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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Vijay Srinivas Agneeswaran is a senior director of technology at Publicis Sapient. Vijay has spent the last 12 years creating intellectual property and building products in the big data area at Oracle, Cognizant, and Impetus, including building PMML support into Spark/Storm and implementing several machine learning algorithms, such as LDA and random forests, over Spark. He also led a team that built a big data governance product for role-based, fine-grained access control inside of Hadoop YARN and built the first distributed deep learning framework on Spark. Earlier in his career, Vijay was a postdoctoral research fellow at the LSIR Labs within the Swiss Federal Institute of Technology, Lausanne (EPFL). He’s a senior member of the IEEE and a professional member of the ACM. He holds four full US patents and has published in leading journals and conferences, including IEEE Transactions. His research interests include distributed systems, cloud, grid, peer-to-peer computing, machine learning for big data, and other emerging technologies. Vijay holds a bachelor’s degree in computer science and engineering from SVCE, Madras University, an MS (by research) from IIT Madras, and a PhD from IIT Madras.

Presentations

Industrialized capsule networks for text analytics Session

Multilabel text classification is a problem where multiple tags or categories may be associated with text or documents, which occurs in scenarios such as news categorization and in bioinformatics. Vijay Agneeswaran and Abhishek Kumar explore how industrialized capsule networks handle spatial relationships between objects and an image and how recurrent capsule networks are useful in text analytics.

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 is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on 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 is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on 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 a director of data science 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 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 Data, 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.

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.

Carolina Barcenas is the vice president of data science and AI for data products at Visa. She’s responsible for exploring and developing advanced ways for leveraging data to create business value for Visa through artificial intelligence techniques. Carolina is also Austin’s coleader of Visa Women in Technology as well as the organizing force behind the community college intern program that focuses on nontraditional candidates. She’s worked both in industry and academia and has over 20 years of experience designing predictive analytical solutions in fintech. Previously, she worked at PayPal, where she was responsible for managing the risk of small and medium ecommerce sellers. She holds a PhD in applied statistics from the Georgia Institute of Technology as a Fulbright Scholar.

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 Chetia and Carolina Barcenas explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

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: A homomorphic encryption-based system Session

Tzvika Barenholz details 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. Take a sneak peak at some of the optimizations and tricks that make FHE practical.

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with the Howard Hughes Medical Institute (HHMI). Dylan studied physics and math at the University of Maryland and holds a PhD in physics from New York University.

Presentations

AI for managers 2-Day Training

Dylan Bargteil and Michael Li lead you through a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

Michael Bauer is a senior software engineer at Sylabs, who’s an expert in Linux container technologies. At Sylabs, he’s the lead engineer of the core services team, providing technical oversight and direction over products such as Singularity, SingularityPRO, and various Kubernetes integrations. Michael has been involved with the Singularity open source project for almost three years, first as a contributor and now as a project lead and maintainer. He’s given talks about Singularity and Linux containers around the world at conferences such as ISC, SC, FOSDEM, and many others. Recently, he’s been exploring novel approaches to machine learning via container technology.

Presentations

Distributed deep learning via containerized decoupled neural interfaces Session

Containerization technology can build distributed, scalable, and complex neural networks by leveraging decoupled resource pools—pools that would not traditionally be amenable to such a task. Using Singularity, Michael Bauer demonstrates the approach of treating a container as a decoupled neural interface (DNI) to enable novel applications for neural networks that were previously impractical.

Mayukh Bhaowal is a director of product management at Salesforce Einstein working on automated machine learning. Previously, Mayukh worked at startups in the domain of machine learning and analytics. He served as head of product of the ML platform startup Scaled Inference, backed by Khosla Ventures, and led product at the ecommerce startup Narvar, backed by Accel. He was also a principal product manager at Yahoo and Oracle. Mayukh holds a master’s degree in computer science from Stanford University.

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.

Aashish Bhateja is a senior program manager working on Microsoft Azure Machine Learning—building an exciting machine learning service that makes it easy for all data scientists and ML engineers to create and deploy robust, scalable, and highly available machine learning web services in the cloud.

Presentations

How Azure machine learning makes the data scientist’s life easier 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Sameki dive deep into how AzureML helps the data scientist to be more productive when developing TensorFlow models for production. You'll see aspects of the whole model development lifecycle from training through deployment, MLOps, and all the way to model interpretability.

Lukas Biewald is currently CEO & founder 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.

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 research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly. He holds a degree in medieval history and a PhD in computing.

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.

Dave Castillo is the managing vice president at Capital One and leads the Center for Machine Learning and Emerging Technology. In this role, Dave is responsible for driving excellence in applied ML research, university ML research, ML technologies (tools and platforms), ML consulting, and ML awareness within Capital One. He’s a strong advocate of responsible AI and has a keen interest in automated machine learning and time series ML. For more than 25 years, Dave has been involved with developing applications involving big data, artificial intelligence, machine learning, and large-scale distributed computing across a wide variety of industries. He’s spent a great deal of time in real-time ML for bidding on real-time auctions and delivering personalized advertising to online and mobile devices. He’s a promoter of analyzing data streams “in flight” to extract meaningful content and for creating and delivering model features in near real time. Dave’s also experienced in developing and deploying fully automated self-learning models. Previously, Dave has developed artificial intelligence applications for NASA, been chief software engineer for Motorola’s Iridium system, chief information officer at KAST (an AI company), chief analytics architect for Adenyo/Motricity, chief technology officer at Voltari Corporation, and chief data scientist for Early Warning. He’s founded two startups in the areas of automated machine learning for online and mobile marketing and advertising. Dave earned his bachelor’s in engineering from the University of Arizona in Tucson, a master’s in engineering from Arizona State University, and a doctorate in engineering from the University of Central Florida. He’s an active speaker and participant in industry events and an adjunct professor of computer science at the University of Maryland University College.

Presentations

Executive Briefing: Responsible AI and ML at scale across the enterprise Session

David Castillo outlines Capital One's approach to explainable AI, with a particular focus on fairness in automated decisioning. He details key best practices in implementing fair and responsible AI systems, as well as the challenges Capital One has faced along the way and the research efforts it has initiated to overcome them.

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 and Roger Chen open the second day of keynotes.

Wednesday Opening Welcome Keynote

Program Chairs, Ben Lorica 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 into 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 realms 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 Chetia and Carolina Barcenas explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

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.

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.

Neil Conway is the cofounder and CTO of Determined AI, a startup building software to make deep learning developers dramatically more productive. Previously, Neil was a technical lead at Mesosphere and earned a PhD in computer science from the University of California, Berkeley. Neil has also been a leader and major contributor to several notable open source projects, including Apache Mesos and PostgreSQL.

Presentations

Modern deep learning: Tools and techniques Tutorial

Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization.

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.

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.

Danielle Dean is a principal data scientist lead in AzureCAT within the Cloud AI Platform Division at Microsoft, where she leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Presentations

Reference architectures for AI and machine learning Session

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

Madhura Dudhgaonkar is a machine learning leader at Workday, 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.

Nicole Eagan is CEO of Darktrace, where she’s reinventing the conversation around cybersecurity. Nicole has successfully delivered Darktrace’s disruptive machine learning technology to the global market and positioned the company as an international leader in cyberdefense. An expert in developing and executing strategies for high-growth businesses, Nicole has secured Darktrace $65 million in series-C funding from KKR and led the company to 600% year-on-year growth. Under her leadership, Darktrace’s innovative approach to cybersecurity has won over 20 awards, including World Economic Forum Technology Pioneer, and Nicole was named woman of the year at the 2016 Cyber Security Awards. Her extensive career as a technology executive also includes over 25 years of commercial and marketing experience.

Presentations

How self-learning AI can understand a company from the inside out Session

While nearly every firm is impacted by a wide variety of external factors, the most robust businesses recognize the need to first learn about themselves. Nicole Eagan explores how a self-learning AI is able to learn how a company functions from the inside, and evolve with changes; this AI enables businesses to detect vulnerabilities, improve processes, and continue to grow.

Jordan Edwards is a principal program manager at Microsoft, working on machine learning frameworks and tools. He focuses on all aspects of ML ops, bringing machine learning workflows to production by augmenting existing DevOps (CI/CD) practices to account for the complexity of model training, validation, deployment and monitoring.

Presentations

How Azure machine learning makes the data scientist’s life easier 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Sameki dive deep into how AzureML helps the data scientist to be more productive when developing TensorFlow models for production. You'll see aspects of the whole model development lifecycle from training through deployment, MLOps, and all the way to model interpretability.

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 and Miro Enev detail a practical next step in DL learning with instructions, demos, and hands-on labs.

Sanji Fernando is a vice president at OptumLabs, where he leads the Center for Applied Data Science (CADS). CADS focuses on the application of new data science methods to solve complex healthcare challenges by applying breakthrough innovations in artificial intelligence and machine learning to create software product concepts. Previously, Sanji was head of data science for Nokia’s Cloud Computing Group, a cofounder and vice president of engineering of the venture-backed software company Vettro, and he began his career in consulting. He earned his bachelor’s degree in computer science from Trinity College. He lives in the Boston area with his wife and three boys. In his free time, Sanji enjoys coaching his sons in basketball and baseball.

Presentations

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

Sanji Fernando examines 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.

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 the radio antenna parameters to improve the service quality. Julien Forgeat explores a reinforcement learning (RL) approach to configure 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.

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 mindset 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.

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.

Marina Rose Geldard, more commonly known as Mars, 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) since 2017. She worked as a senior research scientist in the Artificial Intelligence and Learning Laboratory at the General Electric (GE) Global Research Center from 2006 to 2017. She received her M.S. and Ph.D. degree in Electrical Engineering: Systems from the University of Michigan, Ann Arbor, Michigan in 2002 and 2006, respectively.

Her research interests are in smart automation, robotics, predictive control and optimization, and reinforcement learning (RL). Currently, she is leading science teams in scalable autonomous driving and automation systems including consumer products such as AWS DeepRacer and Sagemaker RL. At GE Research, she led science teams on healthcare analytics, and collaborated with government organizations and research institutions to develop energy analytics for consumers and utilities.

She has more than 30 patents and 50 conference, journal, and technical report publications. She served in the organizing committees for the American Control Conference in 2014 and 2018. She served as an Associate Editor for IEEE Transactions on Automation Science and Engineering from 2015-2018. She has been serving in the industrial committee for International Federation of Automatic Control since 2018.

Presentations

Keynote by Sahika Genc Keynote

Details to come.

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.

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.

Yael Gozin is a senior director at Pfizer, where she works with clinical development teams across Pfizer’s portfolio, which raised her interests in the use of technology to improve the processes and data quality associated with clinical development and regulatory submissions. Yael provides technical guidance both in designing and implementing innovative AI solutions to automate quality processes and coaching and mentoring of project teams across different therapeutic areas including oncology, innovative pharma, established products, and consumer products. She holds a doctorate from Swiss Federal Institute of Technology, Zurich (ETH Zurich) in organic chemistry and a master’s degree from the Weizmann Institute in organometallic chemistry.

Presentations

Executive Briefing: A quality transformation initiative Session

The size and the complexity of regulatory submissions to health authorities consistently increases, ut the process hasn't changed. The process of matching and verifying a data point in a table cell with its accurate source(s) is one of the main challenges of automating data quality checks. Yael Gozin details an an innovative, highly accurate, and efficient structured data verification method.

Trevor Grant is an open source technical evangelist at IBM, a committer on the Apache Mahout, and contributor on Apache Streams (incubating), Apache Zeppelin, and Apache Flink projects. In former roles he called himself a data scientist, but the term is so over used these days. He holds an MS in applied math and an MBA from Illinois State University. Trevor’s an organizer of the newly formed Chicago Apache Flink meetup and has presented at Flink Forward, ApacheCon, Apache Big Data, and other meetups nationwide. Trevor was a combat medic in Afghanistan in 2009 and wrote an award-winning undergraduate thesis between missions. He has a dog and a cat and a ’64 Ford and he loves them all very much.

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 and Trevor Grant—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence, 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, and 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 translates this into precision oncology.

Kristian Hammond is Narrative Science’s chief scientist 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 up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the named entity recognition (NER) system at Twitter and explores the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day.

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, 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 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.

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. We aim to blend gradient-based methodology with game-theoretic goals, as part of a large "microeconomics meets machine learning" program.

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.

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’s 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 open source developer advocate at Google focusing on Apache Spark, Beam, and related big data tools. Previously, she worked at IBM, Alpine, Databricks, Google (yes, this is her second time), 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 and Trevor Grant—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 a lead engineer at Facebook. 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.

Abhishek Kumar is a senior manager of data science in Sapient’s Bangalore 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.

Presentations

Industrialized capsule networks for text analytics Session

Multilabel text classification is a problem where multiple tags or categories may be associated with text or documents, which occurs in scenarios such as news categorization and in bioinformatics. Vijay Agneeswaran and Abhishek Kumar explore how industrialized capsule networks handle spatial relationships between objects and an image and how recurrent capsule networks are useful in text analytics.

Akhilesh Kumar is a senior machine learning engineer at Adobe. He works in the applied machine learning team, which is 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 region of defectiveness 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.

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 automate hyperparameter tuning.

Li Erran Li is the chief scientist at Pony.ai and an adjunct professor at Columbia University. Previously, 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; and he worked 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.

Tianhui Michael Li is the founder and CEO of the Data Incubator, an elite fellowship program that trains and places data scientists and quants with advanced degrees (PhD or masters) into industry roles. Previously, Michael was a data science lead with Foursquare and with Andreessen Horowitz. He holds a PhD in math from Princeton University.

Presentations

AI for managers 2-Day Training

Dylan Bargteil and Michael Li lead you through a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

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: Forecasting warehouse staffing needs with LSTMs Session

Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Tianchu Liang details a warehouse staffing solution deployed in 140 distribution centers, where he implemented a long short-term memory (LSTM) recurrent neural network model to generate staffing-level forecasts and optimize staffing schedules.

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 no existing solutions 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 making 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 and Roger Chen open the second day of keynotes.

Wednesday Opening Welcome Keynote

Program Chairs, Ben Lorica 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

Learn 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 with Joseph Spisak and Hao Lu.

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. Over his career, he’s been 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

Get your hands dirty with Boris Lublinsky and Dean Wampler as they 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.

Maxim Lukiyanov is a principle program manager on the Azure HDInsight team. He works Spark and HBase.

Presentations

How Azure machine learning makes the data scientist’s life easier 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Sameki dive deep into how AzureML helps the data scientist to be more productive when developing TensorFlow models for production. You'll see aspects of the whole model development lifecycle from training through deployment, MLOps, and all the way to model interpretability.

Hagay Lupesko is part of the deep learning leadership team at Amazon Web Services and works to democratize artificial intelligence and deep learning through cloud services and open source projects such as MXNet and ONNX. He’s 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 3D cardiac imaging with real time in-vessel tracking, to semiconductors’ fab systems that measure structures the size of molecules, and up to web-scale systems with global distribution.

Presentations

Deploying and serving chatbot models at Amazon scale Session

Hagay Lupesko outlines how Lex, Amazon's cloud-based AI-powered chatbot service, was architected, built, and deployed. You'll hear about practical considerations for deploying and maintaining deep learning models in production, as well as how Lex used Apache MXNet and MXNet Model Server to build and scale the successful service.

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 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 OCR solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how generative adversarial networks (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.

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 leads the AI research lab at PARC that 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.

Raj joined PARC in September 2013 and, prior to that, he was the Director of Xerox Research Center India for two years. He also held a variety of leadership roles at Xerox Research Center Webster including manager of analytics and large-scale computing research. Raj earned his M.S. and Ph.D. in Electrical and Computer Engineering from 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. While researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications.

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.

Srinivas Narayanan leads Applied Research at Facebook AI. The team does 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 has 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. Prior to Facebook, has was founding member of two startups and was part of the database systems research group at IBM Almaden Research Center.

Presentations

Going beyond supervised learning Keynote

This session will take a deeper look into the next change we are seeing in AI - going beyond fully supervised learning techniques.

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 currently 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 UK, where he oversees the 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.

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.

Jon Peck is a developer and technical advocate at Algorithmia. As full-stack developer with two decades of industry experience, Jon now focuses on bringing scalable, discoverable, and secure machine learning microservices to developers across a wide variety of platforms via Algorithmia.com. He’s an organizer of the Seattle building intelligent applications meetup; a speaker at tech talks such as Galvanize, CodeFellows, Metis, Epicodus, and Alchemy and OpenSeattle, SeattleJS, AI Next, and DeveloperWeek; an educator at Cascadia College, Seattle C&W, and independent instruction; and a lead developer at Empower Engine, Giftstarter, Massachusetts General Hospital, and Cornell University.

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 an 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.

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 to adapt and action ethics within the business. 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 to illustrate model interpretability and explanations.

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 explores natural language processing (NLP) with deep learning, walking you through neural network architectures and NLP tasks. You'll learn how to apply these architectures for those tasks.

Alex Ratner is the cofounder and CEO of Snorkel AI, a company focused on commercializing 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 manager of data science in the data engineering organization at Trulia (Zillow Group). He has over 5 years of industry experience working in AI, deep learning, computer vision and personalization, and deploying these systems to production at scale. Shourabh and his team focus on developing data science solutions to gain a better understanding of Trulia’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.

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.

Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her PhD degree in computer science at Boston University.

Presentations

How Azure machine learning makes the data scientist’s life easier 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Sameki dive deep into how AzureML helps the data scientist to be more productive when developing TensorFlow models for production. You'll see aspects of the whole model development lifecycle from training through deployment, MLOps, and all the way to model interpretability.

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.

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 OCR solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how generative adversarial networks (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

Learn 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 with Joseph Spisak and Hao Lu.

Kenneth O. Stanley is Charles Millican Professor of Computer Science at the University of Central Florida and director there of the Evolutionary Complexity Research Group. He was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he is now also a senior research science manager and head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is 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 has 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 is a coauthor of the popular science book, “Why Greatness Cannot Be Planned: The Myth of the Objective” (published by Springer), and has spoken widely on its subject.

Presentations

Keynote by Kenneth Stanley Keynote

Details to come.

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.

Jasjeet Thind is the vice president of artificial intelligence at Zillow. His group focuses on machine learning prediction models and big data systems that power use cases such as Zestimates, personalization, housing indices, search, content recommendations, and user segmentation. Previously, Jasjeet served as director of engineering at Yahoo, where he architected a machine learning real-time big data platform, leveraging social signals for user interest signals and content prediction. The system powers personalized content on Yahoo, Yahoo Sports, and Yahoo News. Jasjeet holds a BS and master’s degree in computer science from Cornell University.

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. Jasjeet Thind walks you through how implementing computer vision based in deep neural networks allows machines to see images in an entirely new way.

Skyler Thomas is the chief architect for Kubernetes and a principal engineer at MapR, where he 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.

Wee Hyong Tok is a principal data science manager with the AI CTO office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.

Presentations

Reference architectures for AI and machine learning Session

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

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 OCR solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how generative adversarial networks (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.

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.

Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the Lightbend Fast Data Platform project, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean is the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.

Presentations

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

Get your hands dirty with Boris Lublinsky and Dean Wampler as they 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.

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 no existing solutions 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 making changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.

Haixun Wang is the vice president of engineering and distinguished scientist at WeWork. He’s an IEEE fellow and editor in chief of the IEEE Data Engineering Bulletin. Previously, he was a director of natural language processing at Amazon; led the NLP organization in Facebook working on query and document understanding; worked on natural language processing with Google Research; led research in knowledge bases, graph systems, and text processing at Microsoft Research Asia; and he was a research staff member at IBM T. J. Watson Research Center and a technical assistant to Stuart Feldman (vice president of computer science of IBM Research) and Mark Wegman (head of computer science of IBM Research). He’s published more than 200 research papers in international journals and conference proceedings. He served as PC chairs of many academic conferences, and he’s on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10-year best paper award in ICDM 2013, and best paper award of ER 2009. He earned his PhD in computer science from UCLA.

Presentations

Understanding AI for the physical world Session

The AI advancements in the cyberworld far surpass those in the physical world. Haixun Wang outlines how WeWork aims to change this by examining the approaches the company is taking to bring AI to the real world, ranging from modeling a neighborhood to creating digital twins of a building, and how AI can make businesses more efficient and improve people’s quality of life.

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 Weng is the head of data science at Mist. He has over 10 years of extensive experience applying state-of-the-art big data and data science technologies to solving challenging enterprise problems, including security, networking, and IoT. He also leads the development of Marivs—the first AI-driven virtual network assistant that automates visibility, troubleshooting, reporting, and maintenance of enterprise networking. Previously, Jisheng was the senior director of data science in the CTO office of Aruba, a Hewlett Packard Enterprise company, since its acquisition of Niara, where he had been the chief scientist and led the overall innovation and development effort in big data infrastructure and data science, and invented the industry-first modular and data-agonistic user and entity behavior analytics (UEBA) solution which is widely deployed today among global enterprises; and a technical lead in Cisco over various security products. He earned his PhD in electric engineering from Penn State University, and is a frequent speaker at different AI/ML conferences, including the O’Reilly Strata AI Conference, Frontier AI, Spark Summit, Hadoop Summit, and BlackHat.

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 shares 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 no existing solutions 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 making changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.

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.

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.

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 and Miro Enev 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. See new tools for detecting undesirable model behaviors early in large-scale online ML systems.

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.

Before joining Google AI, Andrew was a Senior Strategist in Google’s Trust & Safety group and worked on protecting the integrity of some of Google’s key products by using machine learning to scale, optimize and automate abuse-fighting efforts. Prior to joining Google, Andrew completed his Ph.D. in Cognitive Neuroscience from the University of California, Irvine and was an Insight Data Science fellow.

Presentations

Keynote by Andrew Zaldivar Keynote

Details to come.

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 data scientist at Uber, where he’s a major contributor to several ongoing efforts using deep learning-based NLP, ML, and AI technologies to empower intelligent business operations. Huaixiu has made significant contributions in the fields of quantum waveguide QED, quantum phase transition in dissipative environments, and photonic quantum computation. Previously, he was a postdoctoral researcher at Yale University, where he worked on quantum error corrections and topological quantum computation. He’s published more than 25 journal and conference papers in prestigious journals such as Nature, Nature Physics, and Physical Review Letters and has more than 1,000 citations. He received prestigious academic and industrial awards, including the Chinese Government Award for Outstanding Self-Financed Students Abroad, the John T. Chambers Scholarship, a second-place award from the SPIE-AAPM-NCI Prostate MR Classification Challenge, a second-place award for the SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge, and the second prize (as part of team Future Lifecare) of the 8th Intelligent System Summit & TEEC Cup Startup Contest. He holds a PhD in quantum physics and quantum computation from Duke University.

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.

Shelley Zhuang is the founder and managing partner at 11.2 Capital. Shelley has over 15 years of experience in technology as a software engineer, research scientist, business executive, and venture capitalist. Previously, oversaw business development and sales efforts in North America as the executive vice president of business development with Ecoplast Technologies and was actively involved in a number of investments, including Ecoplast Technologies, FeedBurner (acquired by Google for $100M), Flurry (acquired by Yahoo for $240M), PPLive (acquired by Suning for $420M), TicketsNow (acquired by Ticketmaster for $265M), Xfire (acquired by Viacom for $102M), and YeePay, as a principal at DFJ. Shelley’s a techie at heart. She’s an advisor at Skydeck and ML7 associate at Creative Destruction Lab. She also served on Enigma 2016’s program committee. Shelley holds a BS in computer science and computer engineering from the University of Missouri and a PhD in computer science from the University of California, Berkeley.

Presentations

Executive Briefing: How AI is transforming drug discovery Session

According to the 2015 Tufts Center for the Study of Drug Development, it now costs $2.6B to get a new drug to market. This figure represents the average cost of total research and development dollars spent divided by the total number of drugs approved. Shelley Zhuang explores how artificial intelligence is transforming drug discovery and development.

Yoav Zimmerman is a software engineer at Determined AI, where he works closely with leading organizations to help them apply deep learning successfully using Determined AI’s cutting-edge software. Previously, Yoav worked on knowledge representation at Google. He holds a BSc from UCLA.

Presentations

Modern deep learning: Tools and techniques Tutorial

Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization.

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