Women and Girls in Science, recognized as a Change Maker at the White House, and is a member of the NASA Datanauts. She regularly appears among thought leaders in conferences, peer-review publications, workshops, and speaking engagements in the world. Loretta earned a bachelor’s and master’s of science degree in computer science from Southern University, a master’s in technology management from the University of Phoenix, and a PhD in computer science from Arizona State University. She was born in Baton Rouge, Louisiana.

Presentations

Artificial intelligence social influence model and migration paths: Implications to institutions, governments, and businesses Session

Loretta Cheeks provides the language and framework to talk to experts and executives. You'll gain insights into ways to use machine intelligence for shedding light on complex dynamic real-world issues and understanding the embedded biases that exist in news articles (unstructured text).

Roger Chen is cofounder and CEO of Computable and program chair for the O’Reilly Artificial Intelligence Conference. Previously, he was a principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology. Before startups and venture capital, he was an engineer at Oracle, EMC, and Vicor. He also developed novel nanoscale and quantum optics technology as a PhD researcher at UC Berkeley. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Presentations

Thursday Opening Welcome Keynote

Program chairs Ben Lorica, Roger Chen, and Julie Choi open the second day of keynotes.

Wednesday Opening Welcome Keynote

Program chairs Ben Lorica, Julie Choi, and Roger Chen open the first day of keynotes.

Chiranjeet Chetia is a lead data scientist at Visa. With the scale of data Visa observes every day, he’s immersed in finding meaning and value from this data. To this end, he often collaborates with stakeholders across business and technology at Visa to conduct proof of concepts with the end goal of creating data- and AI-powered products or services for Visa. He has 10+ years of experience in the payments domain, most of it in the realm of ecommerce. Previously, he had various stints from managing SMB merchant risk to managing global collections strategy at PayPal. He holds an MS degree in statistics from Virginia Tech, where he also was a provost bioinformatics fellow.

Presentations

Applying AI to secure the payments ecosystem Session

Artificial intelligence has revolutionized the way we live, work, and play. With the help of AI, electronic payments have become more secure and more convenient for consumers globally—regardless of currency or form factor. Chiranjeet and Shubham explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

Adam Cheyer is a vice president of R&D at Samsung. Previously, he was cofounder and vice president of engineering at artificial intelligence company Viv (acquired by Samsung in 2016); was cofounder and vice president of engineering at Siri (acquired by Apple in 2010); cofounded Sentient Technologies, which applies distributed machine learning algorithms to discover novel solutions to complex problems; served as vice president of engineering at Verticalnet (enterprise software) and Dejima (mobile software); and was chief architect of CALO, the largest AI project in US history, at SRI International. Adam is a founding member and first developer at Change.org, the world’s largest petition platform, with a quarter billion members. He has authored more than 60 publications and 25 patents. Adam graduated with highest honors from Brandeis University and received the Outstanding Masters Student award from UCLA’s School of Engineering.

Presentations

Can an AI assistant be as important as the web or as mobile? Session

Adam Cheyer explores what might take for an assistant to make the leap to a global paradigm and then illustrates the unique architecture and approach being taken by Samsung’s Bixby assistant with the goal of doing just that.

Dr. Jike Chong is an accomplished executive and professor with experience across industry and academia.

Jike currently heads Data Science, Hiring Marketplace at LinkedIn. He was most recently the chief data scientist at Acorns, the leading micro-investment app in US with over four million verified investors, which uses behavioral economics to help the up-and-coming save and invest for a better financial future. Previously, Jike was the chief data scientist at Yirendai, an online P2P lending platform with more than $7B loans originated and the first of its kind from China to go public on NYSE; established and headed the data science division at Simply Hired, a leading job search engine in Silicon Valley; advised the Obama administration on using AI to reducing unemployment; and led quantitative risk analytics at Silver Lake Kraftwerk, where he was responsible for applying big data techniques to risk analysis of venture investment.

Jike is also an adjunct professor and PhD advisor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where he established the CUDA Research Center and CUDATeaching Center, which focus on the application of GPUs for machine learning. Recently, he also developed and taught a new graduate level course on machine learning for Internet finance at Tsinghua University in Beijing, China, where he is serving as an adjunct professor.

Jike holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University and a PhD from the University of California, Berkeley. He holds 11 patents (six granted, five pending).

Presentations

Executive Briefing: Rigorous application of domain insights in AI projects Session

Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.

Michael Chui is a San Francisco-based partner in the McKinsey Global Institute, where he directs research on the impact of disruptive technologies, such as big data, social media, and the internet of things, on business and the economy. Previously, as a McKinsey consultant, Michael served clients in the high-tech, media, and telecom industries on multiple topics. He was the first chief information officer of the City of Bloomington, Indiana, and was the founder and executive director of HoosierNet, a regional internet service provider. Michael is a frequent speaker at major global conferences and his research has been cited in leading publications around the world. He holds a BS in symbolic systems from Stanford University, a PhD in computer science and cognitive science, and an MS in computer science, both from Indiana University.

Presentations

Executive Briefing: AI for social good Session

AI has the potential to create substantial value for business and the global economy. It's less well understood how it can address some of the world’s biggest societal challenges. Michael Chui and James Manyika examine the ethical implications of AI and how you can leverage the technology for good while considering the wide-reaching repercussions on business and human society alike.

Nicholas Cifuentes-Goodbody is a data scientist in residence at the Data Incubator. He’s taught English in France, Spanish in Qatar, and now data science all over the world. Previously, he was at Williams College, Hamad bin Khalifa University (Qatar), and the University of Southern California. He earned his PhD at Yale University. He lives in Los Angeles with his amazing wife and their adorable pit bull.

Presentations

AI for managers 2-Day Training

Nicholas Cifuentes-Goodbody leads you through a nontechnical overview of AI and data science. You’ll learn how to apply common techniques in organization and common pitfalls. You’ll pick up the language and develop a framework to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Presentations

Herding cats: Product management in the machine learning era Tutorial

While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle.

Sequence to sequence modeling for time series forecasting Session

Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting.

Robert Crowe is a data scientist and TensorFlow addict at Google. Robert has a passion for helping developers quickly learn what they need to be productive. He’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Previously, Robert led software engineering teams for large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time, Robert sails, surfs occasionally, and raises a family.

Presentations

TFX: Production ML pipelines with TensorFlow Session

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.

Jason Dai is a senior principal engineer and chief architect for big data technologies at Intel, where he leads the development of advanced big data analytics, including distributed machine learning and deep learning. Jason is an internationally recognized expert on big data, the cloud, and distributed machine learning; he’s the cochair of the Strata Data Conference in Beijing, a committer and PMC member of the Apache Spark project, and the creator of BigDL, a distributed deep learning framework on Apache Spark.

Presentations

Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark Tutorial

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA.

Kushal Datta is a senior research scientist in the Artificial Intelligence Products Group at Intel. He specializes in accelerating deep learning training and inference on Intel architecture. His noteworthy achievements are the optimization of multiscale CNN training time on Multi-Node Xeon and INT8/VNNI quantization of the transformer model. Previously, he contributed to Intel Graph Analytics Toolkit, Genomics Analytics Toolkit-4.0 (from Broad Institute), and TileDB (a multidimensional array store). He earned his PhD in ECE from the University of North Carolina at Charlotte where he created a cycle-accurate microarchitecture simulator called Casper, which was awarded the best contribution to the OpenSPARC Community Project. His doctoral dissertation used statistical machine learning and Casper to improve power efficiency of simultaneous multi-threading SPARCV9 many core microarchitectures.

Presentations

Getting the AI you want on the infrastructure you know: 2 deep-dive case studies of AI on CPU Session

Kushal Datta specializes in optimizing AI applications on CPUs; hear two of his latest customer success stories and get the details behind the technical collaboration that led to incredible performance for AI on CPU.

Leslie De Jesus is the chief innovation officer at Wovenware. With more than 20 years of expertise in software, product development, and data science, Leslie drives disruptive strategies and solutions, including AI and enterprise cloud solutions, to clients in a variety of markets from healthcare and telco to insurance, education, and defense industries. Leslie is responsible for designing advanced deep learning, machine learning and chatbot solutions, including patented groundbreaking products. One of her biggest strengths is team building, which is the foundation of repetition in the product creation process. Previously, Leslie has held positions such as senior software product architect; CTO; and vice president, product development for key firms.

Presentations

Environmental AI: Using machine learning to address mosquito-borne diseases Session

Leslie De Jesus examines a machine learning solution enabling the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika and dengue fever. She outlines the challenges, strategy, and technologies used, the results achieved to date, and the implications of the AI project in helping to address a global threat.

Madhura Dudhgaonkar is a machine learning leader at Workday, where she’s passionate about modernizing the future of work. She’s part of the Workday ML organization, a pioneer in the enterprise machine learning space, and has spent 5+ years building ML products leveraging vision, natural language processing, recommendations, anomaly detection, and more. Previously, Madhura’s work ranged from a hands-on engineer to leading large organizations across Sun Microsystems, Adobe, and Workday. Her background covers building consumer and enterprise products—the latest involving multiple 0 to 1 product journeys leveraging machine learning. She’s considered a thought leader in building ML products and is frequently invited to speak at AI conferences. Madhura holds a master’s degree in math and computer science. When not obsessing over technology, she can be found outdoors, running, hiking, or snowboarding.

Presentations

A framework to bootstrap and scale a machine learning function Session

Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset.

Jana Eggers is CEO of Nara Logics, a neuroscience-inspired artificial intelligence company providing a platform for recommendations and decision support. A math and computer nerd who took the business path, Jana has had a career that’s taken her from a three-person business to fifty-thousand-plus-person enterprises. She opened the European logistics software offices as part of American Airlines, dove into the internet in ’96 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory. Her passions are working with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.

Presentations

Executive Briefing: Similar but different—Delivering software with AI Session

Though Nara Logics doesn't always follow them, it has developed great best practices for designing, developing, and delivering great software. Jana Eggers is here to explore what happens when you start adding AI to great software by covering six key features of software development that are similar when adding AI, six that are different, and how to adjust.

Miro Enev is a senior solutions architect at NVIDIA, specializing in advancing data science and machine intelligence while respecting human values. He supports the Pacific Northwest teams engaged with cloud, industrial, and retail clients while participating in research in deep reinforcement learning and edge-to-cloud AI. Miro holds a PhD from the University of Washington’s computer science and engineering department, where his thesis was on machine learning applications for information privacy in emerging sensor contexts. He studied cognitive science and computer science as an undergraduate at the University of California, Berkeley.

Presentations

Put deep learning to work: A practical introduction using Amazon Web Services 2-Day Training

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye, Miro Enev, and Mahendra Bairag detail a practical next step in DL learning with instructions, demos, and hands-on labs.

Carlos Escapa is the global AI and ML practice leader of the Consulting Partner Network at Amazon Web Services. Previously, he was the cofounder and CEO of VirtualSharp Software, where he led the company to a successful exit to Unitrends (Insight Venture Partners); the general manager of Southern Europe at VMware; vice president of channels at CA Technologies in Europe; and business development director at Sterling Software Japan. Carlos holds an MS in computer science from Virginia Tech and a BS from Illinois State University.

Presentations

Framing business problems as machine learning (ML) problems (sponsored by Amazon Web Services) Session

Carlos Escapa takes a deep dive into how to identify use cases for ML, acquire cutting-edge best practices to frame problems in a way that key stakeholders and senior management can understand and support, and set the stage for delivering successful ML-based solutions for your business.

How to leverage powerful Intel-based instance types to create new solutions AI in the Enterprise: The Intel® AI Builders Showcase Event

Carlos Escapa explores how partners leverage powerful Intel-based instance types to create new solutions and the inherent flexibility available through the Intel-AWS partnership to deploy solutions in the cloud using C5 instant types or at the edge with Intel Atom processors.

Andrew Feldman is founder and CEO of Cerebras Systems, a startup dedicated to accelerating Artificial intelligence (AI) compute. Cerebras is a team of pioneering computer architects, computer scientists, deep learning researchers, and engineers of all types who have come together to build a new class of computer-optimized for AI work. Prior to Cerebras, Andrew was a founder and CEO of SeaMicro. SeaMicro, acquired by AMD for $355 million, was the pioneer in energy efficient computation. SeaMicro invented the microserver category and changed the trajectory of the server industry by creating a new class of the high density, energy-efficient servers. Prior to co-founding SeaMicro, Andrew was Vice President of Marketing and Product Management at Force10 Networks (acquired by Dell for $800 Million) and before that was Vice President of Corporate Marketing and Corporate Development for Riverstone Networks (NASDAQ: RSTN) from inception through IPO.

Andrew is passionate about building teams that solve industry-transforming problems. He is a sought after advisor to startups, and currently serves on the board of directors at Natron Energy and on the advisory board of more than a dozen startups. Andrew is a frequent keynote speaker and guest lecturer at the Stanford Graduate School of Business. Andrew holds a bachelor’s degree and an MBA from Stanford University.

Presentations

Enabling AI’s potential through wafer-scale integration Keynote

The first announced element of the Cerebras solution is the Wafer Scale Engine (WSE). The WSE is the largest chip ever built. It contains 1.2 trillion transistors and covers more than 46,225 square millimeters of silicon. In this talk, we will share some of the details of WSE and discuss its impact on the industry.

Sanji Fernando is a senior vice president at Optum. He leads the artificial intelligence (AI) and analytics platforms team for Optum Enterprise Analytics (OEA), supporting the design and development of leading-edge AI models and analytic tools for the enterprise. Previously, Sanji was a vice president at OptumLabs and led the OptumLabs Center for Applied Data Science (CADS), which applied breakthroughs in AI and machine learning to solve complex healthcare challenges for UnitedHealth Group (UHG) by developing and deploying software product concepts; was the head of data science for the Cloud Computing Group and HERE, the navigation services division, and worked in a variety of other roles with Nokia; was cofounder and vice president of engineering at Vettro, a venture-backed mobile software company; and was a consultant with Viant and Accenture. Sanji is a graduate of Trinity College with a bachelor’s degree in computer science.

Presentations

Improving revenue cycle management with deep learning: A healthcare case study Session

Sanji Fernando explores his experience building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including business alignment, data preparation, model development, model selection, deployment, and operations. Sanji also details key knowledge and opportunities for improvement with deep learning models in healthcare.

Dave Ferrell is the managing director of Dynam.AI, a company focused on helping organizations of any size, stage, or industry maximize the potential of artificial intelligence, where he leads a team bringing groundbreaking AI-driven solutions to enterprises around the world. A 25-year industry veteran, Dave has founded and operated companies in web hosting, web design, web app development, back-office systems, and systems integration. In addition to sourcing and managing engineering talent from around the world, Ferrell has led the development of over 600 projects for some of the technology industry’s most innovative companies.

Presentations

Computer vision and deep OCR in the enterprise: 3 use cases Session

Dave Ferrell explores three examples of nontraditional techniques pushing the boundaries of computer vision in industries today, including identifying "unseen" objects.

Julien Forgeat is a senior specialist in artificial intelligence (AI) infrastructure at Ericsson. He joined Ericsson after spending several years working on network analysis and optimization. At Ericsson, Julien has worked on mobile learning, internet of things, and big data analytics, and now specializes in AI infrastructure. His research focuses on the software components required to run AI and machine learning workloads at scale. He holds an MEng in computer science from the National Institute of Applied Sciences in Lyon, France.

Presentations

AI for cell shaping in mobile networks Session

Cell shaping is used to configure radio antenna parameters to improve the service quality. Julien Forgeat explores a reinforcement learning (RL) approach to configuring radio antenna parameters using industry-leading radio simulators from Ericsson and UC Berkeley RISELab's Ray distributed compute framework together with its built-in RL algorithm in RLlib.

Vijay Gabale is the CTO at Infilect. Previously, he worked at IBM Research Labs, where he worked on research and development of machine learning and deep learning in retail, telecom, and education. He has over 20 A* publications and over 5 patents to his name. He’s a frequent speaker at AI conferences (e.g., GTC 2018, San Jose; ACM KDD, 2018, London), and has won numerous awards for his contribution to advancements in AI. He earned his PhD from the Indian Institute of Technology Bombay.

Presentations

Data distribution search: Deep reinforcement learning to improvise input datasets Session

Beyond computer games and neural architecture search, practical applications of deep reinforcement learning (DRL) to improve classical classification or detection tasks are few and far between. Vijay Gabale outlines a technique and some experiences of applying DRL on improving the distribution input datasets to achieve state-of-the-art performance, specifically on object-detection tasks.

Anjali Gajendragadkar is product manager for ignio at Digitate and is instrumental in managing various technology partners and client engagements. With more than 23 years of experience across academics, R&D, and delivery, she has incubated new products and engineered and deployed ignio at various customer sites to deliver value. Previously, Anjali spent six years as a scientist for TCS Innovation Labs. She has authored several papers and is a patent holder. She earned a master’s in physics from the University of Mumbai.

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Running enterprise IT more efficiently, improving customer experience, and increasing the agility and stability of IT AI in the Enterprise: The Intel® AI Builders Showcase Event

Join Anjali Gajendragadkar to hear how award-winning ignio combines artificial intelligence and machine learning with Intel AI hardware and software to help organizations run their enterprise IT more efficiently, improve customer experience, and increase the agility and stability of IT.

Anu Gali is an engineering leader at Uber, leading business insights. Her team is responsible for rides and eats trip forecasts, helps business optimize the budget across operations and business units, and predicts customer value across all users. As a tech leader, Anu has built high-performing engineering teams from the ground up and steered large-scale projects in data, ML, web, ecommerce, and mobile technologies for companies such as Uber, Groupon, Shutterfly, and Adobe. Anu strongly believes in having an entrepreneurial mind-set and in helping others to reach their potential. She cofounded a company and released a social entertainment app, IntoMovies. She also volunteers for a number of charitable organizations and STEM programs.

Presentations

Supercharging business decisions with AI: Insight, optimize, and personalize to save $100M Session

There are 15 million trips a day on the Uber platform. Anu Gali walks you through how Uber leverages AI to automate its business model via its unique platform. You'll learn about technology that evolves based on current market insights and dynamically adjusts for the future. She shares best practices and the architecture that enables organizations like Uber to grow and scale rapidly.

Triveni Gandhi is a data scientist at Dataiku, where she works with clients to deploy custom AI solutions and find meaning from complex data science pipelines. Her current work focuses on making responsible AI a part of the discourse and practices for enterprise data science. Triveni is also a cohost of the Banana Data Podcast, which highlights new developments and challenges in the world of AI. Previously, she served as a data analyst for a large education nonprofit in New York City, where she developed data pipelines and analyses to support the work of educators across the city. Triveni holds a PhD in political science from Cornell University.

Presentations

The moral responsibility of AI builders (sponsored by Dataiku) Keynote

With the adoption of AI in the enterprise accelerating, its impacts—both positive and negative—are rapidly increasing. Triveni Gandhi explores why the builders of these new AI capabilities all bear some moral responsibility for ensuring that their products create maximum benefit and minimal harm.

Siddha Ganju is a self-driving solutions architect at NVIDIA and was featured by Forbes on their 30 under 30 list. Previously, she developed deep learning models for resource constraint edge devices at Deep Vision. A graduate from Carnegie Mellon University, her prior work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data, and she’s been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she’s also been guiding teams at NASA as well as featured as a jury member in several international tech competitions.

Presentations

Deep learning on mobile Session

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices.

Eric Gardner is the director of sales enabling for the Artificial Intelligence Products Group (AIPG) within the Data Center Group (DCG) at Intel. Since joining Intel through the Accelerated Leadership Program (ALP), Eric has held product marketing, outbound marketing, and strategic planning roles spanning several business units. Previously, he worked for almost five years in silicon development and management at IBM. Eric holds an MBA from the University of Chicago’s Booth School of Business and a BSE from Duke University in electrical & computer engineering. He’s passionate about family, technology, sports, and the great outdoors.

Presentations

Getting from A to AI Keynote

Businesses recognize the transformational potential for advanced analytics, machine, and deep learning but often get lost on their path to AI. Eric Gardner spends his days advising customers about AI and shares a four-step journey that organizations of every kind can use to evaluate their unique path from data to insight.

Marina (Mars) Rose Geldard is a researcher from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she’s found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she’s not busy being the most annoyingly eager researcher ever, she compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS). She’s currently writing Practical Artificial Intelligence with Swift for O’Reilly Media.

Presentations

Build a self-driving car without a car: ML problem-solving with a game engine Tutorial

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine.

Live coding a self-driving car (without a car) Session

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines.

Sahika Genc is a senior applied scientist at Amazon artificial intelligence (AI). Her research interests are in smart automation, robotics, predictive control and optimization, and reinforcement learning (RL), and she serves in the industrial committee for the International Federation of Automatic Control. She leads science teams in scalable autonomous driving and automation systems, including consumer products such as AWS DeepRacer and SageMaker RL. Previously, she was a senior research scientist in the Artificial Intelligence and Learning Laboratory at the General Electric (GE) Global Research Center, where she led science teams on healthcare analytics and collaborated with government organizations and research institutions to develop energy analytics for consumers and utilities, served in the organizing committees for the American Control Conference, and was an associate editor for IEEE Transactions on Automation Science and Engineering. She has more than 30 patents and 50 conference, journal, and technical report publications. She earned her MS and PhD degrees in electrical engineering systems from the University of Michigan-Ann Arbor.

Presentations

Practical insights into deep reinforcement learning Keynote

Sahika Genc dives deep into the current state-of-the-art techniques in deep reinforcement learning (DRL) for a variety of use cases. Reinforcement learning (RL) is an advanced machine learning (ML) technique that makes short-term decisions while optimizing for a longer-term goal through trial and error.

Mazin Gilbert is the vice president of advanced technology and systems at AT&T Labs. He leads AT&T’s research and advanced development of its network and access transformations, where he oversees advancements in artificial intelligence, software-defined networking and access, digital transformation, cloud technologies, open source software platforms, and big data. He holds 176 US patents in communication and multimedia processing and has published over 100 technical papers in human-machine communication. He’s the author of the book Artificial Neural Networks for Speech Analysis/Synthesis and an editor of a recent book Artificial Intelligence for Autonomous Networks. Previously, Mazin worked for Bell Labs, BBC, and British Telecom and in academia at Rutgers University, Princeton University, and Liverpool University. He became an IEEE Fellow in 2012. Mazin holds a bachelor’s and a doctoral degree, with first-class honors, in electrical engineering from the University of Liverpool and an MBA for executives from the Wharton Business School of the University of Pennsylvania. Outside of his technology career, Mazin is an entrepreneur, owning five limited liability companies specializing in commercial and residential real estate and the dental industry. He also serves as a chair of the Linux Foundation Deep Learning Foundation board and a board member at the International Computer Science Institute (Berkeley). Mazin loves to spend time with his daughters and is an avid runner.

Presentations

Executive Briefing: 5G—A playground for AI Session

5G promises to change our lives in a big way. Mazin Gilbert provides a technical- and market-landscape overview of how AI creates the 5G world, highlighting how recent developments in AI help accelerate widespread adoption of 5G-based applications for consumers and enterprises. He explores the roles of open source and open platforms as key ingredients of this 5G AI transformation.

Navdeep Gill is a software engineer and data scientist at H2O.ai, where he focuses on model interpretability, GPU-accelerated machine learning, and automated machine learning. Previously, he worked at Cisco, focusing on data science and software development, and at institutions such as California State University, East Bay; University of California, San Francisco; and Smith Kettlewell Eye Research Institute in neuroscience labs as a researcher and analyst. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research. He earned an MS in computational statistics, a BS in statistics, and a BA in psychology with a minor in mathematics from California State University, East Bay. His interests are machine learning, time series analysis, statistical computing, data mining, and data visualization. You can find Navdeep on Twitter as @Navdeep_Gill_.

Presentations

Human-centered machine learning (sponsored by H2O.ai) Session

Navdeep Gill takes a deep dive into how to combine innovations from several subdisciplines of machine learning research to train understandable, fair, trustable, and accurate predictive modeling systems.

Dylan Glas is a roboticist and researcher with over a decade of experience in the field of social human-robot interaction. He’s a senior robotics software architect at Futurewei Technologies. Previously, he was a guest associate professor at Osaka University and a group leader and senior researcher at the Advanced Telecommunications Research Institute (ATR) in Kyoto, Japan, where he developed frameworks and algorithms for multimodal perception, machine learning, and autonomous behavior generation for a variety of humanoid social robots. He’s been featured on international media, including CBS, BBC, CNN, National Geographic, and the Guardian, for his work as the lead architect of ERICA, a highly humanlike conversational android that is currently operating as a TV news anchor in Japan.

Presentations

Creating autonomy for social robots Session

Robot technologies are becoming more capable and affordable. Yet even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it's often difficult to quantify human social behavior with algorithms. Dylan Glas and Phoebe Liu highlight some of the ongoing research to enable human-robot interaction.

Enhao Gong is the founder and CEO at Subtle Medical, an AI and radiology startup from Stanford and the winner of 2018 NVIDIA Inception Award at AI+Healthcare. He’s a serial entrepreneur and PhD in electrical engineering at Stanford, with a research focus on applying AI and deep learning to improve reconstruction, analysis, and quantification in medical imaging. His work applies AI to accelerate and reduce doses for MRI and PET and has been featured in numbers of academic journals and clinical conferences. Enhao has won several awards, including being recognized by Forbes China as one of 2018’s “30 under 30.”

Presentations

AI and deep learning enable 4x faster scans and productivity gains for clinical radiology Session

Enhao Gong and Greg Zaharchuk detail AI solutions, cleared by the FDA and powered by industry framework, that deliver 4x–10x faster MRI scans, 4x faster PET scans, and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag Hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow.

Pankaj Goyal is the global vice president of AI and leads the AI and accelerated computing business at Hewlett Packard Enterprise. He’s a computer science engineer by education, and he truly believes in the potential of AI to positively change our lives and lives of our next generation. The fact that his 2-year-old son can only talk to four people—his mom, dad, his sister, and Google Home—reconfirms his belief. Pankaj is committed to making HPE a leader in this space. He’s a technology leader with experience ranging from transforming Fortune 50 companies to creating new businesses. He’s an expert in growth strategy, business operations, product management, portfolio management, building teams, and scaling startups. Pankaj has worked in enterprise technology (hardware, cloud and XaaS, and mobility) and consumer technology (consumer internet, platform businesses, IoT, self-driving cars, and AI). Previously, he worked in the high-tech practice at McKinsey & Company and cofounded a mobility startup. He earned his bachelor’s degree in computer science from the Indian Institute of Technology (IIT) and an MBA from the Indian Institute of Management.

Presentations

Unlock your data's value with AI (sponsored by HPE) Session

Join Pankaj Goyal and Nanda Vijaydev to learn how HPE put AI into action and helps enterprises unlock the value of their data with a proven, practical approach to AI.

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence and the author of the beloved O’Reilly book Data Science from Scratch and the blog post “Fizz Buzz in TensorFlow.” Previously, he was a software engineer at Google and a data scientist at a variety of startups. He lives in Seattle.

Presentations

Putting cutting-edge modern NLP into practice Tutorial

AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like; you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems.

Dexter Hadley as an assistant professor of pedatrics, pathology, and laboratory medicine at the University of California, San Francisco (UCSF). His expertise is in translating big data into precision medicine and digital health. His background is in genomics and computational biology, and he has training in clinical pathology. His research generates, annotates, and ultimately reasons over large multimodal data stores to identify novel biomarkers and potential therapeutics for disease. His early work resulted in a successful precision medicine clinical trial for ADHD (Clinicaltrials.gov identifier NCT02286817) for a first-in-class, nonstimulant neuromodulator to be targeted across the neuropsychiatric disease spectrum. More recently, his laboratory was funded by the NIH Big Data to Knowledge Initiative to develop the Stargeo.org online portal to crowdsource annotations of open genomics big data that allows users to discover the functional genes and biological pathways that are defective in disease. He also develops state-of-the-art data-driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage disease. Toward this end, he has been recognized by UCSF with various awards including the inaugural UCSF Marcus Award for Precision Medicine to develop a digital learning health system to use smartphones to screen for skin cancer as well as a pilot award in precision imaging to better screen mammograms for invasive breast cancer. In general, the end point of his work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the spectrum of disease.

Presentations

From bits to bedside: Translating routine clinical data into precision mammography Session

Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translate this into precision oncology.

Kristian Hammond is a chief scientist at Narrative and a professor of computer science and journalism at Northwestern University. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

Presentations

Bringing AI into the enterprise Tutorial

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

Sijun He is a machine learning engineer at Twitter Cortex, where he works on content understanding with deep learning and NLP. Previously, he was a data scientist at Autodesk. Sijun holds an MS in statistics from Stanford University.

Presentations

Named entity recognition at scale with deep learning Session

Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build a deep understanding of its noisy and temporal text content. Sijun He and Ali Mollahosseini explore the named entity recognition (NER) system at Twitter and the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day.

Lindsay Hiebert is a product line manager for AI at Intel, working with the Intel Neural Compute Stick 2. He’s responsible for product line management and strategic business development with Intel AI, Vision, and technology partners to deliver industry-leading AI, IoT market-ready solutions, and commercial offerings for AI: In Production, market-ready solutions across markets using Intel end-to-end AI, Vision, and IoT products and technologies.

Presentations

Delivering AI vision ecosystem offers with Intel AI: In Production Session

Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings.

Bastiane Huang is a product manager at OSARO, a San Francisco-based machine learning company building deep reinforcement learning software for industrial robots, backed by Peter Thiel and Jerry Yang’s AME Cloud Ventures, where she leads product strategy. Bastiane has close to a decade of experience in the automation and manufacturing industries. She has broad experience in product marketing, business development, and operations at international technology companies across the industrial automation, IoT, AI, and robotics industries. Previously, she worked at e2v; she cofounded a software business at Advantech that offered video analytics solutions to improve traffic congestion and shopping experiences through people counting and facial and heat map analysis; she was an investor and advisor to early stage IoT and AI startups in the US and greater China; and she worked as a senior product manager at Amazon Alexa. She’s actively involved with Harvard’s Managing the Future of Work initiative on AI and robotics, writing case studies and articles. Bastiane holds a BS in information management from the National Taiwan University and an MBA in technology and entrepreneurship from Harvard Business School.

Presentations

Robot 2.0: Deep reinforcement learning for industrial robotics Session

Machine learning has enabled the move from manually programming robots to allowing machines to learn from and adapt to changes in the environment. Bastiane Huang examines how AI-enabled robots are used in warehouse automation, including recent progress in deep reinforcement learning, imitation learning, and real-world requirements for various industrial problems.

Tingwei Huang is a lead architect on Intel’s newest AI chips that are tackling this shifting data landscape.

Presentations

Trends to watch: How shifts in data structure and volume demand new approaches to AI compute Session

Taking full advantage of data means using more of it, leading to larger, increasingly complex models with billions of hyperparameters requiring massive clusters of compute nodes, all while meeting ever-stricter latency and power requirements. Tingwei Huang explains how the way we compute AI has to be completely rethought so it can evolve to meet the promise of global business transformation.

Louis Huard is the senior product manager at Zepl. He fell in love with product management designing an educational Lego robotics program for middle schoolers during his college days. Previously, Louis was a product manager for Cisco AppDynamics BiQ platform and the machine learning startup they acquired, Perspica.

Presentations

The holy grail of data science: Rapid model development and deployment (sponsored by Zepl) Session

A key step in the data science workflow is rapid model development; however, gaps still exist. Teams are moving from siloed to sharing and reusing models, code, and results. There are also in challenges deploying models into production using tools like Kubeflow and TensorFlow. Moon Soo Lee and Louis Huard examine how leading companies solve these issues, and how you can improve your workflow.

Phil Hummel is a senior principal engineer in the ready solutions technical marketing team focused on machine learning and AI at Dell EMC. Previously, he was in the energy sector researching market adoption of energy-efficient technologies in residential, commercial, and industrial sectors while working in government, private, and nonprofit consulting and R&D roles; he was in presales and consulting for SQL server with a specialization in analysis services and data mining with Microsoft; and he started as a presales specialist for data platforms, big data, and analytics with Dell EMC.

Presentations

Dell Ready Solutions AI in the Enterprise: The Intel® AI Builders Showcase Event

Phlip Hummel explores Dell EMC's AI-ready solutions, which combine Dell infrastructure leadership in compute, storage, and networking with software tools from Intel and the industry to provide an ease-of-use deep learning experience. As model development moves strongly toward Kubernetes container environments, Dell Ready Solution offers an integrated, turnkey solution.

Rohit Israni is the director of strategic business development for AI programs in the Developer Relations Division at Intel, where he leads a global team responsible for bringing Intel’s AI initiatives via the Intel AI Academy and Intel AI Builders to universities and independent software vendors (ISVs) worldwide. He’s also the vice chair of US TAG of the International Committee for Information Technology Standards (INCITS) steering committee on AI (JTC 1/SC 42). Rohit has 20 years of multifaceted experience in engineering, new product innovation, strategy, finance, and business development in both large and small companies. He’s strategized and executed a wide variety of transactions including mergers, acquisitions, divestitures, LBOs, fundraising (both debt and equity), investments, partnerships, and alliances. Previously, he developed industry-leading pattern recognition algorithms at imaging technology (M&A Teledyne DALSA) and in the Advanced Technology Group at KLA-Tencor. He holds two patents in machine learning and image processing. Rohit earned his master’s degree in engineering with a specialization in robotics and AI from Tulane University and his master’s degree in management science and engineering from Stanford University.

Presentations

Artificial intelligence in action: Horizontal enterprise solutions—Conversational AI Session

Across segments, enterprises are exploring novel ways of providing stellar customer service. Conversational AI is delivering just that—high-quality customer service, available 24-7, and in a geography-agnostic manner. Juby Jose and Rohit Israni explore how enterprise customer service is being reimagined with the power of conversational AI.

Ram Janakiraman is a senior distinguished engineer at the Aruba CTO Office working on machine intelligence for enterprise security. Ram’s recent focus has been on simplifying building behavior models by leveraging approaches in NLP and representation learning. He hopes to improve end user product engagement through a visual representation of entity interactions. Ram has numerous patents in a variety of areas during the course of his career. Previously, he’s been in various startups and was a cofounding member of Niara, Inc., working on security analytics with a focus on threat detection and investigation before it was acquired by Aruba, a HPE Company. Ram is an avid scuba diver and always eager to explore the next reef or kelp. He’s also an FAA certified drone pilot capturing the beauty of dive destinations on his trips.

Presentations

Can behavioral analytics for enterprise security benefit from approaches in NLP? Session

While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.

Presentations

On gradient-based methods for finding game-theoretic equilibria Keynote

Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program.

Juby Jose is senior engineering manager at Intel and has 16 years of industry experience spanning embedded software engineering and product management. She’s leading AI applications in the domain of speech and language processing. Her expertise areas include mobile platforms, IoT applications, and machine learning and deep learning applications. Juby completed her MBA from the Indian School of Business, Hyderabad.

Presentations

Artificial intelligence in action: Horizontal enterprise solutions—Conversational AI Session

Across segments, enterprises are exploring novel ways of providing stellar customer service. Conversational AI is delivering just that—high-quality customer service, available 24-7, and in a geography-agnostic manner. Juby Jose and Rohit Israni explore how enterprise customer service is being reimagined with the power of conversational AI.

Bharath Kadaba is the senior vice president and chief innovation officer at Intuit, and leads the Technology Futures Group. His organization is responsible for creating game-changing technology in support of Intuit’s mission to power prosperity for consumer, small business and self-employed customers. Bharath has served in a variety of executive leadership positions at Intuit; he’s been vice president and engineering fellow with responsibility for leading engineering teams that built innovative new technology for the company’s QuickBooks, TurboTax, and Mint product lines; he led advanced technology development as vice president for global ready offerings, and vice president for the Global Business Division, Product Development, respectively. Previously, Bharath was vice president of media engineering at Yahoo, where he led the development of a shared services platform to serve as the foundation for all media properties (news, finance, sports, games, etc.) and significantly expanded the US media product capabilities; and he was an executive with Siebel Systems, AristaSoft, and News Corp after spending 15 years at IBM and IBM’s TJ Watson Labs. Bharath earned a PhD in computer networks from the University of Hawaiʻi at Mānoa and a BSEE and master’s in computers and control from the Indian Institute of Science.

Presentations

Blending AI disciplines and human experts to build smart assistants of the future (sponsored by Intuit) Session

To unleash the full potential of AI, Intuit envisions a future that melds the best capabilities of machines and humans to deliver personalized customer experiences, all on one secure platform. Bharath Kadaba examines how Intuit combines rules-based knowledge engineering with data-driven machine learning and natural language processing to build the human-expert-in-the-loop AI systems of the future.

Ari Kamlani is a Principal Deep Learning Technologist @Skymind with 20+ years of experience.  He is a recognized Creative Thought Leader, Data Practitioner and Technology Strategist delivering value across a wide range of Industrial Sectors and Clients, such as Financial Services, Retail, Healthcare, Consumer & Industrial IoT, Broadcast Media, and many others.

He is experienced in enabling R&D Incubation Divisions and Large Scale Enterprise Organizations scaling their Product Offerings from Research to Production.  

Prior to Skymind, he worked with ThoughtWorks, a Digital Global Software Consultancy, JP Morgan Chase in their Digital Intelligence Division, Techstars Accelerator within their IoT Division, and many others.  Today he focuses on Machine Intelligence, with specialization in Natural Language, Computer Vision, and Simulation Modeling along with advancing their AI Infrastructure.

Presentations

Accelerating AI from research to production in the enterprise AI in the Enterprise: The Intel® AI Builders Showcase Event

Join Ari Kamlani to learn how, in collaboration with Intel, Skymind supports Intel Xeon Scalable Processors and MKL-DNN to accelerate machine learning workloads from conception to deployment. Supported use cases include natural language processing (e.g., chatbots), computer vision (e.g., object detection), and many others.

Amit Kapoor is a data storyteller at narrativeViz, where he uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Interested in learning and teaching the craft of telling visual stories with data, Amit also teaches storytelling with data for executive courses as a guest faculty member at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. Previously, he gained more than 12 years of management consulting experience with A.T. Kearney in India, Booz & Company in Europe, and startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad.

Presentations

Recommendation system using deep learning 2-Day Training

Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.

Holden Karau is a transgender Canadian software working in the bay area. Previously, she worked at IBM, Alpine, Databricks, Google (twice), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She’s a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.

Presentations

Introducing Kubeflow (with special guests TensorFlow and Apache Spark) Session

Modeling is easy—productizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karau—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.

Meher Kasam is an iOS software engineer at Square and is a seasoned software developer with apps used by tens of millions of users every day. He’s shipped features for a range of apps from Square’s point of sale to the Bing app. Previously, he worked at Microsoft, where he was the mobile development lead for the Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, and the American Council of the Blind, to name a few. A hacker at heart with a flair for fast prototyping, he’s won close to two dozen hackathons and converted them to features shipped in widely used products. He also serves as a judge of international competitions including Global Mobile Awards, Edison Awards.

Presentations

Deep learning on mobile Session

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices.

Arun Kejariwal is an independent lead engineer. Previously, he was he was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install-and-click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Presentations

Sequence to sequence modeling for time series forecasting Session

Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting.

Mayank Kejriwal is a computer scientist at the USC Information Sciences Institute, where he conducts research on the IARPA HFC and DARPA LORELEI, CauseEx, D3M, and MEMEX projects, the latter of which has been covered by 60 Minutes, Forbes, Scientific American, the Wall Street Journal, the BBC, and Wired. He holds a PhD from the University of Texas at Austin. His dissertation, "Populating a Linked Data Entity Name System,” received the Best Dissertation Award by the Semantic Web Science Association in 2017. Mayank is currently coauthoring a textbook on knowledge graphs.

Presentations

Executive Briefing: An age of embeddings Session

Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy.

Induprakas (Indu) Keri is the vice president of development at Intuit, with responsibility for driving the company’s blockchain and distributed ledger technology strategy. Indu has had a long and distinguished career with leading high-technology companies. Previously, he was chief information security officer and vice president of cloud security at Intuit; was chief operating officer of Limelight, the number two content delivery network, where he was responsible for products, R&D, professional services, and customer support, and he built out a robust R&D organization, established people and process improvements that resulted in double-digit net promoter score increases, and presided over the largest capacity increase in the company’s history; was executive vice president products and chief technology officer at Sungard AS, where he drove the overall technology strategy for the company, led R&D, and delivered cloud-based disaster recovery products; was as an engineer at Silicon Graphics, where he implemented his dissertation work into the high-end parallelizing compiler delivered by SGI; and was at McKinsey & Company, BEA Systems, and Oracle.

Presentations

Data science without seeing the data: Advanced encryption to the rescue Session

Tzvika Barenholz and Induprakas Keri detail Intuit’s efforts to deploy fully homomorphic encryption (FHE) in production, which allows models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. You'll take a sneak peak at some of the optimizations and tricks that make FHE practical.

Amine Kerkeni is the head of engineering at InstaDeep, where he leads two research projects applying the latest advancements in natural language processing (NLP). He leads software engineering teams in various industries, including semiconductor, consumer electronics, and investment. Amine’s areas of expertise include computer vision, predictive analytics, NLP, and combinatorial optimizations. He holds a master’s of engineering in computer science and a master’s of business administration.

Presentations

AI-based container usage optimization tool AI in the Enterprise: The Intel® AI Builders Showcase Event

Amine Kerkeni walks you through a system that demonstrates an agent that learns to pack boxes efficiently in containers while respecting multiple physical constraints. The agent is trained using reinforcement learning to minimize the wasted space. Without any human knowledge, the agent achieves superhuman performance and outperforms commercial optimization software.

Ganes Kesari is a cofounder and head of analytics at Gramener, where he leads analytics and innovation in data science, advising enterprises on deriving value from data science initiatives and leading applied research in deep learning at Gramener AI Labs. He’s passionate about the confluence of machine learning, information design, and data-driven business leadership and strives to simplify and demystify data science.

Presentations

Saving Antarctic penguins with deep learning AI in the Enterprise: The Intel® AI Builders Showcase Event

Ganes Kesari explores the background of crowd counting and the pros and cons of various approaches. He presents a real-world application for biodiversity conservation. You'll see how AI helped count penguin populations in Antarctica using time-lapse pictures from camera traps, understand the implementation challenges, and the approach used to address them.

Urs Köster is the head of machine learning at Cerebras Systems, where he develops novel deep learning algorithms to enable the next generation of AI. He has 15 years of experience in neural networks and computational neuroscience, contributed to machine learning frameworks, developed low-precision numerical formats, and led data science engagements. Previously, he was head of algorithms R&D at Intel Nervana and a researcher at UC Berkeley.

Presentations

Scaling AI at Cerebras Session

Long training times are the single biggest factor slowing down innovation in deep learning. Today's common approach of scaling large workloads out over many small processors is inefficient and requires extensive model tuning. Urs Köster explains why with increasing model and dataset sizes, new ideas are needed to reduce training times.

Abhishek Kumar is a senior manager of data science in Publicis Sapient’s India office, where he looks after scaling up the data science practice by applying machine learning and deep learning techniques to domains such as retail, ecommerce, marketing, and operations. Abhishek is an experienced data science professional and technical team lead specializing in building and managing data products from conceptualization to the deployment phase and interested in solving challenging machine learning problems. Previously, he worked in the R&D center for the largest power-generation company in India on various machine learning projects involving predictive modeling, forecasting, optimization, and anomaly detection and led the center’s data science team in the development and deployment of data science-related projects in several thermal and solar power plant sites. Abhishek is a technical writer and blogger as well as a Pluralsight author and has created several data science courses. He’s also a regular speaker at various national and international conferences and universities. Abhishek holds a master’s degree in information and data science from the University of California, Berkeley. Abhishek has spoken at past O’Reilly conferences, Strata 2019, Strata 2018, and AI 2019.

Presentations

Industrialized capsule networks for text analytics Session

Vijay Agneeswaran and Abhishek Kumar explore multilabel text classification problems, where multiple tags or categories have to be associated with a given text or documents. Multilabel text classification occurs in numerous real-world scenarios, for instance, in news categorization and bioinformatics (such as the gene classification problem).

Akhilesh Kumar is a senior machine learning engineer on the applied machine learning team at Adobe, where he’s primarily responsible for putting deep learning models in production. Part of his job is to train, evaluate, and put deep learning models in scalable systems. He’s an avid reader and loves to come up with solutions for a wide variety of problems.

Presentations

Generative models for fixing image defects Session

Photographic defects such as noise, exposure, and blur can ruin the perfect shot. Adobe has developed a solution based on GAN that can identify the defective region in images and fix it. Akhilesh Kumar explores how this solution, which can also be applied to fix videos, is better than traditional algorithms and means you won't have to spend hours manually editing the images.

Margaret Laffan is the vice president of business development at TalentSeer—a specialized AI talent partner dedicated to building and nurturing AI teams for companies at various growth stages—and a venture partner with BoomingStar Ventures—a $1.5B fund focused on AI, robotics, and autonomous driving early stage startups. Margaret leads the development of new partnerships to accelerate the expansion of TalentSeer’s AI talent ecosystem. Previously, Margaret drove sales and business development at SAP and was a client executive at AllyO, an end-to-end AI recruiter with automated and conversational engagement platform. Margaret is published in Forbes and contributes to other media. She earned her master’s degree in political science from the University of Dublin and has 15+ years working in industry, nonprofit, and startup sector.

Presentations

Talent for AI transformation: Building a strong AI team for the future (sponsored by TalentSeer) Session

With the rapid advancement of AI technology and commercial breakthroughs, building a strong AI team becomes increasingly critical for business success in the high-tech era. Margaret Laffan helps tech and talent leaders to better understand the AI talent market and explores best practices for building, nurturing, and retaining the right team to accelerate their business growth.

Danny Lange is the vice president of AI and machine learning at Unity, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business from the app to self-driving cars as the head of machine learning, provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public as the general manager of Amazon Machine Learning, led a product team focused on large-scale machine learning for big data as principal development manager at Microsoft, was CTO of General Magic, Inc., worked on General Motor’s OnStar Virtual Advisor—one of the largest deployments of an intelligent personal assistant until Siri—as the founder of his own company Vocomo Software, and was a computer scientist at IBM Research. He’s a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.

Presentations

Advancing our understanding of deep reinforcement learning with community-driven insights Session

This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Then, they invited the public to try to solve the challenge. Danny Lange reveals what Unity learned from the contest and the real-world impact of observing the behaviors of multiple AI agents in a simulated virtual environment.

Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the technology and operations management unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.

Presentations

Using automated machine learning for hyperparameter optimization and algorithm selection Session

Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and hyperparameter tuning.

Moon Lee is the cofounder and chief technology officer of Zepl, the data science and analytics platform that supports the entire machine learning pipeline. He’s also the creator of Apache Zeppelin, with more than 500,000 downloads worldwide.

Presentations

The holy grail of data science: Rapid model development and deployment (sponsored by Zepl) Session

A key step in the data science workflow is rapid model development; however, gaps still exist. Teams are moving from siloed to sharing and reusing models, code, and results. There are also in challenges deploying models into production using tools like Kubeflow and TensorFlow. Moon Soo Lee and Louis Huard examine how leading companies solve these issues, and how you can improve your workflow.

Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an IEEE Fellow and an ACM Fellow.

Presentations

Machine learning for autonomous driving: Recent advances and future challenges Session

Tremendous progress has been made in applying machine learning to autonomous driving. Li Erran Li explores recent advances in applying machine learning to solving the perception, prediction, planning, and control problems of autonomous driving as well as some key research challenges.

Yunyao Li is a senior research manager with IBM Research – Almaden, where she manages the Scalable Knowledge Intelligence Department. She’s a Master Inventor and a member of the IBM Academy of Technology. She’s also a member of the New Voices program of the American National Academies. Her expertise is in the interdisciplinary areas of natural language processing, databases, human-computer interaction, and information retrieval. Her contributions in these areas have resulted in 50+ research publications at top AI conferences and journals, 20+ patent filings, and recognition by multiple prestigious IBM internal awards. She received her PhD in computer science and engineering and dual master degrees in computer science and engineering and information science from the University of Michigan and undergraduate degrees from Tsinghua University in Beijing, China. Yunyao is also deeply passionate about improving the diversity for the STEM field. She’s been actively mentoring women and underrepresented minorities both in- and outside of IBM. She currently leads the Almaden Women’s Interest Network Group (AWING) at IBM.

Presentations

Toward universal semantic understanding of natural languages Session

Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes.

Tianchu Liang is a principal data scientist with American Tire Distributors. He’s a physicist and mathematician turned computer scientist turned machine learning enthusiast. He develops and deploys machine learning solutions to solve real world business problems, such as using LSTM to forecast staffing needs and using XGBoost models to execute real-time online customer behavior classifications. As one of the first two data scientists in company history to join American Tire Distributors, he helped grow the data science team to a size of 12 within a year and is now developing machine learning solutions to help the company in supply chain, sales, and warehousing, as well as ecommerce.

Presentations

Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs Session

Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.

Jessie Lin is a sales engineer at Cloudera, with twenty years of experience specializing in machine learning and data warehousing. She’s passionate about leveraging data to solve business challenges such as predictive maintenance and fraud detection. Jessie leads a team of solution engineers focusing on machine learning and has helped the largest financial institutions, telecoms, and high-tech manufacturers better engage their customers to achieve higher ROI on data platforms. She holds an MBA from the University of Texas at Austin and a BS in information science from Fudan University.

Presentations

Industrialize AI with Cloudera AI in the Enterprise: The Intel® AI Builders Showcase Event

Organizations face challenges in consistently and repeatedly building and deploying machine learning capabilities to drive efficiencies and cost savings at scale. Jessie Lin examines challenges related to data management, tools, and the machine learning lifecycle in the journey to deploying capabilities repeatedly and successfully at scale.

Phoebe Liu is a machine learning scientist at Figure Eight, an AI and machine learning startup based in San Francisco. Previously, she was a robotics researcher in Japan, working in Hiroshi Ishiguro Laboratory at Advanced Telecommunications Research Institute International (ATR). At the same time, she earned her PhD at Osaka University. She was involved in projects including enabling conversational social robot to imitate human behaviors, android science, and teleoperation system for semiautonomous robots.

Presentations

Creating autonomy for social robots Session

Robot technologies are becoming more capable and affordable. Yet even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it's often difficult to quantify human social behavior with algorithms. Dylan Glas and Phoebe Liu highlight some of the ongoing research to enable human-robot interaction.

Kai Liu is a senior program manager in the AI and Research Group of Microsoft. He has seven years of experience in data-driven engineering, big data platform, and AI infrastructure for Office product families. He led his team to create a service health portal for SharePoint Online, inject a distributed log collection and storage system for Exchange Online, publish curated datasets and key business metrics, and enable subhour experimentations in Office 365. He’s working on the AI and deep learning infrastructure for large-scale enterprise data under compliance obligations.

Presentations

Container orchestrator to DL workload, Bing's approach: FrameworkLauncher Session

Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Presentations

Thursday Opening Welcome Keynote

Program chairs Ben Lorica, Roger Chen, and Julie Choi open the second day of keynotes.

Wednesday Opening Welcome Keynote

Program chairs Ben Lorica, Julie Choi, and Roger Chen open the first day of keynotes.

Hao Lu is a research scientist on Facebook’s AI platform team. She specializes in AI compilers and neural network performance optimization on ARM, x86, and mobile GPU. She holds a bachelor’s degree in microelectronics from Peking University and an MA and PhD in electrical engineering from University of Notre Dame. She’s contributed optimizations and improvements to Caffe2, PyTorch, TVM, and Quantized Neural Networks Package (QNNPACK).

Presentations

Scaling AI experiences at Facebook with PyTorch Session

Joseph Spisak and Hao Lu lead a deep dive into how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook.

Boris Lublinsky is a software architect at Lightbend, where he specializes in big data, stream processing, and services. Boris has over 30 years’ experience in enterprise architecture. Previously, was responsible for setting architectural direction, conducting architecture assessments, and creating and executing architectural road maps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration (EAI). Boris is the coauthor of Applied SOA: Service-Oriented Architecture and Design Strategies, Professional Hadoop Solutions, and Serving Machine Learning Models. He’s also cofounder of and frequent speaker at several Chicago user groups.

Presentations

Hands-on machine learning with Kafka-based streaming pipelines Tutorial

Boris Lublinsky and Chaoran Yu examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more.

Hagay Lupesko is an engineering leader on the AI applied research team at Facebook, where he focuses on Facebook’s AI-powered personalization platform used across Facebook’s family of apps and services. Hagay has been busy building software for the past 15 years and still enjoys every bit of it (literally). He engineered and shipped products across various domains from 3-D cardiac imaging with real-time in-vessel tracking to web-scale systems with global distribution to deep learning frameworks and tools used by engineers and scientists worldwide.

Presentations

Personalization at scale: Challenges and practical techniques Session

Hagay Lupesko explores AI-powered personalization at Facebook and the challenges and practical techniques it applied to overcome these challenges. You'll learn about deep learning-based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice.

Rubayat Mahmud is a director of sales and business development at QuEST Global, where he’s involved in scaling emerging technology (AI, deep learning, ML, and IOT) solutions with QuEST Global’s customers and partners, and he drives the relationship between QuEST Global and Intel Corporation. Previously, Rubayat was with Intel for 10 years, where he worked in multiple divisions including PTD, CQN, ATTD, and IOTG. Rubayat has a PhD in chemical engineering from Texas A&M University and an MBA from Arizona State University.

Presentations

QuEST vision analytics solution with OpenVINO and Intel AI AI in the Enterprise: The Intel® AI Builders Showcase Event

QuEST Global has established an Intel AI Lab for developing and scaling Intel AI-based solutions globally. Rubayat Mahmud showcases some of QuEST Global's Intel AI-based solutions and dives into the Intel AI value proposition for solutions in different verticals.

Sunil Mallya is a Principal Deep Learning Scientist at Amazon Web Services.

Presentations

Build, train, and deploy predictive maintenance models at industrial scale (sponsored by Amazon Web Services) Session

Sunil Mallya explores how to use data from equipment to build, train, and deploy predictive models. You'll dive deep into the architecture, deployment guide, and development resources for using the turbofan degradation simulation dataset to train the model to recognize potential equipment failures.

Making reinforcement learning practical for real-world developers (sponsored by Amazon Web Services) Session

Sunil Mallya walks you through how to build complex ML-enabled products using reinforcement learning (RL), explores hardware design challenges and trade-offs, and details real-life examples of how any developer can up level their RL skills through autonomous driving.

Chaithanya Manda is an assistant vice president at EXL, where he’s responsible for building AI-enabled solutions that can bring efficiencies across various business processes. He has over 10 years of experience in developing advanced analytics solutions across multiple business domains. He holds a bachelor’s of technology degree from the Indian Institute of Technology Guwahati.

Presentations

Improving OCR quality of documents using generative adversarial networks Session

Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an optical character recognition (OCR) solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how GANs can bring significant efficiencies by enhancing resolution and denoising scanned images.

James Manyika is a director of the McKinsey Global Institute, McKinsey & Company’s business and economics research arm, and one of its three global coleaders. James has led research on business and global economic trends, including the digital economy, globalization, growth and productivity, innovation and competitiveness, and labor markets. James is also a director at McKinsey, where he is one of the leaders of McKinsey’s global high-tech, media, and telecom practice. Based in Silicon Valley for 20 years, he’s worked with many of the world’s leading technology companies on a variety of issues. He’s published a book on distributed networks and robotics another on globalization, as well as numerous academic and business papers and reports. In 2012, James was appointed by President Obama to serve on of the US President’s Global Development Council and in 2013 to serve as the vice chairman of the council. In 2011, James was appointed by the US Secretary of Commerce to serve on the Innovation Advisory Board as part of the COMPETES Act. James serves on the boards of the Aspen Institute; the Oxford Internet Institute; the University of California, Berkeley, School of Information; the Harvard Hutchins Center for African & African American Research, including the W. E. B. Du Bois Research Institute; and the School of Global Affairs and Public Policy at the American University in Cairo. James is a nonresident senior fellow of the Brookings Institution, a member of the Council on Foreign Relations, and a member of the Bretton Woods Committee. James was on the engineering faculty at Oxford University and a fellow at Balliol College, Oxford University; a visiting scientist at NASA’s Jet Propulsion Laboratory; and a faculty exchange fellow at MIT. A Rhodes Scholar, James holds DPhil, MSc, and MA degrees from Oxford in engineering, mathematics, and computer science, respectively, and a BSc in electrical engineering from the University of Zimbabwe.

Presentations

Executive Briefing: AI for social good Session

AI has the potential to create substantial value for business and the global economy. It's less well understood how it can address some of the world’s biggest societal challenges. Michael Chui and James Manyika examine the ethical implications of AI and how you can leverage the technology for good while considering the wide-reaching repercussions on business and human society alike.

Brian McMahan is a data scientist at Wells Fargo, working on projects that apply natural language processing (NLP) to solve real world needs. Recently, he published a book with Delip Rao on PyTorch and NLP. Previously, he was a research engineer at Joostware, a San Francisco-based company specializing in consulting and building intellectual property in NLP and Deep Learning. Brian is wrapping up his PhD in computer science from Rutgers University, where his research focuses on Bayesian and deep learning models for grounding perceptual language in the visual domain. Brian has also conducted research in reinforcement learning and various aspects of dialogue systems.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao and Brian McMahan explore natural language processing using a set of machine learning techniques known as deep learning. They walk you through neural network architectures and NLP tasks and teach you how to apply these architectures for those tasks.

Dea Milojicic is a distinguished technologist at Hewlett Packard Labs, leading system software teams in the US, Brazil, and India. Previously, he worked at the OSF Research Institute in Cambridge, Massachusetts; at the Mihajlo Pupin Institute in Belgrade, Serbia; and he was a technical director of the Open Cirrus Cloud Computing Testbed, with academic, industrial, and government sites in the US, Europe, and Asia. Dean earned his PhD from Kaiserslautern University, Germany, and his MSc and BSc from Belgrade University, Serbia. He’s published two books and 180 papers; he has 31 granted patents. He’s an IEEE Fellow and ACM Distinguished Engineer. Dean was on eight PhD thesis committees and taught the cloud management course at SJSU. As president of the IEEE Computer Society, he started Tech Trends, the top-viewed CS news. As the IEEE industry engagement chair, he started IEEE Infrastructure ’18 conference dedicated for industry.

Presentations

Software toolchain for the hybrid digital-analog, memristor-based accelerator for machine learning Session

Dejan Milojicic examines a software stack designed for the special-purpose machine learning accelerator. The software stack improves usability and programmability of the accelerator, making it accessible from common machine learning frameworks. The software toolchain also exposes the intricacies of the parallelism of the accelerator while hiding its complexities.

Raj Minhas is the vice president and director of the interaction and analytics laboratory with PARC, which focuses on people, their behaviors, and interactions with machines. The lab conducts research on all aspects of human-machine interaction including computer vision, conversation agents, explainable AI, and human-machine teaming. Previously, Raj was the director of Xerox Research Center India for two years; held a variety of leadership roles at Xerox Research Center Webster, including manager of analytics and large-scale computing research. He earned his MS and PhD in electrical and computer engineering from the University of Toronto.

Presentations

Executive Briefing: Ethical considerations for AI Session

The use of AI is growing rapidly and expanding into applications that impact people’s lives. Raj Minhas explores how, while researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications.

Vishnu Mohan is a director of product management at Determined AI, where he works closely with customers to improve the productivity of their research teams on their deep learning initiatives. Previously, he was a director of product at Mesosphere. He holds an MS in computer science from the University of Texas at Dallas.

Presentations

Deep learning at scale: Tools and solutions Tutorial

Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.

Ali Mollahosseini is a senior machine learning engineer and tech lead of content understanding and applied deep-learning (CUAD) team at Twitter Cortex. His research focuses on NLP and developing neural network architectures to improve Twitter’s understanding of content on the platform using the latest advances in deep learning. He received his PhD in computer engineering from the University of Denver. He’s published 15 papers in prestigious journals and conferences and has two patents with more than 500 citations.

Presentations

Named entity recognition at scale with deep learning Session

Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build a deep understanding of its noisy and temporal text content. Sijun He and Ali Mollahosseini explore the named entity recognition (NER) system at Twitter and the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day.

Philipp Moritz is a PhD candidate in the electrical engineering and computer sciences (EECS) department at the University of California, Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He’s a member of the Statistical AI Lab and the RISELab.

Presentations

Building reinforcement learning models and AI applications with Ray Tutorial

Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms.

Kurt Muehmel is the vice president of solutions engineering at Dataiku, where he’s built analytics and AI solutions for Fortune 100 companies worldwide and is building its solutions engineering capability worldwide. Having worked with dozens of clients of all sizes and across a multitude of sectors, Kurt has developed a deep understanding of the challenges and opportunities for companies looking to increase the value they’re deriving from their data and increase the capabilities of the growing teams of data scientists, engineers, and analysts. In a career that’s spanned several international moves, he’s worked for the United Nations, a Big Four consultancy, and a struggling high school in the Paris suburbs

Presentations

The race to 10,000 data scientists deploying 1,000,000 models (sponsored by Dataiku) Session

We're rapidly closing in on a future where large companies across different sectors will be enriching every business process and decision with AI and gaining a sustained competitive edge as a result. Join Kurt Muehmel on a forward-looking exploration of companies that are already well on their way toward this target. He details Dataiku's vision of the journey ahead.

Srinivas Narayanan leads the Applied Research team at Facebook AI doing research and development in a wide range of areas such as PyTorch, computer vision, natural language, speech, and personalization to push the state of the art in AI to advance Facebook products. He’s led several major efforts at Facebook, including creating the interest graph, launching the location product, and leading engineering for photos, where he also helped start Facebook’s efforts in computer vision and deep learning. Previously, he was a founding member of two startups and part of the database systems research group at IBM Research – Almaden.

Presentations

Going beyond fully supervised learning Keynote

Srinivas Narayanan takes you beyond fully supervised learning techniques, the next change in AI.

Paco Nathan is known as a “player/coach” with core expertise in data science, natural language processing, machine learning, and cloud computing. He has 35+ years of experience in the tech industry, at companies ranging from Bell Labs to early-stage startups. His recent roles include director of the Learning Group at O’Reilly and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.

Presentations

Executive Briefing: Unpacking AutoML Session

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way.

Stef Nelson-Lindall is a tech lead for PyText, an open source Facebook project for experimentation, training, and productionization of NLP models using PyTorch. He worked to use this project to deploy Transformer architecture models to real-time production systems at Facebook. Previously, he built NLP and dialog systems for products across Facebook, built CRM systems at Google, and helped build web-based medical image viewers at Vital Images. He holds bachelors degrees in math and computer science from the University of Minnesota.

Presentations

PyTorch at scale for translation and NLP Session

PyText is a research to production platform that Facebook has leveraged to quickly develop state-of-the-art natural language processing (NLP) systems and deploy them to critical production use cases. Stef Nelson-Lindall explores several challenges with developing, training, and deploying real production systems with Torch, how to deal with them in NLP use cases, and more.

Dinesh Nirmal is vice president of development for data and AI at IBM. His mission is to empower every organization to transform their industry—whether it’s aerospace, finance, or healthcare—by unlocking the power of their data. Dinesh speaks and writes internationally on operationalizing machine learning and advises business leaders on strategies to ready their enterprises for new technologies. He leads more than a dozen IBM Development Labs globally; recognizing a market need for data science mastery, he launched six Machine Learning Hubs to work face-to-face with clients. Products in his portfolio regularly win major design awards, including two Red Dot Awards and the iF Design Award. Dinesh is a member of the board of the R Consortium and an advisor to Accel.AI. He lives in San Jose with his wife Catherine Plaia, formerly an engineer at Apple, and their two young sons.

Presentations

Unlocking the value of your data (sponsored by IBM Watson) Keynote

Dinesh Nirmal examines how, with a unified, prescriptive information architecture, organizations can successfully unlock the value of their data for AI as well as trust and control the business impact and risks of AI while coexisting in a multicloud world.

Robert Nishihara is a fourth-year PhD student working in the University of California, Berkeley, RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.

Presentations

Building reinforcement learning models and AI applications with Ray Tutorial

Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms.

Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up. He’s writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll (a quoll is an Australian animal).

Presentations

Build a self-driving car without a car: ML problem-solving with a game engine Tutorial

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine.

Live coding a self-driving car (without a car) Session

Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines.

Debo Olaosebikan is the founder and chief technology officer of Gigster, which has shipped over 1,000 projects for the largest companies in the world, and he’s an entrepreneur and engineer based in San Francisco. He’s founded multiple marketplace, energy, and AI startups and is on leave from a physics PhD at Cornell University, where he was lead researcher from Cornell on an MIT and Department of Defense project to build the world’s first electric silicon laser. Through various projects and startups in Nigeria and the US, he has worked on labor marketplaces, AI for software testing and medical diagnoses, natural language processing (NLP)-based query and recommendation engines, carbon nanotube theory, spintronics, nanophotonics, interactive books and authoring tools, silicon lasers, collaborative data science systems, and optical tweezers. He was once a radio-featured musician and was the young Nigerian scientist of 2011. Debo advises startups and helps young founders as a mentor at the Thiel Fellowship.

Presentations

Repeatable AI-driven digital transformation: Insights from 1,000 projects Session

As the gap between technology giants and the rest of the enterprise widens, AI-driven transformation has become essential and urgent. From the lens of over 1,000 projects delivered and a broad view across real use cases in multiple industries, Debo Olaosebikan examines an organizational and technical framework for using AI to drive business impact regardless of where an organization starts from.

Richard Ott is a data scientist in residence at the Data Incubator, where he combines his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Presentations

Deep learning with PyTorch 2-Day Training

PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott.

Alex Palladini is the innovation leader at Music Tribe, where he oversees research in various areas like human-computer interaction, machine learning, and AI, as well as the development of new products based on AI. His research in the field of AI is focused on the user experience of intelligent systems for complex and creative applications.

Presentations

Mozart in the box: Interacting with AI tools for music creation Session

Alessandro Palladini explores the role of experts and creatives in a world dominated by intelligent machines by bridging the gap between the research on complex systems and tools for creativity, examining what he believes to be the key design principles and perspectives on making intelligent tools for creativity and for experts in the loop.

Lei Pan is a senior director of engineering, cloud services at Nauto, leading efforts on building and scaling platform services of core, data, and machine learning infrastructure. Previously, Lei led engineering teams and architecture efforts for a number of startups, focusing on video summarization, segmentation, transcoding, and distribution.

Presentations

Safe and smarter driving, powered by AI (sponsored by Amazon Web Services) Keynote

Lei Pan examines how Nauto uses Amazon SageMaker and other AWS services, including Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to continually evolve smarter data for driver behavior.

Ravi Panchumarthy is a machine learning engineer in the Artificial Intelligence Products Group (AIPG) at Intel. He collaborates with Intel’s customers and partners to build and optimize AI solutions. He also works with cloud service providers to enable Intel’s AI optimizations in cloud instances and services. He has a PhD in computer science and engineering from University of South Florida with a dissertation focused on developing novel nonboolean computing techniques for computer vision applications using nanomagnetic field-based computing. He holds two patents and several peer-reviewed publications in journals and conferences.

Presentations

Accelerating deep learning workloads in the cloud and data centers AI in the Enterprise: The Intel® AI Builders Showcase Event

Ravi Panchumarthy gets you started to quickly set up Intel-optimized AI environments for your workloads, be it in the cloud or in your data center.

Labhesh Patel is CTO and chief scientist at Jumio, where he’s responsible for driving the company’s innovation in the identity verification space with deep learning, computer vision, and augmented intelligence—an alternative conceptualization of artificial intelligence that focuses on AI’s assisted role to enhance human intelligence. An accomplished leader with over 15 years of experience in corporate and entrepreneurial settings, Labhesh has proven experience leading engineering teams, launching new online services (from concept creation to customer delivery), and developing ground-breaking technologies at companies including Cisco, Abzooba, xpresso.ai, Spotsetter, and CellKnight. He has 175 patents filed with another 134 patents issued under his name. Labhesh holds an MS in electrical engineering (MSEE) from Stanford University and a BT from the Indian Institute of Technology in Kanpur.

Presentations

The challenges and opportunities of augmented intelligence at scale (sponsored by Jumio) Session

Labhesh Patel explores how deep learning informs computer vision through smarter data extraction, fraud detection, and risk scoring. Labhesh details what it takes to put AI in production and how a machine learning infrastructure needs to be fundamentally thought out to allow for better human-in-the-loop AI workflows.

Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.

Presentations

Getting started with PyTorch Tutorial

PyTorch captured the minds of ML researchers and developers upon its arrival. Now it's matured into a production-ready ML framework with use cases and applications. Mo Patel explores the PyTorch lifecycle via hands-on examples such as image and text classification and linear modeling. You'll cover other aspects of ML such as transfer learning, data modeling, and deploying to production in labs.

Jonathan Peck is developer and technical advocate at Algorithmia. He’s a full-stack developer with two decades of industry experience, now focusing on bringing scalable, discoverable, and secure machine learning microservices to developers across a wide variety of platforms via Algorithmia.com. He’s been a speaker at DeveloperWeek 2018+19, SeattleJS, Global AI Conf 2018+19, AI Next 2019+19, Nordic APIs 2018+19 (keynote), ODSC East+West, API World, O’Reilly AI, OSCON

Presentations

The OS for AI: How serverless computing enables the next gen of machine learning Session

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.

Justina Petraityte is a developer advocate at Berlin-based startup Rasa, where she helps improve the developer experience in using open source software for conversational AI. Justina has a background in econometrics and data analytics, and her interests include chatbots, natural language processing, and open source. Her curiosity for data science and human-behavior analytics has taken her to many places and industries; over the past three years, she’s worked in the video gaming, fintech, and insurance industries.

Presentations

Going beyond FAQ assistants with machine learning and open source tools Tutorial

AI assistants are among the most in-demand topics in tech. Get hands-on experience with Justina Petraityte as you develop intelligent AI assistants based entirely on machine learning and using only open source tools—Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data.

Vadim Pinskiy is the vice president of research and development at Nanotronics, where he oversees product development, short-term R&D, and long-term development of AI platforms. Vadim completed his doctorate work in neuroscience, focused on mouse neuroanatomy using high throughput whole slide imaging and advanced tracing techniques. Previously, he earned his master’s in biomedical engineering from Cornell University and his bachelor’s and master’s in electrical and biomedical from the Stevens Institute of Technology. Vadim is interested in applying advanced AI methods and systems to solving practical problems in biological and product manufacturing.

Presentations

Development and application of advanced AI decision making for manufacturing Session

Statistical manufacturing has remained largely unchanged since postwar Japan. AI and DL allow for nonlinear feedback and feed-forward systems to be integrated for real-time monitoring and evolution of each part assembly. Vadim Pinskiy explores a system capable of detecting, classifying, and automatically correcting for manufacturing defects in a multinodal process.

Dulce Ponceleón is a principal research staff member in the Infrastructure for Intelligent Information Systems Group at IBM Research – Almaden. Her broad interests across different disciplines include natural language processing, machine learning, blockchain, and security. She led IBM’s content protection team resulting in significant contributions to Blu-ray Content Protection Standard’s consortium. Previously, she has worked in information retrieval, multimedia content analysis, video summarization, speech recognition, numerical linear algebra, nonlinear programming, storage systems, and content protection, and she was a key contributor to QuickTime Conferencing’s video and audio compression at Apple. She earned her master’s and PhD in computer science from Stanford University and her BS (cum laude) in computer science from Universidad Simon Bolivar in Caracas, Venezuela.

Presentations

Toward universal semantic understanding of natural languages Session

Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes.

Shashank Prasanna is a senior AI and machine learning evangelist at Amazon Web Services, where he focuses on helping engineers, developers, and data scientists solve challenging problems with machine learning. Previously, he worked at NVIDIA, MathWorks (makers of MATLAB), and Oracle in product marketing and software development roles focused on machine learning products. Shashank holds an MS in electrical engineering from Arizona State University.

Presentations

Running large-scale machine learning experiments in the cloud Session

Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.

Ramesh Radhakrishnan is a distinguished engineer on the CTO team in the Server and Infrastructure Solutions Group at Dell, where he drives technology strategy for advanced analytics and machine learning and deep learning. At Dell, he’s led technology strategy and architecture in the areas of Energy Efficient uServer Architecture (ARM and Xeon-D), Microsoft hybrid cloud, and workload-optimized systems. He’s a member of the Dell Patent Committee and has 15 published patents.

Presentations

From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies) Session

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs.

Michael Radwin is the vice president of data science at Intuit with responsibility for leading a team dedicated to using artificial intelligence and machine learning models for security, antifraud, and risk. Previously, Michael used machine learning ensemble methods to fight online advertising fraud as the vice president of engineering with Anchor Intelligence, and he built ad-targeting and personalization algorithms with neural networks and Naive Bayesian classifiers and scaled web platform technologies, Apache, and PHP as director of engineering with Yahoo. He holds an ScB in computer science from Brown University.

Presentations

Data science + design thinking: A perfect blend to achieve the best user experience Session

Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.

Maithra Raghu is a research scientist at Google Brain, where her research focuses on developing tools to understand deep neural networks and using these insights in healthcare applications. She’s a PhD candidate at Cornell University, and she’s been named as one of the Forbes 30 Under 30 in Science and an EECS Rising Star by MIT.

Presentations

Artificial and human intelligence in healthcare Session

With the fundamental breakthroughs in artificial intelligence and the significant increase of digital health data, there's been enormous interest in AI for healthcare applications. Maithra Raghu examines how to more effectively develop AI algorithms for these settings and the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts.

Anand Rao is a partner in PwC’s Advisory Practice and the innovation lead for the Data and Analytics Group, where he leads the design and deployment of artificial intelligence and other advanced analytical techniques and decision support systems for clients, including natural language processing, text mining, social listening, speech and video analytics, machine learning, deep learning, intelligent agents, and simulation. Anand is also responsible for open source software tools related to Apache Hadoop and packages built on top of Python and R for advanced analytics as well as research and commercial relationships with academic institutions and startups, research, development, and commercialization of innovative AI, big data, and analytic techniques. Previously, Anand was the chief research scientist at the Australian Artificial Intelligence Institute; program director for the Center of Intelligent Decision Systems at the University of Melbourne, Australia; and a student fellow at IBM’s T.J. Watson Research Center. He has held a number of board positions at startups and currently serves as a board member for a not-for-profit industry association. Anand has coedited four books and published over 50 papers in refereed journals and conferences. He was awarded the most influential paper award for the decade in 2007 from Autonomous Agents and Multi-Agent Systems (AAMAS) for his work on intelligent agents. He is a frequent speaker on AI, behavioral economics, autonomous cars and their impact, analytics, and technology topics in academic and trade forums. Anand holds an MSc in computer science from Birla Institute of Technology and Science in India, a PhD in artificial intelligence from the University of Sydney, where he was awarded the university postgraduate research award, and an MBA with distinction from Melbourne Business School.

Presentations

A practical guide to responsible AI (sponsored by PwC) Session

Anand Rao provides an overview from the practitioner’s perspective on addressing ethics within businesses. Anand explores PwC’s responsible AI toolkit, which enables businesses to identify and contextualize relevant ethical AI principles and provides tools for evaluating interpretability of systems. You'll see example applications that illustrate model interpretability.

Delip Rao is the vice president of research at the AI Foundation, where he leads speech, language, and vision research efforts for generating and detecting artificial content. Previously, he founded the AI research consulting company Joostware and the Fake News Challenge, an initiative to bring AI researchers across the world together to work on fact checking-related problems, and he was at Google and Twitter. Delip is the author of a recent book on deep learning and natural language processing. His attitude toward production NLP research is shaped by the time he spent at Joostware working for enterprise clients, as the first machine learning researcher on the Twitter antispam team, and as an early researcher at Amazon Alexa.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao and Brian McMahan explore natural language processing using a set of machine learning techniques known as deep learning. They walk you through neural network architectures and NLP tasks and teach you how to apply these architectures for those tasks.

Vinay Rao is the cofounder and CEO of RocketML, a machine learning platform on a mission to lead and enable transformation of the world toward artificial intelligence. RocketML implements bleeding-edge learning algorithms to perform at scale, delivering “near-real-time” training performance on any data size.

Presentations

Semisupervised machine learning, the next frontier in AI Session

Current deep learning approaches require large amounts of labeled data. The creation of labeled data is expensive, error prone, and time consuming. Vinay Rao and Santi Adavani walk you through an effective learning method with minimum labelled data and human intervention.

Alex Ratner is the project lead of Snorkel, a system for programmatically building and managing training datasets for machine learning, and (starting in 2020) an assistant professor of computer science at the University of Washington. Previously, he completed his PhD in CS advised by Christopher Ré at Stanford, where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows, such as creating and managing training data, and applying this to real-world problems in medicine, knowledge base construction, and more. At Stanford, he started and led the Snorkel project, which has been deployed at large technology companies like Google, academic labs, and government agencies and was recognized in VLDB 2018 (“Best Of”).

Presentations

Building and managing training datasets for ML with Snorkel Session

Alex Ratner explores programmatic approaches to building, managing, and modeling training data for machine learning (ML) using the open source framework Snorkel. Training data is increasingly one of the key bottlenecks to using modern ML, and Alex outlines recent systems and algorithmic and theoretical advances in building and managing training data for ML.

Shourabh Rawat is a senior engineering manager of applied sciences at Zillow. He has over 5 years of industry experience working in AI, deep learning, computer vision, and personalization, deploying these systems to production at scale. Shourabh and his team focus on developing data science solutions to gain a better understanding of Zillow’s customers, specifically how they engage with content and property recommendations. Shourabh completed his master’s degree from Carnegie Mellon University where he did research on event detection in consumer videos, applying deep learning on multimodal (audio and images) data.

Presentations

Using deep learning models to extract the most value from 360-degree images Session

Lately, 360-degree images have become ubiquitous in industries from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses to direct viewers to the most important parts of the scene. Shourabh Rawat walks you through how to identify and extract engaging static 2-D images using specific algorithms and deep learning methods.

Joy Rimchala is a data scientist in Intuit’s Machine Learning Futures Group working on ML problems in limited-label data settings. Joy holds a PhD from MIT, where she spent five years doing biological object tracking experiments and modeling them using Markov decision processes.

Presentations

Document understanding: Extracting structured information from financial images and forms Session

Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service.

Daniel Russakoff is cofounder (with Jonathan Oakley) and principal scientist of Voxeleron LLC, where he combines traditional computer vision with cutting-edge AI to develop advanced algorithms for ophthalmic image analysis. Previously, he was a computer scientist at the National Institute of Standards and Technology and chief scientist at Fujifilm’s San Jose Research Lab. He’s authored numerous conference and journal papers and holds a number of patents on topics ranging from stereo vision to AI-enabled disease prognostics. He earned his AB in geophysics from Harvard University and his PhD in computer science from Stanford University. His research interests are in computer vision and machine learning in general, and biomedical image analysis in particular.

Presentations

AI for ophthalmology: Doing what doctors can’t (sponsored by Dell Technologies) Keynote

The emphasis in AI is on replicating human performance. Examples abound: ImageNet, self-driving cars, etc. It’s the same in medicine. Daniel Russakoff explains how Voxeleron LLC is working on what’s next—AI algorithms that do things that humans can’t, such as the prediction of age-related macular degeneration (AMD) progression, critical to successful treatment of this leading cause of vision loss.

Brennan Saeta is a software engineer on the Google Brain team leading the Swift for TensorFlow project. Previously, he was the TensorFlow tech lead for Cloud TPUs.

Presentations

Swift for TensorFlow: A next-generation framework for differential programming Session

Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. Brennan Saeta leads you through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects.

Mathew Salvaris is a senior data scientist at Microsoft. Previously, Mathew was a data scientist for a small startup that provided analytics for fund managers; a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will and devised models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and he held a postdoctoral position at the University of Essex’s Brain Computer Interface group and was a visiting researcher at Caltech. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.

Presentations

Reference architectures for AI and machine learning Session

Join Danielle Dean, Mathew Salvaris, and Angus Taylor to learn best practices and reference architectures (which have been validated in real-world AI and ML projects for customers globally) for implementing AI. They detail lessons distilled from working with large global customers on AI and ML projects and the challenges that they overcame.

Ananth Sankaranarayanan is a senior director of AI technical acceleration and scaling at Intel, where he’s responsible for leading a worldwide team that enables customers and partners to build AI solutions with the maximum benefit of Intel hardware and software capabilities. Previously, he led the creation of the big data analytics solutions team and championed multiple high-growth industry-first solutions in intelligent transportation, healthcare, retail, and financial services segments and drove them to a worldwide scale. Ananth won the “Intel Achievement Award,” the highest employee recognition, for his transformative work in the creation of high-performance computing (top 500 supercomputer) production capability to accelerate silicon design and manufacturing. Ananth earned his bachelor’s of engineering in computer science and his master’s degree in business administration from the City University of Seattle. Ananth is a strong supporter of improving education, and as a portion of his volunteer work, he coaches middle and high school robotics and STEM teams who have won national-level recognitions for engineering and teamwork. Ananth holds two patents and has coauthored more than 10 publications and a book, AI for Autonomous Networks, the proceeds of which go to the Girls Who Code nonprofit organization.

Presentations

Accelerate with purpose Keynote

Ananth Sankaranarayanan discusses three key shifts in the AI landscape—incredibly large models with billions of hyperparameters, massive clusters of compute nodes supporting AI, and the exploding volume of data meeting ever-stricter latency requirements—how to navigate them, and when to explore hardware acceleration.

Driving business impact with the Intel AI technology portfolio AI in the Enterprise: The Intel® AI Builders Showcase Event

Many industries embrace new ways to extract meaningful value from their data to offer differentiated services. Ananth Sankar explores a framework for building analytics and AI applications on top of your data platform. He dives into implementation choices and specific examples of how you can take advantage of Intel AI hardware and software capabilities at scale to deliver business value.

Alejandro Saucedo is the chief scientist at the Institute for Ethical AI & Machine Learning, where he leads highly technical research on machine learning explainability, bias evaluation, reproducibility and responsible design. Previously, Alejandro held technical leadership positions across hypergrowth scale-ups and tech giants including Eigen Technologies, Bloomberg LP, and Hack Partners. He has a strong track record of building departments of machine learning engineers from scratch and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing, and construction sectors (in Europe, the US, and Latin America).

Presentations

A practical guide toward explainability and bias evaluation in AI and machine learning Session

Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge.

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Presentations

Deep learning with TensorFlow 2-Day Training

The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Yuan Shen is the founder and CEO of OneClick.ai, a leading platform in automating AI model design and deployment committed to making AI accessible for everyone. Yuan has over 10 years of experience in machine learning across multiple industries from teaching computers to detect tumors in the CT images to building an image search engine to serving relevant online advertisements to making selling on ecommerce site easy and efficient. Previously, Yuan worked at Microsoft and eBay. He’s also a frequent speaker at Seattle local AI meetups, evangelizing the technologies and various applications to broader audiences from different backgrounds, inspiring them study and practice in the wave of this new AI technology.

Presentations

Automated time series forecasting and optimization for enterprises AI in the Enterprise: The Intel® AI Builders Showcase Event

Yuan Shen outlines OneClick.ai, an application-focused AI platform using automated deep learning AI technology to enable businesses to use advanced predictive analysis and decision making. It encompasses easy and customizable solutions for consumer packaged goods (CPG), retail, and financial industries to solve their top challenges, such as forecasting, dynamic pricing, and product recommendation.

Julie Shin Choi is VP and GM of Artificial Intelligence Products and Research Marketing at Intel Corporation. She is responsible for marketing the Intel portfolio of hardware and software products for building enterprise scale AI solutions. She is driving the AI marketing strategy across Intel, helping customers, developers, and the ecosystem understand Intel’s rich set of AI offerings.

Prior to joining Intel, she led product marketing at Hewlett Packard Enterprise, Mozilla and Yahoo, focused on developer and enterprise audiences.

Julie holds a bachelor’s degree from MIT and a master’s degree from Stanford, both in Management Science.

Presentations

Thursday Opening Welcome Keynote

Program chairs Ben Lorica, Roger Chen, and Julie Choi open the second day of keynotes.

Wednesday Opening Welcome Keynote

Program chairs Ben Lorica, Julie Choi, and Roger Chen open the first day of keynotes.

Nagendra Shishodia is the head of analytics products for EXL, where he leads the analytics product development initiative and has written thought leadership articles on healthcare clinical solutions and AI. He has over 17 years of experience in developing advanced analytics solutions across business functions. His focus has been on developing solutions that enable better decision making through the use of machine learning, natural language processing, and big data technologies. Nagendra consults with senior executives of global firms across industries including healthcare, insurance, banking, retail, and travel. Nagendra holds an MS degree from Purdue University and a BTech from the Indian Institute of Technology Bombay.

Presentations

Improving OCR quality of documents using generative adversarial networks Session

Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an optical character recognition (OCR) solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how GANs can bring significant efficiencies by enhancing resolution and denoising scanned images.

Chaitanya Shivade is a research staff member at IMB Research, where he uses machine learning and natural language processing to solve problems with clinical data.

Presentations

Language inference in medicine Session

Using deep learning models to perform natural language inference (NLI) is a fundamental task in natural language processing. Chaitanya Shivade introduces a recently released dataset, MedNLI, for this task in the clinical domain, describes state-of-the-art models, explores how to adapt these into the healthcare domain, and details applications that can leverage these models.

Guoqiong Song is a senior deep learning software engineer on the big data technology team at Intel. She’s interested in developing and optimizing distributed deep learning algorithms on Spark. She holds a PhD in atmospheric and oceanic sciences with a focus on numerical modeling and optimization from UCLA.

Guoqiong Song是英特尔大数据技术团队的高级深度学习软件工程师。 她拥有加州大学洛杉矶分校的大气和海洋科学博士学位,专业方向是数值建模和优化。 她现在的研究兴趣是开发和优化分布式深度学习算法。

Presentations

Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark Tutorial

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA.

Evan Sparks is a cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from the University of California, Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

Presentations

Unshattering the mirror: Defragmenting the deep learning ecosystem Session

Evan Sparks walks you through the current gap between the AI haves (Google, Facebook, Amazon, and Microsoft) and the AI have-nots (the rest of the industry), from the perspective of software infrastructure for model development. You'll learn some of the opportunities for end-to-end system design to enable rapid iteration and scale in AI application development.

Joseph Spisak is the product manager for Facebook’s AI open source platform, including PyTorch and ONNX. Previously, he led AI partnerships and deep learning products at Amazon Web Services, where he and his team were dedicated to building tools and solutions to help democratize deep learning for the developer community and ultimately accelerate the development of deep learning-based applications. Joseph holds a bachelor’s degree in electrical engineering from Michigan State University and an MBA and MS in finance from the University of Denver. He’s a proud graduate of the Entrepreneurial and Innovation Program at Stanford University Graduate School of Business.

Presentations

Scaling AI experiences at Facebook with PyTorch Session

Joseph Spisak and Hao Lu lead a deep dive into how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook.

Vijay Srinivas Agneeswaran is a director of data sciences at Walmart Labs in India, where he heads the machine learning platform development and data science foundation teams, which provide platform and intelligent services for Walmart businesses around the world. He’s spent the last 18 years creating intellectual property and building data-based products in industry and academia. Previously, he led the team that delivered real-time hyperpersonalization for a global automaker, as well as other work for various clients across domains such as retail, banking and finance, telecom, automotive, etc; he built PMML support into Spark and Storm and realized several machine learning algorithms such as LDA and Random Forests over Spark; he led a team that designed and implemented a big data governance product for a role-based fine-grained access control inside of Hadoop YARN; and he and his team also built the first distributed deep learning framework on Spark. He’s a professional member of the ACM and the IEEE (senior) for the last 10+ years. He has five full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems, artificial intelligence, and big data and other emerging technologies. Vijay has a bachelor’s degree in computer science and engineering from SVCE, Madras University, an MS (by research) from IIT Madras, a PhD from IIT Madras, and a postdoctoral research fellowship in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL).

Presentations

Industrialized capsule networks for text analytics Session

Vijay Agneeswaran and Abhishek Kumar explore multilabel text classification problems, where multiple tags or categories have to be associated with a given text or documents. Multilabel text classification occurs in numerous real-world scenarios, for instance, in news categorization and bioinformatics (such as the gene classification problem).

Kenneth O. Stanley is the Charles Millican professor of computer science at the University of Central Florida where he’s director of the Evolutionary Complexity Research Group, and he’s a senior research science manager and head of Core AI research at Uber Labs. Previously, he was a cofounder of Geometric Intelligence, which was acquired by Uber to create Uber AI Labs. He’s an inventor of the neuroevolution of augmenting topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e., evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He’s won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002–2012 from the International Society for Artificial Life. He’s a coauthor of the popular science book Why Greatness Cannot Be Planned: The Myth of the Objective (Springer) and has spoken widely on its subject. He earned a BSE from the University of Pennsylvania in 1997 and a PhD in 2004 from the University of Texas at Austin.

Presentations

Open-endedness: A new grand challenge for AI Keynote

We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continually—even endlessly.

Ion Stoica is a professor in the electrical engineering and computer sciences (EECS) department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Previously, he worked on dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He’s the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).

Presentations

Building reinforcement learning models and AI applications with Ray Tutorial

Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms.

Bargava Subramanian is a cofounder and machine learning engineer of the boutique AI firm Binaize Labs in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies, and he mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland, College Park. He’s an ardent NBA fan.

Presentations

Recommendation system using deep learning 2-Day Training

Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.

Roshan Sumbaly is an engineering manager at Facebook, where he leads computer vision efforts focused on visual people understanding and infrastructure. Previously, he led various teams at Coursera and LinkedIn, working on data products and infrastructure.

Presentations

Lessons from building Facebook's visual cortex Session

There aren't many systems in the world that need to run hundreds of computer vision models (from classification to segmentation) on billions of visual entities (images, videos, 3-D) daily. Roshan Sumbaly walks you through the challenges faced while building such a platform and how, surprisingly, a lot of the answers were found in traditional software engineering best practices.

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, agile, distributed teams. Previously, he led business operations for Bing Shopping in the US and Europe with Microsoft’s Bing Group and built and ran distributed teams that helped scale Amazon’s financial systems with Amazon in both Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

Executive Briefing: What you must know to build AI systems that understand natural language Session

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby outlines challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.

The turnkey high-compliance AI platform AI in the Enterprise: The Intel® AI Builders Showcase Event

Companies that deploy and operate ML and AI models in production, scale to teams and workflows, and need a reproducible and collaborative experimentation environment must often invest substantial effort in assembling their internal AI platforms. David Talby demonstrates a turnkey, complete, scalable, and highly secure AI platform that delivers this functionality to Fortune 500 companies today.

Ankur Taly is the head of data science at Fiddler, where he’s responsible for developing, productionizing, and evangelizing core explainable AI technology. Previously, he was a staff research scientist at Google Brain, where he carried out research in explainable AI, and was most well-known for his contribution to developing and applying integrated gradients— a new interpretability algorithm for deep networks. His research in this area has resulted in publications at top-tier machine learning conferences, and prestigious journals like the American Academy of Ophthalmology (AAO) and Proceedings of the National Academy of Sciences (PNAS). Besides explainable AI, Ankur has a broad research background and has published 25+ papers in areas including computer security, programming languages, formal verification, and machine learning. He’s served on several academic conference program committees (PLDI, POST, and PLAS), delivered several invited lectures at universities and various industry venues, and instructed short courses at summer schools and conferences. Ankur earned his PhD in computer science from Stanford University and a BTech in CS from IIT Bombay.

Presentations

Executive Briefing: Explaining machine learning models Session

As machine learning (ML) models get deployed to high-stakes tasks like medical diagnosis, credit scoring, and fraud detection, an overarching question that arises is why the model made its prediction. Ankur Taly explores techniques for answering this question and applications of the techniques in interpreting, debugging, and evaluating machine learning models.

Angus Taylor is a data scientist at Microsoft, where he builds AI solutions for customers. He holds a MSc in Artificial Intelligence and has previous experience in the retail, energy and government sectors.

Presentations

Reference architectures for AI and machine learning Session

Join Danielle Dean, Mathew Salvaris, and Angus Taylor to learn best practices and reference architectures (which have been validated in real-world AI and ML projects for customers globally) for implementing AI. They detail lessons distilled from working with large global customers on AI and ML projects and the challenges that they overcame.

Skyler Thomas is the chief architect for Kubernetes and a principal engineer at MapR, where he uses Kubernetes-based infrastructure to deliver machine learning and big data applications at scale. Previously, Skyler was a lead architect for WebSphere at IBM, where he worked with hundreds of customers to deliver extreme-scaled applications in various industries, including healthcare and financial services.

Presentations

Getting started with Kubeflow Tutorial

The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores the machine learning lifecycle from model training to model serving.

Solmaz Torabi is a data scientist at EXL, where she’s responsible for building image and text analytics models using deep learning methods to extract information from images and documents. She holds a PhD in electrical and computer engineering from Drexel University.

Presentations

Improving OCR quality of documents using generative adversarial networks Session

Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an optical character recognition (OCR) solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how GANs can bring significant efficiencies by enhancing resolution and denoising scanned images.

TJ Torres is a data scientist at Intuit, where he works on the ML futures team tackling research problems in the areas of computer vision (CV) and natural language processing (NLP) in order to better customer experience within Intuit’s core products. Previously, he worked as an applied ML researcher, including building fashion recommendation models using computer vision to help understand visual style at Stitch Fix and building models to help automatically analyze issues with sign-up conversion at Netflix. He holds a PhD in physics.

Presentations

Document understanding: Extracting structured information from financial images and forms Session

Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service.

Anusua Trivedi is a senior data scientist lead at Microsoft. She works on AI for Good—developing advanced deep learning models and AI solutions for humanitarian causes. Her focus is AI for healthcare, where she explores how AI can help make healthcare more affordable and accessible to everyone around the world. Previously, Anusua has held positions with UT Austin and the University of Utah. Anusua is a frequent speaker at machine learning and AI conferences.

Presentations

Transfer learning NLP: Machine reading comprehension for question answering Session

Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models.

Rohan Vaidyanathan is the lead Offering Manager for IBM Watson OpenScale. He is focused on building systems that can help bring transparency in AI to business stakeholders and help them trust the AI models infused in their applications. He works closely with IBM Research to productize technologies that can ensure fair, explainable, compliant and measurable outcomes from AI models. Before OpenScale, Rohan built and scaled the business for Hadoop and Spark analytics platform on IBM Cloud. He has a Bachelors degree in Computer Science from Mumbai University and an MBA from University of North Carolina, Chapel Hill.

Presentations

Operationalize AI at scale: From drift detection to monitoring the business impact of AI (sponsored by IBM Watson) Session

With the potential to transform businesses, AI has become a strategic imperative for most enterprises. A lot of investment is toward machine learning and deep learning models to support business applications. However, as Manish Bhide and Rohan Vaidyanathan explain, these models bring about risks and uncertainties that are difficult to manage.

Sundar Varadarajan is an industry expert in the field of analytics, machine learning and AI, having ideated, architected and implemented innovative AI solutions across multiple industry verticals. Currently, Sundar is a Consulting Partner at Wipro on AI/ML, and plays an advisory role on edge AI and Machine Learning solutions. Sundar can be reached at sundar.varadarajan@wipro.com

Presentations

Pipe Sleuth: AI-based pipeline assessment AI in the Enterprise: The Intel® AI Builders Showcase Event

Wipro’s Pipe Sleuth solution (in collaboration with DC Water) automates the identification of defects in pipeline videos, annotation of pipeline defects in the videos, scoring and grading of pipeline health, and generation of reports as per the Pipeline Assessment Certification Program (PACP) standards. Sundar Varadarajan dives into the Pipe Sleuth solution, optimized to run on Intel platforms.

Manasi Vartak is the founder and CEO of Verta.ai, an early-stage startup building software to help data science and machine learning teams rapidly build and integrate ML across products. Manasi is the creator of ModelDB, the first open source model management system used at Fortune 500 companies and in popular open source projects including Kubeflow. Manasi earned her PhD in computer science from MIT CSAIL, where she worked on software systems for data science and ML. Besides ML Infra, Manasi has worked on personalizing the Twitter newsfeed, automated data visualization, and ML model debugging. She’s a recipient of the Facebook PhD Fellowship and the Google Anita Borg Scholarship.

Presentations

Are we deployed yet? Turning AI research into a revenue engine Session

Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue-driving functions due to a lack of efficient tooling. Manasi Vartak examines key methods that enterprise AI teams can leverage with regard to driving revenue, including A/B testing, data pipelines, and reproducibility.

Nanda Vijaydev is the lead data scientist and distinguished technologist at BlueData (now HPE), where she leverages technologies like TensorFlow, H2O, and Spark to build solutions for enterprise machine learning and deep learning use cases. Nanda has more than 10 years of experience in data science and data management. Previously, she worked on data science projects in multiple industries as a principal solutions architect at Silicon Valley Data Science. She also served as director of solutions engineering at Karmasphere.

Presentations

Accelerate innovation with DevOps-like agility for machine learning pipelines AI in the Enterprise: The Intel® AI Builders Showcase Event

Nanda Vijaydev walks you through practical examples and lessons learned from ML and deep learning (DL) use cases in financial services, healthcare, and other industries. You’ll learn how to quickly spin up containerized multinode environments for TensorFlow and other ML and DL tools to train models in a multitenant architecture on-premises, in the cloud, or in a hybrid environment.

Unlock your data's value with AI (sponsored by HPE) Session

Join Pankaj Goyal and Nanda Vijaydev to learn how HPE put AI into action and helps enterprises unlock the value of their data with a proven, practical approach to AI.

Vikrant Viniak is the managing director of strategy at Accenture, focusing on Digital Transformation for Telecom Media and Technology (TMT) clients. He leads the high-tech strategy team globally. With over 18 years of experience in running transformations across various clients, he has proven ability to deliver measurable value and driving tangible results. His expertise includes helping clients on their digital transformation journey and using analytics and AI to drive value, including helping clients move from a pure product selling to subscription-based selling or Everything as a Service model to uncover new revenue streams. He also leads the competitive agility offering for TMT clients and has helped several clients with margin expansion. Over the course of his career, he has led several transformations leading to savings of hundreds of millions of dollars. Professionally, he’s active in various communities—AI, analytics, blockchain, ZBx, etc., and has published various point of views on these topics.

Presentations

Delivering AI vision ecosystem offers with Intel AI: In Production Session

Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings.

Bin Wang is a principal software engineering manager in the AI and Research Group of Microsoft, where he’s the tech manager of the multitenancy team and the go-to person across the entire platform team in this area. He’s initiated key efforts to improve the stability of YARN, which now is deployed to 30,000+ machines and supporting 30P+ cold data. He also leads efforts in supporting model training such as ChaNa and LR/MCLR on YARN, which has contributed to ads selection, PA, MM, AdInsight, relevance, etc. He leads the team to support Linux workloads on Windows by extending YARN to support on-demand VM lifecycle provisioning. The MT effort now extends to other key AIR scenarios, such as image processing, DR, Malta data processing, bot trainer, etc. Bin also leads the development of OSS DL training platform OpenPAI, which is specifically designed to be user friendly and extensible for various DL training frameworks and can run on on-premises as well as on cloud environments.

Presentations

Container orchestrator to DL workload, Bing's approach: FrameworkLauncher Session

Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.

Derek Wang is a cofounder of Stratifyd, where he’s focusing on the company’s vision to take data and AI democratization to the next level. The company’s goal is to empower business owners with individual AI assistants to automate their workflows. He grew up in Beijing and earned his PhD in computer science from the University of North Carolina at Charlotte. Shortly after, he—along with the company’s two other cofounders—began conducting government-funded research on the ways AI could be used to ingest, analyze, and visualize unstructured data. This postdoctorate work was the foundation Stratifyd was built on. As the company grows, Derek loves to give back to research community by offering opportunities to students and graduates, which helps foster entrepreneurship and grow the local tech community.

Presentations

Using AI to accelerate time to customer AI in the Enterprise: The Intel® AI Builders Showcase Event

Derek Wang explores Stratifyd's AI platform, an end-to-end customer engagement solution that analyzes, categorizes, and visualizes omnichannel customer feedback in real time, providing clients—including Fortune 500 companies such as Kimberly-Clark and Prudential and brands such as Etsy—with intelligence on both macro and micro levels.

Hui Wang is a staff data scientist at Intuit. Previously, he conducted fundamental natural language processing (NLP) research with grants from the National Institute of Standards and Technology (NIST) and the CIA and provided data modeling for investment banks and hedge funds. Hui has a PhD in chemical engineering from Yale.

Presentations

Document understanding: Extracting structured information from financial images and forms Session

Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service.

Jiao (Jennie) Wang is a software engineer on the big data technology team at Intel, where she works in the area of big data analytics. She’s engaged in developing and optimizing distributed deep learning framework on Apache Spark.

Jiao(Jennie)Wang是英特尔大数据技术团队的软件工程师,主要工作在大数据分析领域。她致力于基于Apache Spark开发和优化分布式深度学习框架。

Presentations

Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark Tutorial

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA.

Jisheng Wang is the head of data science at Mist Systems, where he leads the development of Marvis—the first AI-driven self-driving network solution that automates the visibility, troubleshooting, reporting, and maintenance of enterprise networking. He has 10+ years of experience applying state-of-the-art big data and data science technologies to solve challenging enterprise problems including security, networking, and IoT. Previously, he was the senior director of data science in the CTO office of Aruba, a Hewlett-Packard Enterprise company since its acquisition of Niara; as the chief scientist at Niara, he led the overall innovation and development effort in big data infrastructure and data science; he invented the industry’s first modular and data-agonistic User and Entity Behavior Analytics (UEBA) solution, which is widely deployed today among global enterprises; and he was a technical lead in Cisco, responsible for various security products. He earned a PhD in electric engineering from Penn State. Jisheng is a frequent speaker at AI and ML conferences, including Frontier AI, Spark + AI Summit, the O’Reilly Artificial Intelligence Conference, AI DevWorld, and Hadoop Summit.

Presentations

Building autonomous network operation using deep learning and AI Session

Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also details some of his experiences and the lessons he learned applying ML, DL, and AI to the development of SaaS-based enterprise solutions.

Yuqi Wang is a software engineer in the AI and Research Group of Microsoft. He has three years of experience in Apache YARN, container orchestration, and AI infrastructure. He’s the author and maintainer for Microsoft FrameworkLauncher, which is built to orchestrate all kinds of workloads through the same interface without making changes to the workload themselves. He has also internally contributed several features into YARN to support long-running service better on Windows. He’s working on the FrameworkLauncher to support AI workloads better and running natively on Kubernetes.

Presentations

Container orchestrator to DL workload, Bing's approach: FrameworkLauncher Session

Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.

Moshe Wasserblat is the Natural Language Processing and Deep Learning Research Group manager for Intel’s Artificial Intelligence Products Group. Previously, he was with NICE Systems for more than 17 years, where he founded and led the speech and text analytics research team. His interests are in the field of speech processing and natural language processing. He was the cofounder and coordinator of the EXCITEMENT FP7 ICT program and served as organizer and manager of several initiatives, including many Israeli chief scientist programs. He has filed more than 60 patents in the field of language technology and has several publications in international conferences and journals. His areas of expertise include speech recognition, conversational natural language processing, emotion detection, speaker separation, speaker recognition, deep learning, and machine learning.

Presentations

Challenges and future directions in deploying NLP in commercial environments Session

Moshe Wasserblat demonstrates the challenges and reviews the latest AI solutions in deploying natural language processing (NLP) in commercial environments, specifically dealing with the small amount of data available for training and scaling across different domains.

Mark Weber is a research scientist at the MIT-IBM Watson AI Lab, where he develops new graph analytics methods for anti-money laundering. His expertise is in connecting dots across disciplines to develop emergent technologies for positive real-world impact. Previously, he was at the MIT Media Lab working at the digital currency initiative, where he led the development of the b_verify protocol for publicly verifiable records, focused on warehouse receipts in agricultural supply chains; he produced documentary films on political economy and development, most notably Poverty, Inc., winner of over 50 film-festival honors and the $100,000 Templeton Freedom Award (available on Netflix and other platforms. He earned his MBA in finance from the MIT Sloan School of Management, where he was a fellow at the Legatum Center for Entrepreneurship and Development. As a public speaker, Mark enjoys opportunities to share his research and learn from others. He’s delivered talks at over 100 top universities, organizations, and events around the world. He can be found on Twitter as @markrweber.

Presentations

Fighting crime with graph learning Session

Organized crime inflicts human suffering on a massive scale: upward of 700,000 people per year are "exported" in a $40 billion human-trafficking industry enslaving an estimated 40 million people. Such nefarious industries rely on sophisticated money-laundering schemes to operate. Mark Weber explores how a new field of AI called graph convolutional networks can help.

Josh Weisberg is a senior director on the 3D and computer vision team for Zillow Group. Previously, he led the AI camera and computational photography team at Microsoft Research, spent several years at Apple, and four early-stage startups. He’s written four books on imaging and color. Josh studied digital imaging at the Rochester Institute of Technology and holds a bachelor’s of science degree from the University of San Francisco.

Presentations

Unlocking the next stage in computer vision with deep neural networks Session

Advances in AI and deep learning enable new technologies to mimic how the human brain interprets scenes, objects, and images, which has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. Josh Weisberg walks you through how implementing computer vision based in deep neural networks allows machines to see images in an entirely new way.

Sidney Wijngaarde is a software engineer at Determined AI, where he works closely with leading organizations to help them successfully apply deep learning using Determined AI’s cutting-edge software. Previously, Sidney worked on hybrid and multi-cloud management at IBM. He holds a BA from Dartmouth College.

Presentations

Deep learning at scale: Tools and solutions Tutorial

Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.

Angela Wu is a software engineer at Determined AI where she solves deep learning problems for leading organizations through Determined AI’s cutting-edge software. In a past life, Angela was a mathematician dabbling in property testing, list decoding, voting theory, and fast Fourier transforms. She holds a BA from Swarthmore College and a joint PhD in mathematics and computer science from the University of Chicago.

Presentations

Deep learning at scale: Tools and solutions Tutorial

Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.

Xiao Xiao is a data scientist in Intuit’s Consumer Group, using ML to enhance customer experience. Xiao holds a PhD in ecology and a MS in statistics, where she applied statistical analysis to study ecological patterns at broad spatial and temporal scales.

Presentations

Document understanding: Extracting structured information from financial images and forms Session

Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service.

Han Yang is a senior product manager at Cisco, where he drives UCS solutions for artificial intelligence and machine learning. And he’s always enjoyed driving technologies. Previously, Han drove the big data and analytics UCS solutions and the largest switching beta at Cisco with the software virtual switch, Nexus 1000V. Han has a PhD in electrical engineering from Stanford University.

Presentations

Data analytics at the retail edge AI in the Enterprise: The Intel® AI Builders Showcase Event

Join Han Yang to see how Cisco HyperFlex supports a variety of analytics, including AI and ML at the edge in a retail environment.

Yuhao Yang is a senior software engineer on the big data team at Intel, where he focuses on deep learning algorithms and applications—particularly distributed deep learning and machine learning solutions for fraud detection, recommendation, speech recognition, and visual perception. He’s also an active contributor to Apache Spark MLlib.

Presentations

Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark Tutorial

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA.

Wenming Ye is an AI/ML solutions architect at Amazon Web Services, helping researchers and enterprise customers to use cloud-based machine learning services to rapidly scale their innovations. Previously, Wenming had a diverse R&D experience at Microsoft Research, SQL engineering team, and successful startups.

Presentations

Put deep learning to work: A practical introduction using Amazon Web Services 2-Day Training

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye, Miro Enev, and Mahendra Bairag detail a practical next step in DL learning with instructions, demos, and hands-on labs.

Ting-Fang Yen is the director of research at DataVisor, the leading fraud, crime, and abuse-detection solution using unsupervised machine learning to detect fraudulent and malicious activity such as fake account registrations, fraudulent transactions, spam, account takeovers, and more. She has over 10 years of experience in applying big data analytics and machine learning to tackle problems in cybersecurity. Ting-Fang holds a PhD in electrical and computer engineering from Carnegie Mellon University.

Presentations

Talking to the machines: Monitoring production machine learning systems Session

Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems.

Chaoran Yu is a senior software engineer on the data systems team at Lightbend. He’s worked on technologies to handle large amounts of data in various labs and companies, including those in the finance and telecommunications sectors. He has passion for and expertise in distributed systems for big data storage, processing, and analytics. He specializes in Spark, Kafka, and Kubernetes. He’s a lifelong learner and keeps himself up to date on the fast-evolving field of data technologies.

Presentations

Hands-on machine learning with Kafka-based streaming pipelines Tutorial

Boris Lublinsky and Chaoran Yu examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more.

Greg Zaharchuk is a radiologist and professor in radiology at Stanford University and a neuroradiologist at Stanford Hospital. His research interests include deep learning applications in neuroimaging, imaging of cerebral hemodynamics with MRI and CT, noninvasive oxygenation measurement with MRI, clinical imaging of cerebrovascular disease, imaging of cervical artery dissection, MR/PET in neuroradiology, and resting-state fMRI for perfusion imaging and stroke.

Presentations

AI and deep learning enable 4x faster scans and productivity gains for clinical radiology Session

Enhao Gong and Greg Zaharchuk detail AI solutions, cleared by the FDA and powered by industry framework, that deliver 4x–10x faster MRI scans, 4x faster PET scans, and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag Hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow.

Andrew Zaldivar is a senior developer advocate for Google AI. His job is to help to bring the benefits of AI to everyone. Andrew develops, evaluates, and promotes tools and techniques that can help communities build responsible AI systems, writing posts for the Google Developers blog, and speaking at a variety of conferences. Previously, Andrew was a senior strategist in Google’s Trust and Safety Group and worked on protecting the integrity of some of Google’s key products by using machine learning to scale, optimizing, and automating abuse-fighting efforts. Andrew holds a PhD in cognitive neuroscience from the University of California, Irvine and was an Insight Data Science fellow.

Presentations

AI transparency: A brief overview of frameworks for transparent reporting of AI provenance, usage, and fairness-informed evaluation Keynote

In an effort to encourage responsible transparent and accountable practices, Andrew Zaldivar details existing frameworks technologists can use for ethical decision making in AI.

John Zedlewski is a director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.

Presentations

From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies) Session

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs.

Yi Zhang is the CTO of Rulai and a tenured professor in the computer science and engineering department at the University of California, Santa Cruz. Previously, she was a consultant or technical adviser for enterprises (HP, Toyota, Alibaba, etc.) and startups. She has more than 20 years of experience in AI with various awards, including ACM SIGIR Best Paper Award, National Science Foundation Faculty Career Award, Google Research Award, and Microsoft Research Award. She’s served as program chair, area chair, and PC member for various top-tier international conferences. She received her PhD from the Carnegie Mellon University School of Computer Science.

Presentations

Executive Briefing: 2019 chatbot predictions Session

Consumers want everything now, at their fingertips, with very little effort. To meet these demands and compete, companies need to fundamentally rethink how they operate. Yi Zhang explores some predictions on how conversational technology will evolve from its current state in 2019. She outlines some common misunderstandings about the technologies and provides case studies from several industries.

Huaixiu Zheng is a tech lead on the conversational AI team at Uber, focusing on applications and applied research of natural language processing, deep learning, and conversational AI systems. He’s led several big initiatives at Uber in using AI technologies to empower business applications in the domains of customer support, smart-reply systems, and task-oriented conversational AI systems. He received his PhD in quantum physics and quantum computation from Duke University. He’s published 30+ papers in prestigious journals such as Nature, Nature Physics, and Physical Review Letters and conferences such as KDD.

Presentations

Uber’s deep learning applications in NLP and conversational AI Session

Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems.

Huaiyu Zhu is a senior software engineer and a member of the Infrastructure for Intelligent Information Systems Group at IBM Research – Almaden. His research interests include natural language processing, machine learning and statistics, and scalable information systems. He’s worked on neural networks, information geometry, text analytics, information extraction, enterprise search, knowledge discovery, enterprise analytics platforms, and multilingual natural language processing. He holds a PhD in computational mathematics and statistics from the University of Liverpool. 

Presentations

Toward universal semantic understanding of natural languages Session

Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes.

Liren Zhu is a head of engineering at Subtle Medical, where he leads the engineering work to develop and deploy AI solutions in clinical settings. He has over ten years of experience working on engineering on advanced medical imaging both in academia and the industry, and he’s published in top journals including Nature Biomedical Engineering and Nature Communications. His work has been awarded at SPIE Photons Plus Ultrasound and featured in Fox News, PBS News Hour, Yahoo News, and Scientific American. He’s been invited to speak at NVIDIA GTC conferences and featured by AWS in the BootUP Healthcare Startup episodes. He earned his PhD from Washington University and worked on research at Caltech before joining Subtle Medical.

Presentations

Clinical deployment of radiology AI powered by OpenVINO AI in the Enterprise: The Intel® AI Builders Showcase Event

Subtle Medical develops FDA-cleared AI software products to speed up and improve the quality of radiology exams. Liren Zhu details how Subtle Medical deploys its AI software in an efficient and seamless approach to enable fast inference times powered by the Intel distribution of OpenVINO toolkit.

Shiyuan Zhu is a software engineer at Determined AI, where he helps build end-to-end product for ML/DL developers in leading organizations to efficiently realize their ideas. Previously, Shiyuan was involved in the products on machine learning, data mining, and full stack. Shiyuan holds a MSc in electrical engineering from the University of Southern California.

Presentations

Deep learning at scale: Tools and solutions Tutorial

Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.

As Director of our HPC Architecture and Performance, Matthew is responsible for defining Lenovo’s systems and solutions portofolio for both HPC and AI Deep Learning. His deep knowledge of HPC workloads and supercomputing designs enables him to be a trusted advisor to our clients and has been responsible for some of Lenovo’s most important installations. Matthew received his Bachelor of Arts in Molecular, Cellular and Developmental Biology from the University of Colorado, Boulder and went on to work in and publish leading research in plant genetics. Matthew joined IBM in 2001 and worked as a HPC architect on North America’s Advanced Technical Support team where he broadened his scope of HPC designs into other area such as Oil and Gas, Digital Media, Weather/Atmospheric Sciences and General Research. As the market for x86 based clusters continued to expand, Matthew progressed to the role of Executive Architect in the System x Product Marketing team at IBM before transitioning to Lenovo.

Presentations

Lenovo intelligent computing orchestration AI in the Enterprise: The Intel® AI Builders Showcase Event

TBD

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