Put AI to Work
April 15-18, 2019
New York, NY

Speakers

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

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

Presentations

A software accelerator for machine learning 40-minute session

The AI industry needs new software architectures for distributed systems to solve critical problems. Vinay Rao and Santi Adavani explain why software architectures will lead the next generation of machine learning approaches and how RocketML has built logistic regression models on the KDD12 dataset with ~150 million samples on an eight-Intel Xeon-node cluster in under a minute.

Sarah Aerni is a director of data science at Salesforce Einstein, where she leads teams building AI-powered applications using autoML. Previously, she led teams in healthcare and life sciences at Pivotal, building models for customers, and she cofounded a company offering expert services in informatics to both academia and industry. Sarah holds a PhD in biomedical informatics from Stanford University, where she performed research at the interface of biomedicine and machine learning.

Presentations

Executive Briefing: Agile AI 40-minute session

How does Salesforce make data science an Agile partner to over 100,000 customers? Sarah Aerni shares the nuts and bolts of the platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology.

Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001, a PhD from IIT Madras (2008) and a post-doctoral research fellowship in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL). He currently heads data sciences R&D at Walmart Labs, India. He has spent the last eighteen years creating intellectual property and building data-based products in Industry and academia. In his current role, he heads machine learning platform development and data science foundation teams, which provide platform/intelligent services for Walmart businesses across the world. In the past, he has led the team that delivered real-time hyper-personalization for a global auto-major as well as other work for various clients across domains such as retail, banking/finance, telecom, automotive etc. He has built PMML support into Spark/Storm and realized several machine learning algorithms such as LDA, Random Forests over Spark. He led a team that designed and implemented a big data governance product for a role-based fine-grained access control inside of Hadoop YARN. He and his team have also built the first distributed deep learning framework on Spark. He is a professional member of the ACM and the IEEE (Senior) for the last 10+ years. He has five full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems, artificial intelligence as well as Big-Data and other emerging technologies.

Presentations

Industrialized capsule networks for text analytics 40-minute session

Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.

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

Presentations

AI in the Enterprise: The Intel® AI Builders Showcase Event Tutorial

Watch a series of rapid-fire demos and presentations by Intel AI Builders members that show how to quickly solve enterprise business problems by working with Intel and its partners. Then stay for a networking cocktail party following the showcase.

Intel® AI Builders Showcase closing remarks Intel® AI Builders Showcase

Brigitte Alexander closes the Intel AI Builders Showcase.

Intel® AI Builders Showcase welcome Tutorial

Brigitte Alexander, managing director of artificial intelligence partner programs at Intel, welcomes you to the Intel AI Builders Showcase. Brigitte offers an overview of the AI Builders program and discusses its impact on the AI ecosystem.

Krishna Anumalasetty is a principal program manager for Azure Machine Learning at Microsoft. He’s worked as a program and product manager at Azure cloud services and AI org for the last eight years, enabling enterprise customers with on-premises and cloud hybrid scenarios, helping them scale up and out in the cloud and implement security protections and easy to deploy ML models in the cloud. Krishna is a founding member of Microsoft’s AutoML team. He holds a master’s degree in computer science from Arizona State University.

Presentations

Forecasting financial time series with deep learning on Azure 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.

Presentations

A cognitive intelligence platform for faster and more effective data insights Intel® AI Builders Showcase

When Intel AI Builders partner Mphasis set out to build its Mphasis DeepInsights cognitive intelligence platform, the company chose Intel to help it reach its goals. Ajay Balakrishnan offers an overview of Mphasis DeepInsights, which enables enterprises to have faster and more effective access to insights from data.

Responsible for sales for AI and other engineering services and for joint go-to-market with leading technology companies

Presentations

Automated medical X-ray image segmentation and medical image classification Intel® AI Builders Showcase

Medical imaging and diagnostics involves segmentation of region of interest and classification of images for diagnostics. Sunil Baliga and Sundar Varadarajan share Wipro's medical image segmentation and diagnosis solution, which uses deep learning on Intel’s AI platform.

Satanjeev ‘Bano’ Banerjee is a machine learning engineer at Twitter working on Tweet relevance ranking. Previously he worked as a data scientist on Twitter’s Advanced Analytics team, and as a backend engineer within the consumer engineering team. He holds a PhD from CMU; his dissertation was on active learning in the context of meeting understanding and summarization. Bano’s interests outside of work include volunteering his data analysis skills at local non-profits, and training towards a marathon.

Presentations

ML at Twitter: A deep dive into Twitter's timeline 40-minute session

Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient.

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

Deep learning with TensorFlow (SOLD OUT) 2-Day Training

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications.

Deep learning with TensorFlow (SOLD OUT) Training Day 2

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications.

Rachel Bellamy is a principal research scientist and manages the Human-AI Collaboration Group at the IBM T. J. Watson Research Center, where she leads an interdisciplinary team of human-computer interaction experts, user experience designers, and user experience engineers. Previously, she worked in the Advanced Technology Group at Apple, where she conducted research on collaborative learning and led an interdisciplinary team that worked with the San Francisco Exploratorium and schools to pioneer the design, implementation, and use of media-rich collaborative learning experiences for K–12 students. She holds many patents and has published more than 70 research papers. Rachel holds a PhD in cognitive psychology from the University of Cambridge and a BS in psychology with mathematics and computer science from the University of London.

Presentations

Introducing the AI Fairness 360 toolkit Tutorial

Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

Till Bergmann is a senior data scientist at Salesforce Einstein, building platforms to make it easier to integrate machine learning into Salesforce products, with a focus on automating many of the laborious steps in the machine learning pipeline. He holds a PhD in cognitive science from the University of California, Merced, where he studied the collaboration patterns of academics using NLP techniques.

Presentations

How to train your model (and catch label leakage) 40-minute session

Label leakage is a pervasive problem in predictive modeling data, and it takes on monstrous proportions at enterprise companies, where the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann and Leah McGuire explain how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem.

Anna Bethke is a deep learning data scientist within the Artificial Intelligence Products Group at Intel, where she is the head of AI for Social Good. Her responsibilities include researching and designing fair, transparent, and accessible AI systems and establishing partnerships with social good organizations to enable their missions with Intel’s technologies and AI expertise. She’s an active member of the Intel AI Lab, developing deep learning NLP algorithms as part of the NLP Architect open source repository. Previously, she was a geospatial data scientist at MIT Lincoln Labs and Argonne National Labs and a senior data scientist at Lab41. Anna holds an MS and BS in aerospace engineering from MIT.

Presentations

Leveraging AI for social good 40-minute session

The hardware, software, and algorithms that automatically tag our images or recommend the next book to read can also improve medical diagnosis and protect our natural resources. Jack Dashwood and Anna Bethke discuss a variety of technical projects at Intel that have enabled social good organizations and provide guidance on creating or engaging in these types of projects.

Aishani Bhalla is a software engineer on the Azure Machine Learning team at Microsoft, where she’s helping people operationalize models to build intelligence into every application, accelerated on FPGAs with Project Brainwave. She holds a BS in computer science from the University at Buffalo.

Presentations

Fast (and cheap) AI accelerated on FPGAs 40-minute session

Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave.

Tammy Bilitzky is CIO at DCL, where she’s responsible for managing the company’s technology department and continuing its focus on resilient, high-quality, and innovative products while helping to grow the organization. Tammy has extensive experience in leveraging technology to deliver client value, supporting business process transformation, and managing complex, large-scale programs onshore and offshore. Tammy seeks to ensure that DCL uses the best talent and tools in the marketplace and tp implement methodologies that will consistently provide clients with superior quality. Previously, she spent three decades at Marsh and McLennan Inc. subsidiary Guy Carpenter & Co., LLC, a global leader of reinsurance and capital management, where she successfully served in numerous senior technology management roles.

Presentations

Using AI to transform high-volume, confidential, disparate data for the United States Patent Office 40-minute session

Tammy Bilitzky shares a case study that details lights-out automation and explains how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Along the way, she outlines considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units.

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

Presentations

Bringing your machine learning to production with ML Ops (sponsored by Microsoft) 40-minute session

Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.

Pradip Bose is a distinguished research staff member and manager of the Efficient and Resilient Systems Department at the IBM T. J. Watson Research Center as well as an adjunct professor at Columbia University. Pradip has been involved in the design and presilicon modeling of virtually all IBM POWER-series microprocessors, since the pioneering POWER1 (RS/6000) machine, which started as the Cheetah (and subsequently America) superscalar RISC project at IBM Research. Previously, he was the lead performance engineer for POWER3, a high-end processor development project, at IBM Austin and served as a visiting associate professor at the Indian Statistical Institute, where he worked on practical applications of knowledge-based (AI) systems. His current research interests are in high-performance computers, artificial intelligence, power- and reliability-aware microprocessor architectures, accelerator architectures, presilicon modeling, and validation. Pradip is the author or coauthor of over 100 publications (including several book chapters). He’s an IEEE fellow and a member of the IBM Academy of Technology and was the editor-in-chief of IEEE Micro from 2003 to 2006 and the chair of ACM SIGMICRO from 2011 to 2017. He’s received 25 Invention Plateau Awards and several Research Accomplishment and Outstanding Innovation Awards from IBM.

Presentations

Toward self-aware, resilient systems and ethical artificial intelligence 40-minute session

Pradip Bose details a next-generation AI research project focused on creating "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field—in particular, cognitive bias and inaccurate decisions that are perceived as being unethical.

Fidan Boylu Uz is a senior data scientist at Microsoft, where she’s responsible for successful delivery of end-to-end advanced analytic solutions. She’s also worked on a number of projects on predictive maintenance and fraud detection. Fidan has 10+ years of technical experience on data mining and business intelligence. Previously, she was a professor conducting research and teaching courses on data mining and business intelligence at the University of Connecticut. She has a number of academic publications on machine learning and optimization and their business applications and holds a PhD in decision sciences.

Presentations

Deploying deep learning models on GPU-enabled Kubernetes clusters 40-minute session

Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster.

Joanna J. Bryson is a transdisciplinary researcher on the structure and dynamics of human- and animal-like intelligence and a Reader (associate professor) at the University of Bath. Her research covers topics from artificial intelligence to autonomy and robot ethics to human cooperation and has appeared in venues ranging from Reddit to Science. She holds degrees in psychology from Chicago and Edinburgh and artificial intelligence from Edinburgh and MIT. She has additional professional research experience from Princeton, Oxford, Harvard, and Lego as well as technical experience in Chicago’s financial industry and international management consultancy.

Presentations

Maintaining human control of artificial intelligence 40-minute session

Although not a universally held goal, maintaining human-centric artificial intelligence is necessary for society’s long-term stability. Joanna Bryson discusses why this is so and explores both the technological and policy mechanisms by which it can be achieved.

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

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

Presentations

Game engines and machine learning 40-minute session

Games are wonderful contained problem spaces, making them great places to explore AI—even if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world.

Angelo A. Calvello is cofounder of Rosetta Analytics, a technology startup using proprietary deep learning techniques to create investment signals and scalable investment strategies. He’s also cofounder of Distributed Alpha LLC, a proprietary trading firm that invests in mispriced application-driven, decentralized technologies, cryptocurrencies, and blockchain tokenized assets, and founder of Impact Investment Partners, an investment consulting firm. A serial innovator and author and speaker focusing on artificial intelligence, Angelo’s unique insights into the rise of disruptive technologies and how they are reshaping investment management—new sources of return and new tools to measure and manage risk, realignment of the relationships between asset owners and asset managers, different composition and demographics of the workforce, and transformation of traditional business models—have revealed the future of beneficial investing. Angelo is the “dissident” columnist for Institutional Investor, where he had the most read opinion columns in 2017, and a former columnist for Chief Investment Officer. His column The Doctor Is In won the American Business Media’s 2016 Jesse H. Neal Award for best commentary. He has also published extensively in other publications, including Pensions & Investments, Healthcare Financial Management, and the American Indian Culture and Research Journal (UCLA). He’s the author of Environmental Alpha: Institutional Investors and Climate Change (Wiley 2009), founder and publisher of the Journal of Environmental Investing, and a member of the Chicago Quantitative Alliance. He’s also the chairman of the board of outreach with Lacrosse and Schools, a sports-based nonprofit that creates educational opportunities for at-risk youths on Chicago’s South and West Sides. He has over 25 years of experience in the institutional investment industry and holds a PhD in contemporary European philosophy from DePaul University.

Presentations

Executive Briefing: AI changes everything. . .except in investment management 40-minute session

Angelo Calvello explains why asset managers will inevitably (but slowly and haltingly) incorporate AI into their investment processes in a meaningful manner and argues that this incorporation could be accelerated by the entrance of an external AI-based actor or the success of AI-based investment startups.

Lawrence Carin has been a professor of electrical and computer engineering at Duke University for the past 22 years, and he has served as the vice provost for research at Duke for over three years. An IEEE fellow, Lawrence is one of the most widely published machine learning researchers in the world: he’s coauthored over 350 papers affecting fields as diverse as bomb detection, neuroscience, and voting behavior. He holds a BS, MS, and PhD in electrical engineering from the University of Maryland. Outside of the office, Lawrence spends every possible minute with his family and works hard to keep his body as fit as his mind.

Presentations

Executive Briefing: From cutting-edge AI research to business impact 40-minute session

Larry Carin, one of the world’s most published machine learning researchers, discusses the state of the art in machine learning and how it translates to business impact. Along the way, Larry shares examples of how modern machine learning is transforming business in several sectors, including healthcare delivery, security, and back-office business processing.

Gunnar Carlsson is a professor of mathematics (emeritus) at Stanford University and cofounder and president at Ayasdi, which is commercializing products based on machine intelligence and topological data analysis. Gunnar has spent his career devoted to the study of topology, the mathematical study of shape. Originally, his work focused on the pure aspects of the field, but in 2000 he began work on the applications of topology to the analysis of large and complex datasets, which led to a number of projects, notably a multi-university initiative funded by the Defense Advanced Research Projects Agency. He has taught at the University of Chicago, the University of California, San Diego, Princeton University, and, since 1991, Stanford University, where he has served as the chair of the Mathematics Department. He is also a founder of the ATMCS series of conferences focusing on the applications of topology and a founding editor of the Journal for Applied and Computational Topology. Gunnar is the author of over 100 academic papers and has given numerous addresses to scholarly meetings. He holds a BA in mathematics from Harvard and a PhD in mathematics from Stanford. He is married with three grown children.

Presentations

Using topological data analysis to understand, build, and improve neural networks Tutorial

Gunnar Carlsson explains how to use topological data analysis to describe the functioning and learning of a neural network in a compact and understandable way—resulting in material speedups in performance (training time and accuracy) and enabling data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all datasets.

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

Presentations

How to build privacy and security into deep learning models 40-minute session

In recent years, we've seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.

Jian Chang is a senior algorithm expert at the Alibaba Group, where he is working on cutting-edge applications of AI at the intersection of high-performance databases and the IoT, focusing on unleashing the value of spatiotemporal data. A data science expert and software system architect with expertise in machine learning and big data systems and deep domain knowledge on various vertical use cases (finance, telco, healthcare, etc.), Jian has led innovation projects and R&D activities to promote data science best practices within large organizations. He’s a frequent speaker at technology conferences, such as the O’Reilly Strata and AI Conferences, NVIDIA’s GPU Technology Conference, Hadoop Summit, DataWorks Summit, Amazon re:Invent, Global Big Data Conference, Global AI Conference, World IoT Expo, and Intel Partner Summit, and has published and presented research papers and posters at many top-tier conferences and journals, including ACM Computing Surveys, ACSAC, CEAS, EuroSec, FGCS, HiCoNS, HSCC, IEEE Systems Journal, MASHUPS, PST, SSS, TRUST, and WiVeC. He’s also served as a reviewer for many highly reputable international journals and conferences. Jian holds a PhD from the Department of Computer and Information Science (CIS) at University of Pennsylvania, under Insup Lee.

Presentations

Building an AI engine for time series data analytics 40-minute session

Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency.

Celia joined AB in April 2017 as a data scientist. She has been working with multiple teams on building machine learning models, applying natural language processing techniques and leveraging other modern data science techniques to gain business insights and integrate alternative datasets to make better and faster investment decisions.

After completing her MA in Quantitative Methods with a data science focus at Columbia University, she joined the Data Incubator, a data science fellowship program, to train on cutting-edge data science techniques and technology. She is currently pursuing a MS in Computer Science specializing in machine learning from Georgia Institute of Technology.

Presentations

Leveraging data science in asset management 40-minute session

Andrew Chin and Celia Chen offer an overview of data science applications within the asset management industry, covering use cases on using ML to derive better investment insights and improve client engagement.

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

Accelerating AI Adoption Keynote

Keynote by Ben Lorica and Roger Chen

Decentralized governance of data 40-minute session

Data remains a linchpin of success for machine learning yet too often is a scarce resource. And even when data is available, trust issues arise about the quality and ethics of collection. Roger Chen explores new models for generating and governing training data for AI applications.

Opening remarks Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the second day of keynotes.

Opening remarks and keynote Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the first day of keynotes.

Sanjian Chen is a Senior Algorithm Expert at the Alibaba Group. He has deep knowledge of large-scale machine learning algorithms. Over his career, he’s partnered with and advised leaders at several Fortune 500 companies on making data-driven strategic decisions and provided software-based data analytics consulting service to seven global firms across multiple industries, including financial services, automotive, telecommunications, and retail.

Presentations

Building an AI engine for time series data analytics 40-minute session

Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency.

Chakri Cherukuri is a senior researcher in the Quantitative Financial Research Group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies, and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.

Presentations

Applied machine learning in finance 40-minute session

Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.

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

Presentations

The evolution of software development and conversational assistants 40-minute session

We're entering a new age of software development, where humans and machines work collaboratively together, each doing what they do best. Adam Cheyer offers an overview of a freely downloadable development environment so that you can give this a try yourself and start monetizing your content and services through a new channel that will be backed by more than a billion devices in just a few years.

Andrew Y. Chin is the chief risk officer and head of quantitative research for AllianceBernstein, where he oversees all aspects of risk management to ensure that the risks being taken are well understood and appropriately managed and is responsible for the firm’s data science strategy and for optimizing the quantitative research infrastructure, tools, and resources across the firm’s investing platforms. He’s held a number of quantitative research roles at the firm, in both New York and London, including senior portfolio manager for style blend equities and director of quantitative research for value equities. Prior to joining the firm, he was a project manager and business analyst in global investment management at Bankers Trust. Andrew teaches in the School of Operations Research and Information Engineering at Cornell University and leads teams of students on capstone projects utilizing quantitative and data science skills to address investment issues. He holds a BA and an MBA from Cornell University.

Presentations

Leveraging data science in asset management 40-minute session

Andrew Chin and Celia Chen offer an overview of data science applications within the asset management industry, covering use cases on using ML to derive better investment insights and improve client engagement.

Scott Clark is a cofounder and CEO of SigOpt, providing optimization tools as a service that help experts optimally tune their machine learning models. Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Previously, he worked on the ad targeting team at Yelp, leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott was chosen as one of Forbes’s 2016 “30 under 30.” He holds a PhD in applied mathematics, an MS in computer science from Cornell University, and BS degrees in mathematics, physics, and computational physics from Oregon State University.

Presentations

Best practices for scaling modeling platforms 40-minute session

Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.

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

Deep learning for time series data 40-minute session

Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data.

Brendan Collins is a Solutions Engineer working to implement machine learning infrastructure for Algorithmia’s largest enterprise customers. Previously, he held a similar position at Synology. He has worked in financial enterprise infrastructure for more than 10 years, with groups ranging in size from the largest financial institutions in the world to community banks. Brendan has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems.

Presentations

Designing a machine learning operating platform 40-minute session

Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science.

Simon Crosby is CTO at SWIM.AI, an edge intelligence software vendor that focuses on edge-based learning for fast data. He cofounded Bromium in 2010 and now serves as a strategic advisor. Previously, he was the CTO of the Data Center and Cloud Division at Citrix Systems; founder, CTO, and vice president of strategy and corporate development at XenSource; and a principal engineer at Intel, as well as a faculty member at Cambridge University, where he led the research on network performance and control and multimedia operating systems. Simon is an equity partner at DCVC, serves on the board of Cambridge in America, and is an investor in and advisor to numerous startups. He’s the author of 35 research papers and patents on a number of data center and networking topics, including security, network and server virtualization, and resource optimization and performance. He holds a PhD in computer science from the University of Cambridge, an MSc from the University of Stellenbosch, South Africa, and a BSc (with honors) in computer science and mathematics from the University of Cape Town, South Africa.

Presentations

Turn devices into data scientists—at the edge 40-minute session

Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head.

Alexis Crowell Helzer is senior director of artificial intelligence product marketing at Intel, where she and her team are responsible for technical positioning and messaging as well as outbound content and campaigns for Intel AI products. Alexis and her team partner with AI adopters across the industry from small device implementations to HPC clusters to launch products, showcase innovative use cases, and help other companies find their own AI path. She has an unyielding passion to deliver technology solutions that help businesses thrive. Over her rich career, she has run a cloud software engineering team focused on distributed computing and microservices integration, led the open source marketing efforts from Intel, and worked with many of the Fortune 100 companies to help incubate service offerings and deliver innovative products.

Presentations

How leaders are tackling their most pressing AI challenges (sponsored by SAS) 40-minute session

Drawing on case studies and recent survey insights, Tom Roehm and Alexis Crowell Helzer offer a front-row view into how companies are taking on everything from trust in AI to its impact on jobs, oversight, and ethics.

Opening remarks Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the second day of keynotes.

Opening remarks and keynote Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the first day of keynotes.

Nick Curcuru is vice president of enterprise information management at Mastercard, where he’s responsible for leading a team that works with organizations to generate revenue through smart data, architect next-generation technology platforms, and protect data assets from cyberattacks by leveraging Mastercard’s information technology and information security resources and creating peer-to-peer collaboration with their clients. Nick brings over 20 years of global experience successfully delivering large-scale advanced analytics initiatives for such companies as the Walt Disney Company, Capital One, Home Depot, Burlington Northern Railroad, Merrill Lynch, Nordea Bank, and GE. He frequently speaks on big data trends and data security strategy at conferences and symposiums, has published several articles on security, revenue management, and data security, and has contributed to several books on the topic of data and analytics.

Presentations

Artificial Intelligence - the “refinery” for Data (Sponsored by Dell Technologies) Keynote

Nick Curcuru, VP, Data Analytics and Cyber Security, will discuss Mastercard’s commitment to AI and its recent investments and developments.

Deployment considerations and best practices for your AI workloads from Mastercard (sponsored by Dell Technologies) 40-minute session

There are many different decisions to make when choosing the right solutions and infrastructure. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—Mastercard exercised to build and deploy a successful AI.

Morten Dahl is cofounder and research scientist at Dropout Labs, a startup building a platform for secure, privacy-preserving machine learning to manage the sensitive, competitive, and regulatory nature of data. He is also lead developer on TF Encrypted, an open source project for integrating and experimenting with privacy-preserving machine learning directly in TensorFlow. With a background in cryptography and privacy, Morten has spent recent years applying and adapting techniques from these fields to machine learning, focusing on practical tools and concrete applications in hope of making the field more accessible to practitioners.

Presentations

Privacy-preserving machine learning in TensorFlow with TF Encrypted 40-minute session

Morten Dahl reviews modern cryptographic techniques such as homomorphic encryption and multiparty computation, sharing concrete examples in TensorFlow using the open source library TF Encrypted. Join in to learn how to get started with privacy-preserving techniques today, without needing to master the cryptography.

Jack Dashwood is a product marketing manager at Intel, which he joined as part of the company’s acquisition of Movidius in 2016; he continues to work in the Movidius Division of Intel’s Internet of Things Group. Previously, Jack worked in the field of augmented reality and commercial finance. He holds a BS, where he studied the complexities of the human vision system and cognitive processes, as well as a degree in finance and a master’s degree in global business from the University of Victoria.

Presentations

Leveraging AI for social good 40-minute session

The hardware, software, and algorithms that automatically tag our images or recommend the next book to read can also improve medical diagnosis and protect our natural resources. Jack Dashwood and Anna Bethke discuss a variety of technical projects at Intel that have enabled social good organizations and provide guidance on creating or engaging in these types of projects.

Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.

Presentations

Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft Azure) Keynote

Automated ML is at the forefront of Microsoft’s push to make Azure ML an end-to-end solution for anyone who wants to build and train models that make predictions from data and then deploy them anywhere. Join Danielle Dean for a surprising conversation about a data scientist’s dilemma, a researcher’s ingenuity, and how cloud, data, and AI came together to help build automated ML.

Fabrizio Del Maffeo is managing director of AAEON Technology Europe, a leading company in industrial AI on the edge, the IoT, and machine vision. He’s the founder of UP Bridge the Gap, an Intel reference platform for machine vision and IoT use cases.

Presentations

AI on the edge Intel® AI Builders Showcase

AAEON brings AI on the edge to resolve challenges developers face when they try to bring all AI tasks to the cloud, such as latency, network bandwidth, reliability, and security. Fabrizio Del Maffeo explains why UP AI Edge is the solution for cloud limitations by bringing AI performance and hardware acceleration not "at" but "on" the edge of the internet of things and the latest technology.

Zongjie, aka ”Z”, is currently a Director of Product Strategy at Cisco’s Data Center Compute product group (DCC). DCC provides world leading compute solutions to enterprise and SMB customers. DCC’s leading products include Unified Compute Systems (UCS), Cisco Converged Infrastructure (FlexPod, FlashStack, VBlock, etc.) and Cisco’s own HyperConverged solution – HyperFlex.
Z is known to be the “Intrapreneur”, who helps drive Cisco compute core product strategy, as well as identifying adjacent and new market opportunities to drive business growth. She also leads the Product Management team for Cisco’s AI/ML infrastructure products, aiming at helping Cisco’s customers deploy AI/ML in both on-prem and multi-cloud environment.
Prior to Cisco, Z worked at McKinsey & Company as an Engagement Manager. She was appointed to be the first Engagement Manager at McKinsey’s Fast Growth Tech Practice, specialized in serving start-ups. Prior to McKinsey, she spent 6 years in Singapore as a Product Engineer at Avago and a Supply Chain consultant at iCognitive.

Presentations

Deploy machine learning for real impact: Bridge the gap between data scientists and IT (sponsored by Cisco) 40-minute session

Zongjie Diao outlines the key ML challenges deployment companies face and examines the root causes. More importantly, Zongjie explores solutions in depth to demystify ML development in the enterprise.

With over twenty years in the IT industry, Anthony Dina serves as the North America Director of Data Analytics at Dell EMC. He leads a team of solutions architects that synthesize the tsunami of new data
types (machine, application, person-driven) with traditional systems of record. Their expertise in big data, data warehouse modernization and analytic modeling, artificial intelligence helps customers
succeed in the era of Digital Transformation. This work not only involves intellectual property from Dell EMC but also from partners like Cloudera, HortonWorks, Splunk, Intel, Nvidia and SAP. Prior to this, he
served as executive director of strategy and director of solutions marketing. He has earned a Masters of Business Administration from the University of St. Thomas and a Masters of Fine Art from Cranbrook
Academy of Art. His technical certifications include ITIL v3 Foundation and ITIL Services Strategy.

Presentations

Deployment considerations and best practices for your AI workloads from Mastercard (sponsored by Dell Technologies) 40-minute session

There are many different decisions to make when choosing the right solutions and infrastructure. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—Mastercard exercised to build and deploy a successful AI.

Yu Dong is a senior technical product manager at Facebook, where he works on the company’s AI/ML platform, FBLearner, which enables more personalized and smarter products. Previously, he was senior software engineer manager at HPE and Cisco. He holds a PhD in computer engineering and an MBA from the University of California, Berkeley. His passion is to democratize AI across various industries by building a performant, reliable, efficient, resilient, and easy-to-use AI platform.

Presentations

Building a production-scale ML platform 40-minute session

Yu Dong offers an overview of the why, what, and how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions.

Michael Eagan has 18 years of Project Management experience providing leadership for consulting clients on developmental and organizational issues.

As Vice President Product Development at Korn Ferry, the preeminent global people and organizational advisory firm, Eagan is responsible for enabling new and existing suites of products to operate in a cloud-based SaaS environment and driving excellence in the company’s fastest growing business segment. Prior to his work at Korn Ferry, he served in multiple roles at Aon Hewitt, most recently as Senior Director HR Services Workday.

Eagan is based in Charlotte, North Carolina.

Presentations

Executive Briefing: From cutting-edge AI research to business impact 40-minute session

Larry Carin, one of the world’s most published machine learning researchers, discusses the state of the art in machine learning and how it translates to business impact. Along the way, Larry shares examples of how modern machine learning is transforming business in several sectors, including healthcare delivery, security, and back-office business processing.

Rob Earhart is a deep learning software engineer in the Artificial Intelligence Products Group at Intel, where he works on PlaidML, an open source polyhedral tensor compiler that makes it pretty easy to run neural networks with good performance on a wide variety of hardware. Prior to Intel (and prior to diving into machine learning systems implementation), Rob worked on the NT kernel, was one of the original Hyper-V hypervisor engineers, and cofounded the virtual machine monitor that grew up to power Google Compute Engine.

Presentations

nGraph: Unlocking next-generation performance with deep learning compilers 40-minute session

The rapid growth of deep learning in demanding large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. Adam Straw, Adam Procter, and Robert Earhart offer a comprehensive overview of Intel's nGraph deep learning compiler.

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: Fear and loathing in explainability and transparency—A savage journey to the heart of AI 40-minute session

Jana Eggers explores explainability and transparency as both required and unachievable goals for AI, with a focus on helping teams structure discussions about levels of explainability possible and needed for both user trust and regulatory requirements.

Yoav Einav is vice president of product at GigaSpaces, where he drives product management, technology vision, and go-to-market activities. Yoav has more than 12 years of industry experience in product management and software engineering at high-growth software companies. Previously, Yoav held product management roles at Iguazio and Qwilt, mapping the product strategy and roadmap while providing technical leadership regarding architecture and implementation. An entrepreneur at heart, Yoav drives innovation and product excellence and successfully incorporates it with the market trends and business needs. He holds a BSC (magna cum laude) in computer science and business from Tel Aviv University and an MBA in finance from the Leon Recanati School in Tel Aviv University.

Presentations

Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory Intel® AI Builders Showcase

Yoav Einav and Vin Costello explain how to achieve faster analytical processing, leveraging in-memory performance for the cost of flash with persistent memory (~300% faster than SSD); smarter insights at optimized TCO, scaling the speed layer capacity for smarter real-time analytics with 7x lower footprint); and Agile automation of your ML and DL model CI/CD pipeline, for faster time to market.

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 give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs.

Mohamed Fawzy is senior manager and tech lead at Facebook. In his six years at the company, he’s worked on its distributed storage system and was part of the team that developed cold storage, Facebook’s exabyte archiver storage system that keeps your memories safe. More recently, he started the Distributed Training Group to build large-scale distributed training infrastructure for deep learning.

Presentations

Distributed AI at scale 40-minute session

Session with Mohamed Fawzy

Neil Fendley is a computer vision researcher at JHU/APL, where he works on machine perception and reasoning, focusing on deep learning. Over the last two years, Neil has worked on automated retinal disease diagnosis and satellite imagery classification through JHU/APL’s support of IARPA’s functional Map of the World effort. Recently, Neil has focused on adversarial machine learning and hosted a JHU/APL internal challenge to design and defend against adversarial perturbations.

Presentations

ImageNet for satellite imagery: Opportunities and risks 40-minute session

While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. Ryan Mukherjee and Neil Fendley offer an overview of functional Map of the World (fMoW), an ImageNet for satellite imagery built to address this issue, and explain how you can attack or defend these deep learning models.

Sheldon Fernandez is CEO of DarwinAI. A seasoned executive and respected thought leader in the technical and enterprise communities, Sheldon has coupled his entrepreneurial endeavors with nontechnical pursuits throughout his career, resulting in an interdisciplinary approach that is critical to the intelligent application of AI. Sheldon is also an accomplished author and speaker at spaces including Singularity University, the prestigious think tank in the Bay Area, and he has written technical books and articles on many topics including both artificial intelligence and computational creativity. He holds a master’s degree in theology from the University of Toronto, with thesis work in neuroscience and metaethics, and also pursued creative writing at Oxford University.

Presentations

Ethical AI: Separating fact from fad 40-minute session

Sheldon Fernandez draws on his degrees in both engineering and theology to separate fact from fad in ensuring that artificial systems behave ethically.

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Presentations

Executive Briefing: Quantum machine learning 40-minute session

Quantum computers will enable us to efficiently compute things never thought possible, but how will this impact artificial intelligence? Jennifer Fernick explains how to filter signal from noise in discussions surrounding quantum machine learning by exploring how quantum computers work, what types of AI problems they may be good at, and which industries and use cases will (and won't) benefit.

Marina Rose Geldard (Mars) is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. 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) as well as the AUC. She’s writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.

Presentations

Game engines and machine learning 40-minute session

Games are wonderful contained problem spaces, making them great places to explore AI—even if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world.

Lise Getoor is a professor in the Computer Science Department at the University of California, Santa Cruz, and director of the UCSC Data Science Research Center. Her research areas include machine learning, data integration, and reasoning under uncertainty, with an emphasis on graph and network data. Previously, she was a professor in the Computer Science Department at the University of Maryland, College Park. Lise has over 250 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a fellow of the Association for Artificial Intelligence and an elected board member of the International Machine Learning Society; she serves on the board of the Computing Research Association (CRA) and was cochair for ICML 2011. Lise is a recipient of an NSF Career Award and 12 best paper and best student paper awards. She holds a PhD from Stanford University, an MS from UC Berkeley, and a BS from UC Santa Barbara.

Presentations

The unreasonable effectiveness of structure 40-minute session

Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. Lise Getoor discusses AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Along the way, Lise explores the benefit of utilizing structure—and the inherent risk of ignoring structure.

Bruno Gonçalves is currently a Senior Data Scientist working at the intersection of Data Science and Finance. Previously, he was a Data Science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since completing his PhD in the Physics of Complex Systems in 2008 he has been pursuing the use of Data Science and Machine Learning to study Human Behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading. In 2015 he was awarded the Complex Systems Society’s 2015 Junior Scientific Award for “outstanding contributions in Complex Systems Science” and in 2018 is was named a Science Fellow of the Institute for Scientific Interchange in Turin, Italy.

Presentations

Recurrent neural networks for time series analysis Tutorial

Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

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

How to use AI to improve efficiency, safety, and patient satisfaction in radiology 40-minute session

Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals.

Josh Gordon is a developer advocate at Google AI and teaches applied deep learning at Columbia University and machine learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter as @random_forests.

Presentations

TensorFlow 2.0: Machine learning for you 40-minute session

Josh Gordon shares the very latest in TensorFlow, focusing on TensorFlow 2.0 and its easy-to-use eager execution. Josh also covers how to use TensorFlow's revised high-level API and details pitfalls and tricks to get better performance on accelerator hardware.

Desirée Gosby is vice president of identity and profile at Intuit, where she leads a team of data and software engineers to architect and develop all aspects of identity for 50M Intuit customers. Desi began her 10-year career at Intuit as a senior engineer and has continued to take on bigger roles and responsibilities—including as a founding member of the company’s Innovation and Advanced Technology Group. Prior to her current role, she served as director of product development for Intuit’s Emerging Services Group, where her team built shared infrastructure to deliver personalized mobile, image data extraction (OCR), and search experiences used throughout Intuit’s mobile and online products, including TurboTax, QuickBooks, and Mint. She holds six patents. Desi received Intuit’s Scott Cook Innovation award in 2009 for Intuit’s first mobile application GoPayment.

Presentations

Teaching a computer to read Keynote

Desi Gosby dedicated years to developing technology that applies advanced machine learning capabilities to translate images and characters into an easy-to-use digital experience. Desi shares unique technical challenges faced and lessons learned while applying computer vision to seeing and reading complex financial documents, as well as what is next for the future of computer vision.

Sean Gourley is founder and CEO of Primer. Previously, he was cofounder and CTO of augmented intelligence company Quid. He sits on the board of directors at Anadarko and is a TED fellow. Sean holds a PhD in physics from Oxford, where his research as a Rhodes Scholar focused on complex systems and the mathematical patterns underlying modern war.

Presentations

Computational Propaganda Keynote

Keynote by Sean Gourley

Pankaj Goyal is the vice president of artificial intelligence strategy at HPE, where he’s focused on helping enterprises accelerate their digital transformation journeys through the power of AI. He’s passionate about the potential of AI to improve human lives. A computer science engineer by training, Pankaj built his first NLP algorithms in his undergrad days. Previously, he was a technology entrepreneur and a technology consultant at McKinsey & Company. He holds a bachelor’s degree in computer science from IIT Kanpur, India, and an MBA from IIM Bangalore, India.

Presentations

Unlock your data with AI (sponsored by HPE) 40-minute session

Regardless of your AI, ML, or DL needs, HPE has the best-in-class people, technology, and partners to ensure you’re ready for the projects and challenges of today and tomorrow. Join Pankaj Goyal to hear about HPE’s latest AI offerings and discover how HPE can help you unlock your data with AI.

Matt Greenwood is the chief innovation officer at Two Sigma Investments, where he has led company-wide efforts across both engineering and modeling teams. Matt oversees development of BeakerX, which extends the Jupyter Notebook to support to six languages, additional widgets, and one-click publication. Matt is also a board member and Venture Partner at Two Sigma Ventures and works closely with portfolio companies in both board membership and advisory capacities. Matt began his career at Bell Labs and later moved to IBM Research, where he was responsible for early efforts in tablet computing and distributed computing. Matt was also lead developer and manager at Entrisphere, where he helped create a product providing access equipment for broadband service providers. Matt holds a PhD in mathematics from Columbia University, where he taught for many years, as well as a BA and MA in math from Oxford University and an MA in theoretical physics from the Weizmann Institute of Science in Israel.

Presentations

Best practices for scaling modeling platforms 40-minute session

Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.

Anna R. Gressel is a litigation associate at Debevoise & Plimpton LLP, where she’s a member of the firm’s Technology, Media, and Telecommunications Group. Her practice focuses on complex civil litigation, corporate governance, and intellectual property, and she actively advises on issues related to emerging technologies. She also maintains a pro bono practice that includes federal civil rights litigation and indigent criminal defense. Anna is the coauthor of “Storm Clouds or Silver Linings? Assessing the Impact of the US CLOUD Act on Cross-Border Criminal Investigations” with Frederick T. Davis, “Key Insights from the New York AI in Finance Summit,” “Report of the Investigation,” and “Do the Apps Have Ears? Cross-Device Tracking” with Jeremy Feigelson. She’s a member of the Bar of New York and is also admitted to practice before the US Courts of Appeals for the Second and Third Circuits and the US District Courts for the Southern and Eastern Districts of New York. Anna is a member of the Harvard Law School Women’s Alliance, the Federal Bar Council, and the Association of the Bar of the City of New York. She holds a JD from Harvard Law School, where she twice served as teaching assistant for the school’s flagship Negotiation Workshop course and the Harvard Negotiation Institute’s executive education programs. She also holds a BA from Pomona College and was the recipient of a Fulbright Research Fellowship to Morocco.

Presentations

Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019 40-minute session

Anna Gressel, Jim Pastore, and Anwesa Paul lead a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and the California Consumer Privacy Act. They also explore key lawsuits challenging AI in US courts and unpack the implications for companies going forward, helping you mitigate legal and regulatory risks and position your AI products for success.

Presentations

Predicting credit card payment default using H2O Driverless AI Intel® AI Builders Showcase

H2O Driverless AI employs the techniques of expert data scientists in an easy-to-use application that helps scale your data science efforts. Eric Gudgion explores its capabilities by predicting credit card payment default using a Taiwanese bank's dataset from a Kaggle experiment.

Cibele Montez Halasz is a machine learning engineer at Twitter Cortex, where she helps to build Twitter’s deep learning platform. Previously, Cibele was a data scientist and systems design engineer at Apple and a product applications engineer at Analog Devices, where she built machine learning algorithms that use smartphone sensors to understand a person’s behavior. Cibele holds an MS in electrical engineering with an emphasis on computer vision and machine learning from the California Institute of Technology and a BS in electrical engineering and physics from Stanford University.

Presentations

ML at Twitter: A deep dive into Twitter's timeline 40-minute session

Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient.

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

Presentations

Bringing AI into the enterprise Tutorial

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

Kevin Han is a business consultant and service planner at Naver/LINE, a Korean company known for the biggest domestic web portal (Naver), mobile messenger (LINE), and AI-related solutions (Clova). His team provides an end-to-end AI service for clients, from improving dialogue models to consulting clients to create maximum value out of the company’s chatbot service. Previously, Kevin was a cognitive consultant at IBM. He holds a bachelor’s degree in psychology and business from New York University.

Presentations

AutoML in the Chatbot Builder Framework 40-minute session

Jaewon Lee and Sihyeung Han walk you through implementing a self-trained dialogue model using AutoML and the Chatbot Builder Framework. You'll discover the value of AutoML, which allows you to provide better model, and learn how AutoML can be applied in different areas of NLP, not just for chatbots.

Behrooz Hashemian is Vice President of Artificial Intelligence at VideaHealth. Previously, he was lead machine learning scientist at the MGH & BWH Center for Clinical Data Science, where he was responsible for developing and implementing state-of-the-art machine learning models to address various clinical use cases by leveraging medical imaging data, clinical time series data, and electronic health records, and chief data officer at the MIT Senseable City Lab, where he focused on innovative implementation of big data analytics and artificial intelligence in smart cities.

Presentations

Interpretable deep learning in healthcare 40-minute session

Artificial intelligence has shown great potential to revolutionize clinical medicine and healthcare delivery. However, incorporating these algorithms into clinical workflows involves a big challenge: convincing clinicians and regulators to trust a “black box” solution. Behrooz Hashemian explains how he's helping make deep neural networks interpretable to provide evidence for clinical decisions.

Vishal “Vish” Hawa is a principal data scientist at Vanguard, where he works closely with marketing managers to design attribution, propensity, and attrition modeling. Vish has over 15 years of experience in the retail and financial services industries. He has training in executive management from the Wharton School and holds postgraduate degrees in information sciences, statistics, and computer engineering from the Indian Statistical Institute.

Presentations

Regularization of RNNs through Bayesian networks 40-minute session

While deep learning has shown significant promise for model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and overfit. Vishal Hawa explains how a combination of RNNs and Bayesian networks (PGM) can improve the sequence modeling behavior of RNNs.

Kim Hazelwood is a senior engineering manager leading the AI Infrastructure Foundation and AI Infrastructure Research efforts at Facebook, where the focus is designing and optimizing efficiency hardware and software systems for Facebook’s many applied machine learning-based products and services. Previously, Kim was a tenured associate professor at the University of Virginia, a software engineer at Google, and director of systems research at Yahoo Labs. She’s been recognized with an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, and the ACM SIGPLAN 10-Year Test of Time Award. She serves on the board of directors of CRA and has authored over 50 conference papers and one book. She holds a PhD in computer science from Harvard University.

Presentations

Applied machine learning at Facebook Keynote

Applied Machine Learning at Facebook

Kevin He is the founder of DeepMotion. Kevin has spent his career pushing the boundaries of gaming and engineering. Previously, Kevin was the CTO of Disney’s midcore mobile game studio, technical director of ROBLOX, senior developer of World of Warcraft at Blizzard, and a technical lead at Airespace (now Cisco Systems). He has 16 years of engineering and management experience with multiple AAA titles shipped, including World of Warcraft, StarCraft II, Star Wars Commander, and ROBLOX.

Presentations

Using AI to create interactive digital actors 40-minute session

Digital character interaction is hard to fake, whether it’s between two characters, between users and characters, or between a character and its environment. Nevertheless, interaction is central to building immersive XR experiences, robotic simulation, and user-driven entertainment. Kevin He explains how to use physical simulation and machine learning to create interactive character technology.

Martial Hebert is the director of Carnegie Mellon University’s Robotics Institute, a department in the School of Computer Science. A leading researcher in computer vision and robotics, Martial joined the faculty of the Robotics Institute in 1984, just five years after its founding. The Robotics Institute has since grown into the world’s largest robotics academic robotics research center, with an annual expenditures of over $85 million. Martial played a role in such high-profile projects as a pioneering program for self-driving vehicles. His research interests include computer vision, especially recognition in images and video data; model building and object recognition from 3D data; and perception for mobile robots and intelligent vehicles. In the area of machine perception for robotics, his group has developed techniques for people detection, tracking and prediction, and for understanding the environment of ground vehicles from sensor data. Martial has served on the editorial boards of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the International Journal of Computer Vision, for which he serves as editor-in-chief. He holds a PhD in computer science from the University of Paris.

Presentations

AI and the robotics revolution Keynote

Martial Hebert offers a brief overview of current challenges in AI for robotics and a glimpse of the exciting developments emerging in current research.

Thomas Henson is a data engineering advocate and senior systems engineer for the unstructured data solutions team at Dell EMC. Thomas has been involved in many different big data, analytics and artificial intelligence projects throughout his career with a focus on distributed systems. He’s a proud alumnus of the University of North Alabama, where he earned his undergraduate and graduate degree in computer information systems. Thomas is an accomplished speaker in the artificial intelligence and big data ecosystem at various conferences.

Presentations

Michael Hind is a distinguished research staff member in the IBM Research AI organization. His current research passion is in the general of area of trusted AI, focusing on the fairness, explainability, and reliability of the construction of AI systems. Previously, he led departments of dozens of researchers focusing on programming languages, software engineering, cloud computing, and tools for cognitive systems. Michael’s team has successfully transferred technology to various parts of IBM and launched several successful open source projects. Previously, Michael spent seven years as an assistant/associate professor of computer science at SUNY New Paltz. Michael is an ACM Distinguished Scientist, a member of IBM’s Academy of Technology, and a former associate editor of ACM TACO. He has served on over 30 program committees, given talks at top universities and conferences, and coauthored over 40 publications. His 2000 paper on adaptive optimization was recognized as the OOPSLA’00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012. He holds a PhD from NYU.

Presentations

Introducing the AI Fairness 360 toolkit Tutorial

Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

Kyle Hoback is director of market enablement at WorkFusion, where he’s responsible for partner enablement, sales enablement, and software demonstration, working within the product team but closely tied to the sales, marketing, and professional services teams as well. Kyle has nearly 15 years of consulting and software experience. Previously, he was a consultant with AT Kearney and Booz Allen Hamilton. He’s passionate about helping organizations efficiently collect and utilize the data, information, and knowledge of their organizations. He holds an MSc in information systems from the London School of Economics and a BS in computer science from the University of Nebraska at Omaha.

Presentations

Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA 40-minute session

Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant anti-money laundering (AML) and "know your customer" (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. Kyle Hoback walks you through a series of case studies that utilize AI-powered RPA that address AML and KYC.

Ana Hocevar is a data scientist in residence at the Data Incubator, where she combines her love for coding and teaching. Ana has more than a decade of experience in physics and neuroscience research and over five years of teaching experience. Previously, she was a postdoctoral fellow at the Rockefeller University, where she worked on developing and implementing an underwater touch screen for dolphins. She holds a PhD in physics.

Presentations

Deep learning with PyTorch 2-Day Training

PyTorch is a machine learning library for Python that allows users 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. Ana Hocevar introduces the PyTorch workflow and demonstrates how to use it to build deep learning models using real-world datasets.

Deep learning with PyTorch (Day 2) Training Day 2

PyTorch is a machine learning library for Python that allows users 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. Ana Hocevar introduces the PyTorch workflow and demonstrates how to use it to build deep learning models using real-world datasets.

Garrett Hoffman is a director of data science at StockTwits, where he leads efforts to use data science and machine learning to understand social dynamics and develop research and discovery tools that are used by a network of over one million investors. Garrett has a technical background in math and computer science but gets most excited about approaching data problems from a people-first perspective—using what we know or can learn about complex systems to drive optimal decisions, experiences, and outcomes.

Presentations

Deep learning methods for natural language processing Tutorial

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.

Carlos Humberto Morales is the senior director of deep learning systems at Intel, where he obsesses over making AI development easier and more available. Previously, Carlos was Nervana’s platform architect and an architect for Cisco Systems, where he focused on topics such as cross-platform infrastructure security and fog computing. Over the span of his career, Carlos has been fortunate enough to be involved with the development of software and hardware at just about every level, from microcode to data center-scale orchestration suites.

Presentations

Making real-world distributed deep learning easy with Nauta Keynote

Carlos Humberto Morales offers an overview of Nauta, a new open source multiuser platform that allows teams of data scientists to run complex deep learning models on shared hardware resources.

Hamel Husain is a senior data scientist at GitHub, where he’s focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Previously, Hamel was a data scientist at Airbnb, where he worked on growth marketing, and at DataRobot, where he helped build automated machine learning tools for data scientists.

Presentations

Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues 40-minute session

Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.

Magnus Hyttsten is a developer advocate for TensorFlow at Google, where he works on developing the TensorFlow product. A developer fanatic, Magnus is an appreciated speaker at major industry events such as Google I/O, the AI Summit, AI Conference, ODSC, GTC, QCon, and others on machine learning and mobile development. Right now, he’s focusing on reinforcement learning models and making model inference effective on mobile.

Presentations

Distributed TensorFlow with distribution strategies 40-minute session

Magnus Hyttsten explains how to use TensorFlow effectively in a distributed manner using best practices. Magnus covers using TensorFlow's new DistributionStrategies to get easy high-performance training with Keras models (and custom models) on multi-GPU setups as well as multinode training on clusters with accelerators.

Humayun Irshad is a lead scientist in machine learning and computer vision at Figure Eight, where he’s developing deep learning frameworks for various applications like object detection, segmentation, and classification for medical, retail, and self-driving car use cases. Previously, he was a postdoc at Harvard Medical School, where he developed machine learning and deep learning frameworks, including region of interest detection and classification, nuclei and gland detection, and segmentation and classification in 2D and 3D medical images.

Presentations

An active learning framework to optimize training of deep models with human in the loop 40-minute session

Humayun Irshad offers an overview of an active learning framework that uses a crowdsourcing approach to solve parking sign recognition—a real-world problem in transportation and autonomous driving for which a large amount of unlabeled data is available. The solution generates an accurate model, quickly and cost-effectively, despite the unevenness of the data.

Maryam Jahanshahi is a research scientist at TapRecruit, a platform that uses AI and automation tools to bring efficiency and fairness to the recruiting process. She holds a PhD from the Icahn School of Medicine at Mount Sinai, where she studied molecular regulators of organ-size control. Maryam’s long-term research goal is to reduce bias in decision making by using a combination of computation linguistics, machine learning, and behavioral economics methods.

Presentations

Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills 40-minute session

Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.

Tony Jebara is director of machine learning at Netflix and professor on leave from Columbia University. He has published over 100 peer-reviewed papers in leading conferences and journals across machine learning, computer vision, social networks, and recommendation and is the author of the book Machine Learning: Discriminative and Generative. His work has been recognized with best paper awards from the International Conference on Machine Learning and from the Pattern Recognition Society, the Career award from the National Science Foundation, and faculty awards from Google, Yahoo, and IBM. He has cofounded and advised multiple startup companies in the domain of artificial intelligence and served as general chair and program chair for the International Conference on Machine Learning. He holds a PhD from MIT.

Presentations

Machine learning for personalization Keynote

For many years, the main goal of the Netflix recommendation system has been to get the right titles in front of each member at the right time. Tony Jebara details the approaches Netflix uses to recommend titles to users and discusses how the company is working on integrating causality and fairness into many of its machine learning and personalization systems.

Presentations

How Anaconda Enterprise enables CPU-optimized TensorFlow models Intel® AI Builders Showcase

Anaconda Enterprise combines core AI technologies, governance, and cloud-native architecture to enable organizations to automate AI at speed and scale. Rachel Jordan takes you through how to leverage Anaconda Enterprise across the deep learning lifecycle, from model building using TensorFlow to deployment on CPUs, without sacrificing speed.

Patrick Kaifosh is the cofounder and chief science officer at CTRL-labs, a venture-backed startup developing noninvasive neural interfaces. Patrick completed his graduate studies under the supervision of Larry Abbott and Attila Losonczy at Columbia University, where he studied circuit-level mechanisms of learning and memory in hippocampal and cerebellar memory systems and led the development of a widely used open source software package for analysis of calcium imaging data.

Presentations

Sooner than you think: Neural interfaces are finally here 40-minute session

Following the launch of CTRL-labs’s developer kit, CTRL-kit (neural interface device), Patrick Kaifosh paints a picture of a world with neural interfaces, explaining how this technology will change our lives. Patrick outlines a future where we'll be looking up at the world instead of down at our phones—and leads a live demo of CTRL-kit in action.

Swara Kantaria is a senior product manager at BuzzFeed, where she’s worked on a range of products from launching Buzzfeed.com in international markets to building new internal tools. Swara is the product lead for distribution tools, a team responsible for building products that send all of the company’s content to social media platforms (like Facebook, YouTube, Twitter, and Instagram), OTT destinations (like Roku and Pluto), and syndication partners. Previously, Swara was a technology consultant at Deloitte.

Presentations

Media meets AI: How we give superpowers to BuzzFeed's social curators 40-minute session

As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. The company first built publishing tools that let people work more efficiently, then built artificial intelligence tools that let people work more intelligently. Join Lucy Wang and Swara Kantaria to learn more about this evolution.

Anoop Katti is a data scientist in the Deep Learning Center at SAP, where he combines computer vision with techniques from natural language processing to work on documents with strong 2D structure. Previously, he built telecom software at Huawei. Anoop holds multiple patents and publications in the field. He did his bachelor studies at BIT, Bangalore, and pursued a research-based master’s in computer vision at IIT Madras.

Presentations

Chargrid: Understanding 2D documents 40-minute session

Anoop Katti explores the shortcomings of the existing techniques for understanding 2D documents and offers an overview of the Character Grid (Chargrid), a new processing pipeline pioneered by data scientists at SAP.

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

Presentations

Deep learning for time series data 40-minute session

Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data.

Chief Data Officer

Presentations

Putting AI prototypes into production operation Intel® AI Builders Showcase

Dan Klein explores a prototype that uses AI to recognize human behavior in retail stores, using Intel Movidius to enable deep learning across video streams and connecting multiple AI functions in a single pipeline using the Intel Distribution of OpenVINO. The solution enables users to track the movement of individuals around a store with real-time recognition of gender, sentiment, and apparel.

Sanjay Krishnan is an assistant professor of computer science at the University of Chicago. His research focuses on applications of machine learning and control theory to computer and cyberphysical systems problems. His work has received a number of awards including the 2016 SIGMOD Best Demonstration award, 2015 IEEE GHTC Best Paper award, and Sage Scholar award. Sanjay holds a PhD and master’s degree in computer science from UC Berkeley.

Presentations

The curse of generality: Deep reinforcement learning in the wild 40-minute session

Drawing on his work building and deploying an RL-based relational query optimizer, a core component of almost every database system, Sanjay Krishnan highlights some of the underappreciated challenges to implementing deep reinforcement learning.

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

Presentations

Industrialized capsule networks for text analytics 40-minute session

Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.

Marcel Kurovski is a data scientist at inovex, a German IT project house focusing on digital transformation, where he works on novel methods to exploit deep learning for recommender systems in order to better personalize content and improve user experience for clients in ecommerce and retail. His work bridges the gap between proof of concept and scalable AI systems, and his research spans recommender systems, deep learning, and methods for approximate nearest neighbor search. He holds a master’s degree in industrial engineering and management from the Karlsruhe Institute of Technology (KIT), where he focused on computer science, machine learning, and operations research.

Presentations

Deep learning for recommender systems, Or How to compare pears with apples 40-minute session

Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for vehicle recommendations at Germany's biggest online vehicle market.

Marcelo Labre is executive director at Morgan Stanley, where he works on model risk management. Marcelo has extensive experience both as a practitioner and in academia in traditional quantitative finance, machine learning, and artificial intelligence. Previously, he was head of model risk at OnDeck Capital; head of quant analytics and market data for OCBC Bank; adjunct associate professor at the National University of Singapore’s Business School; managing director and head of quantitative analytics at Standard Bank; director and head of quantitative analytics at ING Bank; and a member of the faculty at London Business School. He holds a PhD in mathematical finance from Imperial College London and a master’s degree in finance from London Business School.

Presentations

Using artificial intelligence and machine learning for risk modeling in financial services (sponsored by IBM Watson) 40-minute session

Successful financial institutions like Morgan Stanley are growing more committed to efficiency and investing heavily in tools to do so. Marcelo Labre explains how the computing power and AI-readiness of IBM Power Systems enables a new journey of exploration and new possibilities in AI/ML use cases in finance.

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

Learning from multiagent emergent behaviors in a simulated environment 40-minute session

Join Danny Lange to learn how to create artificially intelligent agents that act in the physical world (through sense perception and some mechanism to take physical actions, such as driving a car). You'll discover how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices.

James is WorkFusion’s Director of Strategic Markets. He focuses on those areas where customers have achieved greatest impact with AI, and where WorkFusion sees greatest opportunity, for example, Anti-Money Laundering.

James is a highly commended business adviser, supporting executives to transform their operations. He helped accelerate WorkFusion’s rapid growth across EMEA, and now has a global role. His recent Intelligent Automation work has involved two of Europe’s top 5 Banks, a top 3 US bank, a top 3 European Teleco, a leading Scandinavian retailer, and one of the world’s leading entertainment streaming companies.

He previously worked in Strategy & Operations at Deloitte, where he was identified as one of the UK’s leading consultants by the Management Consultancies Association. He focused on corporate strategy, innovation initiatives, operating model transformation and digital programmes.

His clients have included a range of global financial institutions and FTSE 100 companies, as well as the UK government, Police and the City of London. James is a fellow of the Adam Smith Institute and read Philosophy, Politics and Economics at the University of Oxford

Presentations

Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA 40-minute session

Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant anti-money laundering (AML) and "know your customer" (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. Kyle Hoback walks you through a series of case studies that utilize AI-powered RPA that address AML and KYC.

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

Forecasting financial time series with deep learning on Azure 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.

Forecasting financial time series with deep learning on Azure (Day 2) Training Day 2

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.

Using AutoML to automate selection of machine learning models and hyperparameters 40-minute session

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is a fundamental shift in how organizations approach machine learning. Francesca Lazzeri and Wee Hyong Tok demonstrate how to use AutoML to automate the selection of machine learning models and automate tuning of hyperparameters.

Jaewon Lee is a data scientist working on NLP at Naver and LINE in South Korea. His team focuses on developing the Clova Chatbot Builder Framework, enabling customers to easily build and serve chatbots to their own business, and undertakes NLP research to improve performance of their dialogue model. He joined Naver/LINE after his company, Company.AI, was acquired in 2017. Previously, Jaewon was a quantitative data analyst at Hana Financial Investment, where he used machine learning algorithms to predict financial markets. He holds a BS in applied math and statistics with computer science from Johns Hopkins University.

Presentations

AutoML in the Chatbot Builder Framework 40-minute session

Jaewon Lee and Sihyeung Han walk you through implementing a self-trained dialogue model using AutoML and the Chatbot Builder Framework. You'll discover the value of AutoML, which allows you to provide better model, and learn how AutoML can be applied in different areas of NLP, not just for chatbots.

Nicholas Leonard is a software engineer at Twitter Cortex. He was a core contributor to Lua Torch and currently works with TensorFlow as part of the DeepBird team. He graduated from the Royal Military College of Canada and holds an MS in computer science from the University of Montreal.

Presentations

Unifying Twitter around a single ML platform 40-minute session

Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.

Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—an effort to help developers and enterprises deploy and use ML cloud natively everywhere. He’s been building on Kubernetes since its inception, starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.

Presentations

Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues 40-minute session

Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street JournalTech CrunchWiredFast CompanyHarvard Business ReviewMIT Sloan Management ReviewEntrepreneurVenture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Presentations

AI for managers (SOLD OUT) 2-Day Training

Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Eric Liang is a second-year PhD student working in the University of California, Berkeley, RISELab with Ion Stoica. He works on frameworks and applications for machine learning and reinforcement learning. Previously, he spent several years working on systems in industry at Databricks and Google.

Presentations

Building reinforcement learning models and AI applications with Ray Tutorial

Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Joleen Liang is a partner at Squirrel AI Learning, mainly in charge of branding and media. Joleen is a successful serial entrepreneur and experienced brand planner who has successively built a number of fast-moving consumer goods, young fashion catering, and AI internet education brands. After becoming the partner at Yixue Squirrel (Songshu) AI Learning, she created the Squirrel AI brand. She has been invited to give keynote speeches at AI conferences such as IJCAI in Sweden, KDD in London, TechCrunch in Berlin, World Summit AI in Amsterdam, Bloomberg in Shanghai, and UBS headquarter in Zurich. Joleen also founded 360D, a branding theory that helped Squirrel AI Learning expand its influence and brand reputation internationally, which also resulted in a 7x compound annual growth rate.

Katie Link is a data analyst at the Allen Institute for Brain Science, where she is working on building deep learning tools to solve practical problems in neuroscience research. A member of the Mount Sinai Health System AI Consortium (AISINAI), she is passionate about applying her skills in machine learning to solving problems in healthcare, and her research has focused on developing a novel semisupervised learning approach for accelerating the training of deep convolutional neural networks. She is a graduate of Johns Hopkins University (Phi Beta Kappa) with degrees in neuroscience and computer science and a FlexMed member of the Icahn School of Medicine’s class of 2023.

Presentations

How deep learning can improve medical outcomes now 40-minute session

There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them.

Victor Llorente is a technology program manager at Dow Jones, where he’s responsible for data strategy applications in the professional information business. Victor has worked in several workflow automation and AI-driven projects using BPM engines, big data, and data science technologies. He holds master’s degrees in computer engineering from Polytechnic University of Catalonia, Barcelona, and computer science from the Royal Institute of Technology, Stockholm, as well as an MBA from Instituto de Empresa, Madrid.

Presentations

Deep learning for third-party risk identification and evaluation at Dow Jones 40-minute session

Companies have a strong need for complying with anti-money laundering, antibribery, corruption, and economic sanctions regulation in mitigating third-party risk. Yulia Zvyagelskaya and Victor Llorente highlight how Dow Jones Risk & Compliance uses deep learning and NLP for efficient compliance solutions.

Ben Lorica is the chief data scientist at O’Reilly. 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

Accelerating AI Adoption Keynote

Keynote by Ben Lorica and Roger Chen

Opening remarks Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the second day of keynotes.

Opening remarks and keynote Keynote

Conference cochairs Ben Lorica, Roger Chen, and Alexis Crowell Helzer open the first day of keynotes.

Siwei Lyu is a tenured associate professor in the Department of Computer Science within the College of Engineering and Applied Sciences at the University at Albany, State University of New York, where he’s the director of the Computer Vision and Machine Learning Lab (CVML). His research interests include digital image forensics, computer vision, computational neuroscience, and machine learning. Siwei has published over 110 refereed journal and conference papers. He’s the recipient of the 2011 IEEE Signal Processing Society Best Paper Award, the 2010 National Science Foundation CAREER Award, SUNY Albany’s Presidential Award for Excellence in Research and Creative Activities, and the SUNY Chancellor’s Award for Excellence in Research and Creative Activities. He’s a senior member of the International Society of Electric and Electronic Engineers (IEEE) and a member of Omicron Delta Kappa. He holds a PhD in computer science from Dartmouth College.

Presentations

Seeing is deceiving: The rise of fake media and how to fight back

Siwei Lyu reviews the evolution of techniques behind the generation of fake media and discusses several projects in digital media forensics for the detection of fake media, with a special focus on recent work on detecting AI-generated fake videos (DeepFakes).

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Presentations

Is AI human-ready? Keynote

Aleksander Madry discusses major roadblocks that prevent current AI frameworks from having a broad impact and outlines approaches to addressing these issues and making AI frameworks truly human-ready.

Presentations

Introduction to Mobiliya (a QuEST Global company) and its capabilities in the AI space with Intel Intel® AI Builders Showcase

Mobiliya is global software engineering services (SI) company focusing on next-gen technologies like AI and DL, the IoT, and AR/VR to accelerate digital transformation for the world’s leading companies. Rubayat Mahmud showcases Mobiliya's capabilities in AI, vision, and analytics with Intel technologies.

Adam Marek is a software architect at Intel.

Presentations

Sailing with Nauta 40-minute session

Adam Marek discusses the motivation for, architecture behind, and functionality of Nauta's offerings and explains how this solution differs from other OSS offerings in the deep learning space.

Tom Marlow is the CTO for Black Hills IP, where he works tirelessly to drive IP legal services into that age of AI and automation. Relying on data wherever it can be found, Tom and the Black Hills team build products to power internal staff and legal professional customers. Tom has a background in technology and analytics. Being exposed to massive amounts of patent data very early in his career, he learned techniques to quickly build datasets and evaluate the results to drive strategy. This work led to a corporate leadership position driving global IP operation and strategy for the renowned Fairchild Semiconductor Corporation. In addition to maintaining a portfolio of several thousand patents, Tom managed patent development, litigation, licensing and acquisition across the US, Europe, and Asia. Around this time, Tom coauthored a desk reference for patent attorneys that provided an indexed analysis of appeals decisions for use in prosecuting patent applications (which is now in its 7th edition). Tom is a registered patent attorney and electrical engineer with a passion for IP systems. Previously, he was cochair of the Patent Analytics and Portfolio Management Department at the Minneapolis patent firm Schwegman, Lundberg & Woessner, PA. Tom has advised companies from startups to household name multinationals on IP strategy, operations, and policy and has spoken before a diverse audiences from patent attorneys to C-suite executives to engineers to startup founders on patent management, analysis, and strategy. He holds a law degree from Franklin Pierce Law Center and a BS from the University of Notre Dame.

Presentations

Executive Briefing: The hidden data in AI IP 40-minute session

Three elements will control the AI market: technology, data, and IP rights. Leveraging rich patent data, Thomas Marlow uncovers the companies with the top patent holdings across the world in groundbreaking research and implementation technologies, surfacing insights into the sources and owners of AI technology as well as the hurdles and opportunities that those entering the field today face.

Russell Martin is a data scientist in residence at the Data Incubator, where he instructs fellows, teaches online courses, and leads training courses with corporate partners. Russ lived and worked in the UK for 17 years, including at Warwick University and the University of Liverpool, where he taught in the Department of Computer Science. He holds a PhD in applied mathematics from the Georgia Institute of Technology.

Presentations

AI for managers (SOLD OUT) 2-Day Training

Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Hilary Mason is the general manager for machine learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company (acquired by Cloudera in 2017). Hilary is the data scientist in residence at Accel Partners and serves on the board of the Anita Borg Institute. Formerly, she cofounded HackNY.org, a nonprofit that helps engineering students find opportunities in New York’s creative technical economy, served on Mayor Bloomberg’s Technology Advisory Council, and was the chief scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.

Presentations

Building enterprise data products 40-minute session

Hilary Mason shares a process for repeatedly creating effective AI products, from idea through process to specific design considerations, and explains how architecture and algorithmic choices can support or hinder this process.

Alina Matyukhina is a cybersecurity researcher and PhD candidate at the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick. Her research focuses on applying machine learning, computational intelligence, and data analysis techniques to design innovative security solutions. Previously, she was a research assistant at the Swiss Federal Institute of Technology, where she took part in cryptography and security research projects. Alina is a member of the Association for Computing Machinery and the IEEE Computer Society. She has spoken at several security and software engineering conferences, including HackFest, IdentityNorth, ISACA Security & Risk, Droidcon SF, and PyCon Canada.

Presentations

Adversarial machine learning in digital forensics 40-minute session

Machine learning models are often susceptible to adversarial deception of their input at test time, which leads to poorer performance. Alina Matyukhina investigates the feasibility of deception in source code attribution techniques in real-world environments and explores attack scenarios on users' identities in open source projects—along with possible protection methods.

Leah McGuire is a principal member of the technical staff at Salesforce Einstein, where she builds platforms to enable the integration of machine learning into Salesforce products. Previously, Leah was a senior data scientist on the data products team at LinkedIn working on personalization, entity resolution, and relevance for a variety of LinkedIn data products and completed a postdoctoral fellowship at the University of California, Berkeley. She holds a PhD in computational neuroscience from the University of California, San Francisco, where she studied the neural encoding and integration of sensory signals.

Presentations

How to train your model (and catch label leakage) 40-minute session

Label leakage is a pervasive problem in predictive modeling data, and it takes on monstrous proportions at enterprise companies, where the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann and Leah McGuire explain how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem.

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

Presentations

Natural language processing with deep learning (SOLD OUT) 2-Day Training

Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Ming-Wei Chang is a research scientist at Google AI Language. He enjoys developing interesting machine learning algorithms for practical problems, especially in the field of natural language processing. He has published more than 35 papers at top-tier conferences and won an outstanding paper award at ACL 2015 for his work on question answering over knowledge bases. He also won several international machine learning competitions on topics like entity linking, power load forecast prediction, and sequential data classification. His recent paper, “BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding“—cowritten with his colleagues in Google AI Language—demonstrates the power of language model pretraining and details the new state of the art for over 11 natural language processing tasks.

Presentations

BERT: Pretraining deep bidirectional transformers for language understanding 40-minute session

Ming-Wei Chang offers an overview of a new language representation model called BERT (Bidirectional Encoder Representations from Transformers). Unlike recent language representation models, BERT is designed to pretrain deep bidirectional representations by jointly conditioning on both left and right context in all layers.

Vinay Seth Mohta is CEO at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Previously, Vinay was a product manager at KAYAK, where he worked with both Hadoop and Hive to develop a robust view of customers and developed a predictive model for flight pricing, and an architect at Endeca Technologies, where he worked on the engineering team that developed new data structures and indexing technologies to enable search and faceted navigation. He’s a coinventor on several granted patents for search and faceted navigation. Vinay holds a BS and a master’s degree in engineering from MIT.

Presentations

Executive Briefing: 5 key questions to kick off your AI implementation 40-minute session

The significant hype bubble building up around AI has convinced many executives that if they’re not already tech savvy, they might not be ready for AI’s “transformative power.” However, the reality is that AI is just another tool that can help your business, and you’re probably not that far behind. Vinay Seth Mohta explains how to evaluate AI as you would any other strategic investment.

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

Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

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

Presentations

Toward ethical AI: Inclusivity as a messy, difficult, but promising answer (sponsored by Dataiku) Keynote

AI technologists must consider the ethical implications of what we're building. Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration is a necessary answer.

Ryan Mukherjee is a senior research engineer at JHU/APL. Ryan has been involved in machine learning, computer vision, and remote sensing projects for over nine years within JHU/APL’s Research and Exploratory Development Department. Most recently, Ryan led JHU/APL’s support of IARPA’s functional Map of the World effort and is focused on providing easy and free access to associated tools and data.

Presentations

ImageNet for satellite imagery: Opportunities and risks 40-minute session

While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. Ryan Mukherjee and Neil Fendley offer an overview of functional Map of the World (fMoW), an ImageNet for satellite imagery built to address this issue, and explain how you can attack or defend these deep learning models.

Banu Nagasundaram is a product marketing manager with the Artificial Intelligence Products Group at Intel, where she drives overall Intel AI products positioning and AI benchmarking strategy and acts as the technical marketer for AI products including Intel Xeon and Intel Nervana Neural Network Processors. Previously, Banu was a product marketing engineer with the Data Center Group at Intel, where she supported performance marketing for Xeon Phi, Intel FPGA, and Xeon for AI; was a design engineer on the exascale supercomputing research team with Intel Federal; and worked at Qualcomm doing design verification of mobile processors. Banu holds an MS in electrical and computer engineering from the University of Florida and is working toward an MBA at UC Berkeley’s Haas School of Business.

Presentations

Understanding and integrating Intel Deep Learning Boost (Intel DL Boost)

Banu Nagasundaram offers an overview of Intel's Deep Learning Boost (Intel DL Boost) technology, featuring integer vector neural network instructions targeting future Intel Xeon scalable processors. Banu walks you through the 8-bit integer convolution implementation made in the Intel MKL-DNN library to demonstrate how this new instruction is used in optimized code.

Karthikeyan Natesan Ramamurthy is a research staff member in IBM Research AI at the T. J. Watson Research Center. His broad research interests include understanding the geometry and topology of high-dimensional data and developing theory and methods for efficiently modeling the data. He has also been intrigued by the interplay between humans, machines, and data and the societal implications of machine learning. His papers have won best paper awards at the 2015 IEEE International Conference on Data Science and Advanced Analytics and the 2015 SIAM International Conference on Data Mining. He’s an associate editor of Digital Signal Processing and a member of the IEEE. He holds a PhD in electrical engineering from Arizona State University.

Presentations

Introducing the AI Fairness 360 toolkit Tutorial

Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

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

Presentations

Executive Briefing: Overview of data governance 40-minute session

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.

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

Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Jack Norris is the senior vice president of data and applications at MapR Technologies, where he works with leading customers and partners worldwide to drive the understanding and adoption of new applications enabled by data and analytics. With over 25 years of enterprise software experience, he has demonstrated success from identifying new markets to defining new products to launching companies. Jack’s background includes senior executive positions with establishing analytic, virtualization, and storage companies. Jack was an early employee of MapR Technologies and held senior executive roles with EMC, Brio Technology, and Bain and Company.

Presentations

Unlocking AI value at scale: 3 building blocks and 1 massive mistake to avoid (sponsored by MapR) 40-minute session

Jack Norris delves into the three building blocks and the one massive mistake to avoid for any organization looking to leverage AI.

Will Nowak is a data scientist a Dataiku, where he helps Fortune 500 companies improve data science operations. Previously, he engineered machine learning models for several Y Combinator startups, learning the pitfalls and challenges to productionalizing machine learning. Will holds a bachelor’s in math and economics from Northwestern University and a master’s in organizational leadership from Columbia University. In addition to the aforementioned topics, Will enjoys biking, coffee, and donuts and dislikes buzzwords, pretension, and meanness.

Presentations

Getting to ROI: Case studies in operationalizing machine learning (sponsored by Dataiku) 40-minute session

AI and machine learning are top priorities for nearly every company. Despite this, "productionalizing" machine learning processes is an underappreciated problem, and as a result, businesses often find themselves failing to maximize ROI from their data initiatives. Will Nowak identifies best practices and common pitfalls in bringing machine learning and AI models to production.

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

Presentations

Game engines and machine learning 40-minute session

Games are wonderful contained problem spaces, making them great places to explore AI—even if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world.

Eric Karl Oermann is an instructor of neurological surgery in the Mount Sinai Health System and the director of AISINAI, Mount Sinai’s artificial intelligence research group. Prior to attending medical school, Eric spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino. He has won numerous awards for his scholarship, including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation, where he was first exposed to neural networks and deep learning. He has published over 50 manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, he was selected as one of Forbes’s “30 under 30” for his work in applying machine learning to develop prognostic models for cancer patients. He’s interested in weakly supervised learning, reinforcement learning with imperfect information and in building artificial neural networks that more accurately model biological neural networks. As an actively practicing neurosurgeon, he is also interested in the application of deep learning to solve a wide range of problems in the medical sciences and improving clinical care. He holds an MD and studied mathematics at Georgetown University with a focus on differential geometry; he completed a postdoctoral fellowship at Google (Google Health/Verily Life Sciences).

Presentations

How deep learning can improve medical outcomes now 40-minute session

There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them.

Diego Oppenheimer is the founder and CEO of Algorithmia. An entrepreneur and product developer with extensive background in all things data, Diego has designed, managed, and shipped some of Microsoft’s most used data analysis products, including Excel, Power Pivot, SQL Server, and Power BI. Diego holds a bachelor’s degree in information systems and a master’s degree in business intelligence and data analytics from Carnegie Mellon University.

Presentations

Designing a machine learning operating platform 40-minute session

Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science.

Catherine Ordun is a senior data scientist at Booz Allen focused on growing AI capabilities for biosurveillance and biodefense clients across the public health and defense markets. She specializes in leading teams to develop machine learning models for computer vision, natural language processing, and time series forecasting and collaborates with modern software and Agile development teams to build environments for deployable models. Over the course of her career at Booz Allen, Catherine has served clients in the intelligence community, the Centers for Disease Control and Prevention (CDC), the Food and Drug Administration (FDA), the Department of Veterans Affairs (VA), the US Army, and the Department of Treasury. The breadth of her experience is reflected by the diversity of the data, use cases, and client requirements across these organizations, ranging from leading prototypes that combine computer vision and robotic process automation at the Department of Treasury to predicting hostile work environment risk at the VA to developing time series disease forecasting models for the DoD and developing cognitive search capabilities for the US Army. Recently, Catherine has been leading a team of data scientists to develop prototype sentiment modeling on images and is working to help lead investments in model reproducibility and interpretability at Booz Allen. She’s passionate about mentoring junior talent and promoting education for the firm’s Women in Data Science group. Previously, Catherine worked for the CDC, the Defense Advanced Research Projects Agency (DARPA), and the US intelligence community. She holds a BS in applied biology from Georgia Tech, an MPH in environmental and occupational health from Emory University, and an MBA from George Washington University. She’s also a Booz Allen NVIDIA-certified Deep Learning Instructor.

Presentations

Developing your own model tracking leaderboard in Keras 40-minute session

While building machine learning models for most large projects, data scientists typically design dozens of models using different combinations of hyperparameters, data configurations, and training settings. Catherine Ordun describes how to build your own machine learning model tracking leaderboard in Keras.

Jim Pastore is a litigation partner at Debevoise & Plimpton LLP and a member of the firm’s Cybersecurity and Data Privacy practice and Intellectual Property Litigation Group. Jim has assisted a broad range of clients in cybersecurity and data privacy matters, including the Home Depot (in connection with its 2014 data breach), PayPal (in connection with a 2017 data security incident at its subsidiary, TIO Networks), American Express, KKR, and the NBA, among others. He’s been recognized by the Legal 500 for both his intellectual property and cybersecurity and data privacy work; by Chambers USA 2018 as a leading lawyer for privacy and data security; and by the National Law Journal as a cybersecurity trailblazer. He has also twice been named to Cybersecurity Docket’s “Incident Response 30,” to Benchmark Litigation’s “Under 40 Hot List,” and as a "rising star” by Law360 for his cybersecurity work. Previously, he was an assistant United States attorney in the Criminal Division of the Southern District of New York, where he was assigned to the Complex Frauds Unit and the Computer Hacking and Intellectual Property Section. He successfully litigated eight jury trials to verdict and was the lead prosecutor in United States v. Monsegur, a.k.a. “Sabu,” and Operation Cardshop, both of which were named to the FBI’s top 10 cases of 2012. He also led Operation Dirty RAT, which targeted the creators and users of Blackshades ransom and malware, resulting in the largest ever worldwide law enforcement action against cybercriminals. In connection with the so-called “doomsday virus,” he obtained a unique order to prevent catastrophic Internet outage. Before that, he was an associate at Debevoise, working on a variety of high-profile intellectual property matters, including the well-publicized Google books copyright litigation.

Jim is routinely sought out as a speaker on cybersecurity and data privacy and has been invited to present to the Department of Justice’s National Cyber Security Division and its National Advocacy Center, Georgetown Law’s Cybersecurity Law Institute, the FBI-led International Conference on Cyber Security, the annual meeting of the Association of Life Insurance Counsel (ALIC), and the Fiduciary and Investment Risk Management Association (FIRMA)’s National Risk Management Training Conference, as well as to the boards of multiple public companies. His publications include “Cybersecurity: Evaluating Transactional Risk,” “A Closer Look,” “New York State Department of Financial Services Expands Its Cyber Focus to Insurers,” and “Debevoise & Plimpton on Cybersecurity: Reducing Threats to Private Equity Firms and Their Portfolio Companies." He holds a JD, with distinction, from Stanford Law School, where he served as copresident of the Stanford Law & Technology Association and was a member of the Stanford Technology Law Review. He holds a BA, summa cum laude and Phi Beta Kappa, from the University of Notre Dame, where he was a Notre Dame Scholar, the recipient of the James E. Robinson Award for outstanding senior English major, and one of 40 class members of the Honors Program of the College of Arts & Letters.

Presentations

Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019 40-minute session

Anna Gressel, Jim Pastore, and Anwesa Paul lead a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and the California Consumer Privacy Act. They also explore key lawsuits challenging AI in US courts and unpack the implications for companies going forward, helping you mitigate legal and regulatory risks and position your AI products for success.

Maharshi Patel is a Software Engineer at Microsoft working on FPGA accelerated Azure Machine Learning Platform for Cloud and Edge.

Presentations

Fast (and cheap) AI accelerated on FPGAs 40-minute session

Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave.

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

Mo Patel leads a deep dive into all aspects of the PyTorch lifecycle via hands-on examples such as image classification, text classification, and linear modeling. Along the way, you'll explore other aspects of machine learning such as transfer learning, data modeling, and deploying to production with immersive labs.

Anwesa Paul is chief global privacy counsel at American Express, where she advises all parts of the business with legal questions relating to US and Canadian financial privacy laws, marketing privacy laws, online and mobile privacy self-regulatory guidelines, big data governance, and big data breaches. Previously, Anwesa was in-house counsel at online advertising technology startups Kinetic Social and Epic Media Group and worked in the privacy space at the New York State Attorney General’s Office Internet Bureau, handling consumer protection issues related to online marketing.

Presentations

Executive Briefing: The regulatory road ahead—How to navigate the legal trends driving AI in 2019 40-minute session

Anna Gressel, Jim Pastore, and Anwesa Paul lead a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and the California Consumer Privacy Act. They also explore key lawsuits challenging AI in US courts and unpack the implications for companies going forward, helping you mitigate legal and regulatory risks and position your AI products for success.

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

Building AI assistants that scale using machine learning and open source tools Tutorial

Justina Petraityte offers a hands-on walk-through of developing intelligent AI assistants based entirely on machine learning and using only the open source tools Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and best practices for developing AI assistants that scale and learn from real conversational data.

Dmitry Petrov is cofounder and CEO at Iterative AI, where he’s working on tools for machine learning and data versioning. An ex-data scientist at Microsoft and an active open source contributor, Dmitry wrote and open-sourced the first version of the DVC.org project and implemented a wavelet-based image hashing algorithm (wHash) in open source library ImageHash for Python. He holds a PhD in computer science.

Presentations

Open source tools for machine learning model and dataset versioning 40-minute session

ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.

Forough Poursabzi-Sangdeh is a postdoctoral researcher at Microsoft Research New York City. She works in the interdisciplinary area of interpretable and interactive machine learning, collaborating with psychologists to study human behavior when interacting with machine learning models. She uses these insights to design machine learning models that humans can use effectively. She’s also interested in several aspects of fairness, accountability, and transparency in machine learning and their effect on users’ decision-making process. Forough holds a BE in computer engineering from the University of Tehran and a PhD in computer science from the University of Colorado at Boulder.

Presentations

Manipulating and measuring model interpretability 40-minute session

Forough Poursabzi-Sangdeh argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users’ behavior.

Rajendra Prasad (RP) leads automation and artificial intelligence for Accenture Technology Services. In this role, he focuses on driving efficiency into the delivery of Accenture services across the application lifecycle and leads a global team of highly qualified professionals who help IT organizations achieve success in their automation and Agile transformations. RP also leads the team that created and deploys Accenture myWizard, an intelligent automation platform with artificial intelligence at its core. He has 23 years of experience, more than 20 patents and patents pending, and 30 papers published in international journals and conferences.

Presentations

Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) 40-minute session

After crossing the first AI implementation milestone, leaders often ask, "What’s next?" Based on experience implementing AI-led automation for more than 100 clients, Accenture has developed an easy-to-use methodology for scaling and sustaining reliable AI solutions. Rajendra Prasad (RP) explains how leaders and change makers in large enterprises can make AI adoption successful.

Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture) Keynote

After crossing the first AI implementation milestone, leaders often ask, "What’s next?" Based on experience implementing AI-led automation for more than 100 clients, Accenture has developed an easy-to-use methodology for scaling and sustaining reliable AI solutions. Rajendra Prasad (RP) explains how leaders and change makers in large enterprises can make AI adoption successful.

Adam Procter is a deep learning software engineer in the Artificial Intelligence Products Group at Intel, where he works on the core design of the Intel nGraph deep learning compiler. He holds a PhD in computer science from the University of Missouri, where his research focused on programming language semantics, high-assurance computing, and techniques for compiling functional programming languages to reconfigurable hardware.

Presentations

nGraph: Unlocking next-generation performance with deep learning compilers 40-minute session

The rapid growth of deep learning in demanding large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. Adam Straw, Adam Procter, and Robert Earhart offer a comprehensive overview of Intel's nGraph deep learning compiler.

Ruchir Puri is the chief scientist of IBM Research and an IBM Fellow. Previously, he led IBM Watson as its CTO and chief architect and has held various technical, research, and engineering leadership roles across IBM’s AI and Research businesses and served as an adjunct professor at Columbia University and a visiting scientist at Stanford University. Ruchir is a fellow of the IEEE, has been recognized as an ACM Distinguished Speaker and an IEEE Distinguished Lecturer, and was awarded 2014 Asian American Engineer of the Year. Ruchir was also honored with the John Von-Neumann Chair at the Institute of Discrete Mathematics at Bonn University, Germany. Ruchir is an inventor who holds over 50 United States patents; he has authored over 100 scientific publications on software, hardware automation methods, and optimization algorithms.

Presentations

Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) Keynote

Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks, accelerating enterprises' journey to AI.

Automation of AI: Accelerating the AI revolution (sponsored by IBM Watson) 40-minute session

Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks, accelerating enterprises' journey to AI.

Anand Rao is a partner in PwC’s Advisory Practice and is the global AI lead and the competency lead for the Analytics Practice in US. 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 responsible for research and commercial relationships with academic institutions and startups. 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. 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’s a frequent speaker on AI, behavioral economics, autonomous cars and their impact, analytics, and technology topics in academic and trade forums. Anand was recently selected as one of the top 100 innovators of data and analytics and also in the top 50 data and analytics professionals in the US and Canada by Corinium. His recent paper, “A Strategist’s Guide to Artificial Intelligence,” won the 2017 Azbee Award for Best Paper. Anand holds an MSc in computer science from the Birla Institute of Technology and Science, 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

Executive Briefing: Responsible AI—An approach to and case studies for building fair, interpretable, safe AI 40-minute session

Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. Anand Rao synthesizes the current research in FAT (fairness, accountability, and transparency) into a step-by-step methodology to address these issues—illustrated with case studies from the financial services and healthcare industries.

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 (SOLD OUT) 2-Day Training

Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Natural language processing with deep learning (SOLD OUT) Training Day 2

Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

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

Presentations

A software accelerator for machine learning 40-minute session

The AI industry needs new software architectures for distributed systems to solve critical problems. Vinay Rao and Santi Adavani explain why software architectures will lead the next generation of machine learning approaches and how RocketML has built logistic regression models on the KDD12 dataset with ~150 million samples on an eight-Intel Xeon-node cluster in under a minute.

Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University. He’s also affiliated with the Statistical Machine Learning Group, the Pervasive Parallelism Lab, and the Stanford AI Lab. His work’s goal is to enable users and developers to build applications that more deeply understand and exploit data. His contributions span theory, systems, and machine learning, and his work has won best papers at the premiere venue in each area. Work from his group has been incorporated into major scientific and humanitarian efforts, including MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies. He cofounded a company, based on his research, that was acquired by Apple in 2017. He also recently cofounded a company, SambaNova, based on his research, that is currently building accelerated platforms for AI.

Presentations

Software 2.0 & Snorkel Keynote

Keynote by Christopher Ré

Matthew Reyes is a consultant and an independent researcher developing a reinforcement learning-based approach to influence the maximization on social networks. Previously, he spent more than four years at MIT’s Lincoln Laboratory. He holds a PhD and MS in electrical engineering systems from the University of Michigan, Ann Arbor, and an MS and BS in math from Wichita State University.

Matthew Reyes is a contractor at Technergetics working on deep learning and FPGAs. He earned his B.S. and M.S. in mathematics at Wichita State University in Wichita, KS. He then earned his M.S. and Ph.D. in EE:Systems at the University of Michigan in Ann Arbor, MI doing a thesis on compression of Markov random fields. He worked for four years at MIT Lincoln Laboratory doing work on sensor calibration. From early 2015 to early 2019, Matt conducted independent research on compression, belief propagation, and interpolation of Markov fields, and is currently developing a model of social decision-making based on random utility.

Presentations

A reinforcement learning approach to optimizing preference on a social network 40-minute session

Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations.

Bill Roberts is managing director of Deloitte Consulting, where he specializes in data science, algorithms, and advanced analytics. He has over 20 years’ experience applying machine learning and stochastic modeling to real-world problems in industry, government, and academia, including algorithmic solutions that enable analytics to be applied at scale to open and closed source data.

Presentations

Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting) 40-minute session

Bill Roberts discusses artificial intelligence for strategic business insight and for the solution of new business problems using advanced cognitive algorithms. Along the way, he highlights the importance of using the right algorithm for a given business challenge, using real-world examples.

Thomas Roehm is senior director of global marketing and messaging at SAS, where he’s responsible for providing global direction for messaging, sales enablement, and industry sales support. He leads positioning and market-facing activities in artificial intelligence and oversees several operational teams, including corporate messaging, product and industry marketing, customer validation, global industry practices, and marketing and sales enablement. Thomas has more than 20 years’ experience working within the manufacturing industry and 32 years’ experience in information technology, spent helping clients leverage analytics for new business values.

Presentations

How leaders are tackling their most pressing AI challenges (sponsored by SAS) 40-minute session

Drawing on case studies and recent survey insights, Tom Roehm and Alexis Crowell Helzer offer a front-row view into how companies are taking on everything from trust in AI to its impact on jobs, oversight, and ethics.

Tom Sabo is a principal solutions architect at SAS. He’s been immersed in the field of text analytics as it applies to federal government challenges since 2005. Tom presents work internationally on diverse topics including modeling applied to government procurement, best practices in social media analysis, and using analytics to leverage and predict research trends. He also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS and OHA on social media strategies. He holds a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.

Presentations

An artificial intelligence framework to counter international human trafficking 40-minute session

Sources of international human trafficking data contain a wealth of textual information that is laborious to assess using manual methods. Tom Sabo demonstrates text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response.

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

Presentations

Deploying deep learning models on GPU-enabled Kubernetes clusters 40-minute session

Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster.

Shioulin Sam is a research engineer at Cloudera Fast Forward Labs, where she bridges academic research in machine learning with industrial applications. Previously, she managed a portfolio of early stage ventures focusing on women-led startups and public market investments and worked in the investment management industry designing quantitative trading strategies. She holds a PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.

Presentations

Machine learning possibilities Intel® AI Builders Showcase

Cloudera Fast Forward Labs translates machine learning breakthroughs in academia for industry applications. Shioulin Sam provides a high-level overview of this capability and discusses how it's now possible to build models and applications faster and cheaper in industries such as autonomous vehicles and healthcare.

Tony Sandoval has been working in the AI and Machine Learning Space for the past few years, having assisted in deploying AI solutions into companies such as Liberty Mutual Life Insurance and Nationwide. Starting as a developer during the early dot-com years, he advanced through various technology-based organizations in the US, Europe and Asia, with his last international posting being the Director of Technology for the multinational HR, Payroll and Billing company, Adecco in North East Asia. He has also worked for the Advertising Agencies Publicis, Leo Burnett and J Walter Thompson. In addition, Tony has served as CTO for MyRegistry.com as well as having been a founding member in startups, most notably, the Tackable and Eventster apps.

Tony holds a Bachelors of Arts in International Relations from New York University as well as a Juris Doctor from Brooklyn Law School. He also is a retired member of the New York and New Jersey Bar, as well as the Federal Bar.

Tony has also worked on Digital Advertising Campaigns for such brands as: Thule, General Mills products, Grey Goose, Bacardi, Yellow Tail Wines, Sisley and Plantronics.

Presentations

Vertical AI for banking with NLU Intel® AI Builders Showcase

Tony Sandoval leads a live walkthrough of how general-purpose natural language understanding (NLU) can be customized for banking using domain-specific AI. Join in to learn how Avaamo utilizes Intel Xeon Scalable processors to execute domain-specific NLU, transfer learning, conversational intelligence, domain modeling, flow modeling, and conversation validation.

Sumit Sanyal is founder and chief revenue officer at Minds.ai.

Presentations

Addressing the market for AI-based controllers using high-accuracy multibody simulations Intel® AI Builders Showcase

AI-based controllers for electromechanical systems are proving to be very effective. This requires training multiple agents and solvers in parallel at scale. Sumit Sanyal offers an overview of DeepSim—a platform to design and deploy such controllers—and compares its performance against results achieved using conventional controller design techniques.

Tim Schwuchow is a data scientist in residence at the Data Incubator. Previously, he designed and instructed several undergraduate- and graduate-level courses at Duke, was an analyst at quantitative hedge fund D. E. Shaw & Co., and was an economist for the Postal Regulatory Commission. He holds degrees in economics from Harvard and Duke.

Presentations

AI for managers (SOLD OUT) Training Day 2

Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Kabir Seth is the director of operations for the Wall Street Journal product, design and engineering lead, and the colead of the AI Center of Excellence at Dow Jones. He has worked in a variety of industries including apparel, travel, and children’s media. He spends his free time with his family, watching movies, playing with Legos, and writing fiction.

Presentations

Leveraging AI in a large organization Tutorial

Alex Siegman and Kabir Seth walk you through the steps necessary to appropriately leverage AI in a large organization. This includes ways to identify business opportunities that lend themselves to AI as well as best practices on everything from data intake and manipulation to model selection, output analysis, development, and deployment, all while navigating a complex organizational structure.

Ivan Shcheklein is cofounder and CTO at Iterative AI, where he’s working on tools for data scientists. Previously, he was team lead for open source project Sedna.org and cofounded the Tweeted Times (acquired by Yandex in 2011). He holds an MS in CS.

Presentations

Open source tools for machine learning model and dataset versioning 40-minute session

ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.

Alex Siegman is an AI technical program manager at Dow Jones and colead of the Dow Jones AI Center of Excellence. Alex’s expertise includes successfully managing projects, products, and people as well as studying and perfecting the constants—those skills and techniques necessary to design, develop, and deploy revenue-driving technologies across place and time.

Presentations

Leveraging AI in a large organization Tutorial

Alex Siegman and Kabir Seth walk you through the steps necessary to appropriately leverage AI in a large organization. This includes ways to identify business opportunities that lend themselves to AI as well as best practices on everything from data intake and manipulation to model selection, output analysis, development, and deployment, all while navigating a complex organizational structure.

Gadi Singer is vice president of the Artificial Intelligence Products Group and general manager of architecture at Intel, where he’s responsible for the planning and architecture of future products and technologies in the AI space, including dedicated deep learning ASICs such as the Intel Nervana Neural Network Processor. He previously oversaw the architecture of future IPs and leadership technologies for integration in Intel products and foundry such as CPU cores, graphics, imaging, security and audio, among others and drove solutions for emerging workloads and usages in artificial intelligence and machine learning.

Presentations

Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale Keynote

Gadi Singer explores four real-world AI deployments at enterprise scale.

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

Risk-free deep learning without sacrificing performance 40-minute session

Building deep learning applications is hard. Building them repeatably is harder. Maintaining high computational performance during a repeatable deep learning development process is borderline impossible. Evan Sparks describes the key pitfalls associated with fast, repeatable model development and details what practitioners can do to avoid them and maintain a supercharged AI development workflow.

Matt Speck is a data engineer and senior consultant in the Strategic Innovation Group (SIG) at Booz Allen Hamilton, where he works on cognitive solutions projects, building intelligent search and chat applications. Previously, he taught data science and Python at General Assembly, a coding boot camp with locations across the globe.

Presentations

Building intelligent conversational agents with transfer learning and cognitive automation 40-minute session

Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You'll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation.

Vladimir Starostenkov is a machine learning solution architect at Altoros with extensive experience in big data.

Presentations

Using machine learning to automate car damage assessment and document workflows Intel® AI Builders Showcase

Vladimir Starostenkov and Siarhei Sukhadolski discuss two ML solutions from Altoros: one was developed to facilitate the process of assessing car damage right at the accident scene, while the second helps to automate recognition, extraction, and analysis. Join in to see how to integrate both solutions into the existing workflows of insurance, car rental, and maintenance services.

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

Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Adam Straw is a Deep Learning Software Engineer in the Artificial Intelligence Products Group at Intel Corporation. Adam received a B.S. in Computer Engineering from Iowa State University and is currently working on a M.S. in Computer Science with a specialty in machine learning at Georgia Institute of Technology. Adam work on Intel® nGraph™ deep learning compiler with special focus on core design including current responsibilities for the nGraph quantization scheme.

Presentations

nGraph: Unlocking next-generation performance with deep learning compilers 40-minute session

The rapid growth of deep learning in demanding large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. Adam Straw, Adam Procter, and Robert Earhart offer a comprehensive overview of Intel's nGraph deep learning compiler.

Presentations

Using machine learning to automate car damage assessment and document workflows Intel® AI Builders Showcase

Vladimir Starostenkov and Siarhei Sukhadolski discuss two ML solutions from Altoros: one was developed to facilitate the process of assessing car damage right at the accident scene, while the second helps to automate recognition, extraction, and analysis. Join in to see how to integrate both solutions into the existing workflows of insurance, car rental, and maintenance services.

Andrew Caosun is a senior at Horace Mann School. He’s been actively involved with Concerts in Motion since middle school, spending Sunday afternoons singing with seniors in nursing homes, and has also participated in seasonal events at the Turtle Bay Music School, raising a music education fund for children from disadvantaged families. The friendships he developed during these events helped him understand just how much music can mean to someone, giving him the idea to combine his love for singing and recent technological advancements to help others compose their own pieces. His research, under the guidance of David Gu, applies a hybrid HMM and convolutional neural network with LSTM to compose music.

Presentations

Make music composing easier for amateurs: A hybrid machine learning approach 40-minute session

Andrew Caosun discusses a framework that unifies hidden Markov models and deep learn algorithms (RNN) with modeling components that consider long-term memory and semantics of music (LSTM and convolution). It takes users' original creations as input, modifies the raw scores, and generates musically appropriate melodies.

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

Industrial-grade, state-of-the-art language understanding Intel® AI Builders Showcase

Spark NLP provides state-of-the-art accuracy, speed, and scalability for common natural language processing tasks. David Talby shares recent achievements in accelerating Spark NLP using Intel’s optimized MKL-DNN and TensorFlow for the Xeon processor, productizing Intel NLP Architect models, and applying the library in real projects.

What you must know to build AI systems that understand natural language 40-minute 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 shares 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.

Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.

Presentations

Random search and reproducibility for neural architecture search 40-minute session

Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. Ameet Talwalkar shares work that aims to help ground the empirical results in this field and proposes new NAS baselines.

Katherine Taylor is a senior data scientist in the Global Technology Practice for Artificial Intelligence and Machine Learning at SAS. She researches and develops AI applications in industry that are practical and understandable and deliver tangible benefits; in this capacity, she works closely with SAS research and development and customers to connect the technology with the market and the market with technical understanding. Her background is in quantitative analysis, primarily in energy and banking.

Presentations

Synergize your tech stack to realize AI’s full potential (sponsored by SAS) 40-minute session

The whole of AI is greater than the sum of its parts, but achieving the best analytics edge often requires a mixture of technologies—chaining together AI technologies to build smart end-to-end processes. Katie Taylor explores use cases within key industries to uncover how companies are succeeding with AI through a layered technology stack.

Jeff Thompson is an artist, programmer, and educator based in the NYC area. His work explores collaboration with, empathy for, and the poetics of computers and technological systems. Through code, sculpture, sound, and performance, Jeff’s work uses conceptual processes like remix, translation, and visualization to physicalize and give materiality to otherwise invisible processes. He’s an assistant professor and program director of visual art and technology at Stevens Institute of Technology, where he’s also the coordinator of the Arts and Music research cluster at the Institute for Artificial Intelligence. This fall, he’s a visiting fellow at King’s College and artist in residence at the Computer Laboratory, both at University of Cambridge.

Presentations

Artists and supercomputers: Creative collaborations in AI 40-minute session

What's it like to be a mobile phone or to attach a wind sensor to a neural network? Jeff Thompson outlines several recent creative projects that push the tools of AI in new directions. Part technical discussion and part case study for embedding artists in technical institutions, this talk explores the ways that artists and scientists can collaborate to expand the ways that AI can be used.

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

Forecasting financial time series with deep learning on Azure 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.

Using AutoML to automate selection of machine learning models and hyperparameters 40-minute session

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is a fundamental shift in how organizations approach machine learning. Francesca Lazzeri and Wee Hyong Tok demonstrate how to use AutoML to automate the selection of machine learning models and automate tuning of hyperparameters.

Richard Tong is the Chief Architect of Squirrel AI Learning by Yixue Education Group. He is an experienced ed-tech technologist, executive and entrepreneur. He was the Head of Implementation, Greater China Region for Knewton, and Director of Solution Architecture for Amplify Education. He also served as CTO of Phoenix New Media (NYSE:FENG). He has been heavily involved in education technology standardization in the last 8 years. He is a current member of the IEEE AIS (Adaptive Instructional Systems) Standard working group and chair for the 2247.2 Interoperability Subgroup, a member of the IEEE ICICLE (IC Industry Consortium on Learning Engineering) and IEEE FML (Federated Machine Learning) working group. He was a member of the School Interoperability Framework Association (SIFA) Technology Board, and co-chair of the Assessment Group and IDM Group. He also served as a member of the Assessment Interoperability Framework working group for Common Education Data Standard (CEDS), and IMS Global Caliper standard workgroup and Computer Adaptive Testing workgroup, etc.

Presentations

The intersection between human learning and machine learning. How AI will fundamentally change teaching and learning. (sponsored by Squirrel AI Learning) 40-minute session

One of the most critical issues of traditional education is the lack of high-quality teachers for the personalized attention of individual student need. AI technology, especially the AI adaptive technology can enable the new generation of teachers to teach student much more effectively and improve the efficiency of the education industry.

Olga Troyanskaya is a professor at the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University and deputy director for genomics at the Flatiron Institute of the Simons Foundation. Her group is focused on developing machine learning methods to address cutting edge problems in genomics and precision medicine. She’s a fellow of the International Society for Computational Biology and a recipient of the Sloan Research Fellowship, the NSF CAREER award, the Howard Wentz faculty award, and the Blavatnik Finalist Award. She has also been honored as one of the top young technology innovators by MIT Technology Review and is the 2011 recipient of the Overton Prize from the International Society for Computational Biology and the 2014 Ira Herskowitz Award from the Genetic Society of America. She holds a PhD from Stanford University.

Presentations

Decoding the human genome with deep learning Keynote

How can machine learning decode the mysteries of life? Why are algorithms essential to enabling precision medical treatments? How do genomes encode the diversity of cells that make up humans and the signals predisposing us to diseases? Olga Troyanskaya discusses these and other questions through the prism of developing deep learning-based approaches for analysis of the human genome.

Suresh Vadakath is an NYC-based data scientist at DataRobot, where he helps customers and prospects understand and leverage automated machine learning. Suresh has eight years of experience in analytics and data science. Previously, he was a senior solutions engineer at Alteryx, where he guided high-touch enterprise customers in their data prep and predictive analytics initiatives. He holds an MBA in finance from the University of Rochester in Upstate NY.

Presentations

Automated machine learning for the enterprise Intel® AI Builders Showcase

DataRobot, powered by Intel Xeon Scalable processors, is the first automated machine learning solution that enables enterprises to become AI driven and build machine learning models at the click of a button. Suresh Vadakath examines DataRobot's automated ML platform within the context of a personal loan default case.

From prediction to prescription: Optimizing AI (sponsored by DataRobot) 40-minute session

Many companies want to influence the future by adjusting factors that they control. Suresh Vadakath covers practical ways to extend machine learning models via simulations and points out common pitfalls to avoid. Suresh then discusses a few applications in marketing, pricing, and operations to illustrate how this approach works in the real world.

From prediction to prescription: Optimizing AI (sponsored by DataRobot) 40-minute session

Many companies want to influence the future by adjusting factors that they control. Suresh Vadakath covers practical ways to extend machine learning models via simulations and points out common pitfalls to avoid. Suresh then discusses a few applications in marketing, pricing, and operations to illustrate how this approach works in the real world.

Pamela Vagata is the AI tech lead at Stripe, where she focuses on building deep learning models. Previously, she was a member of Facebook AI Research, developed FBLearner Flow (Facebook’s production ML infrastructure), and spent time building data infrastructure.

Presentations

Fraud detection without feature engineering 40-minute session

Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance.

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

Presentations

Automated medical X-ray image segmentation and medical image classification Intel® AI Builders Showcase

Medical imaging and diagnostics involves segmentation of region of interest and classification of images for diagnostics. Sunil Baliga and Sundar Varadarajan share Wipro's medical image segmentation and diagnosis solution, which uses deep learning on Intel’s AI platform.

Deepashri Varadharajan is a lead analyst at CB Insights, researching the intersection of AI and different industries. She’s passionate about studying new business models and opportunities that AI is helping shape and how traditional corporations and governments are adapting to a rapidly changing landscape. Previously, she worked for media houses including Al Jazeera America. Deepashri holds an undergraduate degree in electronics and communications engineering from VIT University in India and an MS from the Columbia University Graduate School of Journalism.

Presentations

Executive Briefing: New business models in the age of artificial intelligence 40-minute session

CB Insights tracks over 3,000 AI startups across 25+ verticals. While every vertical has benefited from deep learning and better hardware processing, the bottlenecks and opportunities are unique to each sector. Deepashri Varadharajan explores what's driving AI applications in different verticals like healthcare, retail, and security and analyzes emerging business models.

Kush R. Varshney is a research staff member and manager at IBM Research AI at the T. J. Watson Research Center, where he leads the Learning and Decision Making Group. He’s the founding codirector of the IBM Science for Social Good initiative. His research applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He also conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences. He holds a PhD and SM in electrical engineering and computer science from MIT, where he was a National Science Foundation Graduate Research Fellow, and a BS (magna cum laude) in electrical and computer engineering with honors from Cornell University.

Presentations

Introducing the AI Fairness 360 toolkit Tutorial

Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

Benjamin Vigoda is the CEO of Gamalon. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor, a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing, and a cofounder of Design That Matters, a not-for-profit that, for the past decade, has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. Lyric Semiconductor was named one of the “50 most innovative companies” by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Lyric was successfully acquired by Analog Devices, and Lyric’s products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. Ben has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation and National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. He’s serving on the DARPA Information Science and Technology (ISAT) steering committee. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.

Presentations

Continuous improvement of chat, social, and survey interactions using AI “idea analysis” (sponsored by Gamalon) 40-minute session

How does customer experience and digital marketing know what customers are saying in human chat, bot chat, survey, or social interactions? The first step is to deeply analyze customer conversations. Ben Vigoda explains how a new generation of AI technology makes it possible to extract the ideas contained in text to summarize, organize, and display for analysis.

Sumeet Vij is a director in the Strategic Innovation Group (SIG) at Booz Allen Hamilton, where he leads multiple client engagements, research, and strategic partnerships in the field of AI, digital personalization, recommendation systems, chatbots, digital assistants, and conversational commerce. Sumeet is also the practice lead for next-generation digital experiences powered by AI and data science, helping with the large-scale analysis of data and its use to quickly provide deeper insights, create new capabilities, and drive down costs.

Presentations

Building intelligent conversational agents with transfer learning and cognitive automation 40-minute session

Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You'll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation.

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

Presentations

Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData) 40-minute session

Nanda Vijaydev shares practical examples of—and lessons learned from—ML/DL use cases in financial services, healthcare, and other industries. You'll learn how to quickly deploy containerized multinode environments for TensorFlow and other ML/DL tools in a multitenant architecture either on-premises, in the cloud, or in a hybrid environment.

One-click deployment for containerized ML and DL environments Intel® AI Builders Showcase

Nanda Vijaydev explains how to spin up instant ML/DL environments using containers—all while ensuring enterprise-grade security and performance. Find out how to provide your data science teams with on-demand access to the tools and data they need, whether on-premises or in the cloud.

Maja Vukovic is a research manager and a research staff member at the IBM T. J. Watson Research Center. Maja’s research expertise is in IT service innovation, AI planning, crowdsourcing technologies, API ecosystems innovation, and social media applications for disaster management. Maja leads the cognitive service management team, focusing on AI-driven insights and automation in hybrid cloud systems. Maja has received numerous IBM Outstanding Technical Achievement Awards and IBM Research awards for her technical leadership. She’s a member of the IBM Academy of Technology and a senior member of the IEEE. In 2018, she was recognized with the IEEE TCSVC Award for Women in Services Computing. She’s an IBM Master Inventor, with over 160 patents filed and 50 granted. She’s also the author of over 90 papers in top international conferences and journals. Maja is a cofounder of a number of workshops, including Enterprise Crowdsourcing and Ubiquitous Crowdsourcing and Social Web for Disaster Management, collocated with leading international conferences. Previously, Maja was a research scientist at the Mercedes-Benz Research and Technology Center in Palo Alto, working in the field of telematic services. Maja holds a PhD from the University of Cambridge, UK, for her work on context aware service composition using AI planning, as well as an MSc from International University in Germany and a BSc from the University of Auckland, New Zealand.

Presentations

Toward automated AI planning in enterprise: Opportunities and challenges 40-minute session

AI planning offers an opportunity to drive reasoning about action trajectories to help build automation. Maja Vukovic demos an application of AI planning for the migration of legacy infrastructure to the cloud, based on real-world examples and data, and discusses challenges in adopting AI planning solutions in the enterprise.

Lucy X. Wang is a senior data scientist at BuzzFeed working on machine learning tools for optimizing audience reach and engagement. She performed research on social networks and information diffusion at Columbia University.

Presentations

Media meets AI: How we give superpowers to BuzzFeed's social curators 40-minute session

As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. The company first built publishing tools that let people work more efficiently, then built artificial intelligence tools that let people work more intelligently. Join Lucy Wang and Swara Kantaria to learn more about this evolution.

Ted Way is a senior program manager on the Azure Machine Learning engineering team at Microsoft, where he works on bringing machine learning to the edge and hardware acceleration of AI. He’s passionate about telling the story of how AI will empower people and organizations to achieve more and has been invited as a keynote speaker for two Microsoft partner conferences. He has twice received the Microsoft Executive Briefing Center’s Distinguished Speaker Award, awarded to only five out of over 1,000 speakers. He holds BS degrees in electrical engineering and computer engineering, MS degrees in electrical engineering and biomedical engineering, and a PhD in biomedical engineering from the University of Michigan—Ann Arbor. His PhD dissertation was on “spell check for radiologists,” a computer-aided diagnosis (CAD) system that uses image processing and machine learning to predict lung cancer malignancy on chest CT scans.

Presentations

Fast (and cheap) AI accelerated on FPGAs 40-minute session

Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave.

Nick Werstiuk is Director of OM for Spectrum Computing and PowerAI. He and his team are responsible for the vision and roadmap of the Spectrum Computing portfolio of offerings, driving expansion from HPC and Analytics into AI and Cloud. Since joining Platform in 2004 he has led a range of new product, and corporate strategy initiatives, which included the acquisition of Platform by IBM.

Presentations

Using artificial intelligence and machine learning for risk modeling in financial services (sponsored by IBM Watson) 40-minute session

Successful financial institutions like Morgan Stanley are growing more committed to efficiency and investing heavily in tools to do so. Marcelo Labre explains how the computing power and AI-readiness of IBM Power Systems enables a new journey of exploration and new possibilities in AI/ML use cases in finance.

Aric Whitewood is cofounder of WilmotML, a machine learning and macroeconomics-focused investment and advisory firm. He is also an honorary senior lecturer in the Computer Science Department of University College London (UCL), for which he runs several research programs with UCL students on machine learning topics. Aric focuses on the combination of neuroscience, artificial intelligence, and investing, with a particular emphasis on developing investment systems that are transparent (enabling trust in investment decisions) and that operate on longer timescales than has historically been the case with algorithmic systems (typically months). Previously, he was head of data science at Credit Suisse Zurich, where he ran AI projects across a number of businesses and geographic locations and served as the bank’s subject-matter expert in machine learning, regularly presenting to both the bank’s management and its major clients. He holds a PhD in electronic engineering from UCL.

Presentations

GAIA: The Global AI Allocator 40-minute session

Aric Whitewood details WilmotML's research on the application of AI to investment management and offers an overview of the company's prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018.

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 give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs.

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

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 give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs.

Haizi Yu is a fifth-year doctoral student in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where he’s a research assistant in the Coordinated Science Laboratory. His research interest spans automatic concept learning, interpretable machine learning, automatic knowledge discovery, optimization, computational creativity, and music intelligence. He holds an MS in computer science from Stanford University and a BS from the Department of Automation at Tsinghua University.

Presentations

Automatic concept learning 40-minute session

Can an AI learn the laws of music theory from sheet music in the same human-interpretable form as a music theory textbook? How little prior knowledge is needed to do so? Haizi Yu considers questions like these as he walks you through developing a general framework for automatic concept learning.

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

How to use AI to improve efficiency, safety, and patient satisfaction in radiology 40-minute session

Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals.

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

Presentations

Responsible AI practices: A technical demonstration 40-minute session

The development of AI is creating new opportunities to improve the lives of all people. It's also raising new questions about ways to build fairness, interpretability, and other moral and ethical values into these systems. Using Jupyter and TensorFlow, Andrew Zaldivar shares hands-on examples that highlight current work and recommended practices toward the responsible development of AI.

Matthew Zeiler is the founder and CEO of Clarifai, where he is applying his pioneering research in applied artificial intelligence to create developer-friendly products that allow enterprises to quickly and seamlessly integrate AI into their workflows and customer experiences. An artificial intelligence expert, Matt led groundbreaking research in computer vision, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, that has propelled the image recognition industry from theory to real-world practice.

Presentations

Closing the loop on AI: How to maintain quality long-term AI results 40-minute session

At the core of today's problems with image classification and deep learning lies one fundamental truth: most AI systems operate by choosing the path of least resistance, not the path of highest long-term quality. Matt Zeiler discusses Clarifai's approach to closing the loop on AI and the techniques it employs to counter the AI quality regression phenomenon.

Yi Zhuang is a senior staff machine learning software engineer at Twitter, where he leads a team building a platform for working with ML models. He works on uniting ML practitioners around a single ML platform, bringing consistency to ML practices at Twitter. Previously, Yi led a team to develop a trillion-document-scale distributed search engine at Twitter. Yi holds an MS in computer science from Carnegie Mellon University. He loves cats and enjoys pondering over all things technical and logical.

Presentations

Unifying Twitter around a single ML platform 40-minute session

Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.

Yulia Zvyagelskaya is a data scientist at Dow Jones, where she’s responsible for the development and implementation of machine learning applications. Yulia has developed several AI-driven projects in the fields of computer vision and natural language processing. She holds master’s degrees in NLP (computational linguistics and artificial intelligence) and big data management and analytics. Yulia has won several international artificial intelligence and big data competitions.

Presentations

Deep learning for third-party risk identification and evaluation at Dow Jones 40-minute session

Companies have a strong need for complying with anti-money laundering, antibribery, corruption, and economic sanctions regulation in mitigating third-party risk. Yulia Zvyagelskaya and Victor Llorente highlight how Dow Jones Risk & Compliance uses deep learning and NLP for efficient compliance solutions.