Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Speakers

New speakers are added regularly. Please check back to see the latest updates to the agenda.

Filter

Search Speakers

Brad Abrams is the group product manager for the Google Assistant platform. Previously, Brad was the product unit manager of the Application Framework team at Microsoft. Brad is the author of several books from Addison Wesley, including Framework Design Guidelines. He holds a BS in computer science from North Carolina State University.

Presentations

Building conversational experiences Session

Brad Abrams explores the latest design and development techniques for building natural language interfaces and draws on the Google Assistant, Actions on Google, and API.AI as examples to explore conversational UI best practices.

Amir Banifatemi leads artificial intelligence at XPRIZE Foundation and is managing partner of K5 Ventures. Amire has 25 years of experience in development and growth of emerging and transformative technologies. He began his career at the European Space Agency and then held executive positions at Airbus, AP-HP, and the European Commission Division for Information Society and Healthcare. He has contributed to the formation of more than 10 startups with emphasis on predictive technologies, the IoT, and healthcare. Amir is a guest lecturer and an adjunct MBA professor at UC Berkeley, Chapman University, Claremont McKenna College, UC Irvine, and HEC Paris. He holds a master’s degree in electrical engineering from the University of Technology of Compiègne, a PhD in system design and cognitive sciences from the University Paris Descartes, and an MBA from HEC Paris.

Presentations

XPRIZE invites workshop Session

A member of the board of advisors from the AI XPRIZE challenge leads a workshop on applied AI and its impact on society.

Ron Bodkin is CTO Architecture and Services for Teradata. Ron is responsible for leading the global emerging technology team focusing on Artificial Intelligence, GPU and Blockchain. Responsible for leading global consulting teams for enterprise analytics architectures combining Hadoop and Spark, public cloud and traditional data warehousing, driving strategic pillar for Teradata.

Previously, Ron was the founding CEO of Think Big Analytics. Think Big provides end to end support for enterprise Big Data including data science, data engineering, advisory and managed services and frameworks such as Kylo for enterprise data lakes. Think Big was acquired by Teradata in 2014 and was the leading global pure play big data services firm.

Previously, Ron was VP Engineering at Quantcast where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making. Prior to that, Ron was Founder of New Aspects, which provided enterprise consulting for Aspect-oriented programming. Ron was also Co-Founder and CTO of B2B applications provider C-Bridge, which he led to team of 900 people and a successful IPO. Ron graduated with honors from McGill University with a B.S. in Math and Computer Science. Ron also earned his Master’s Degree in Computer Science from MIT, leaving the PhD program after presenting the idea for C-bridge and placing in the finals of the 50k Entrepreneurship Contest.

Presentations

Fighting financial fraud at Danske Bank with artificial intelligence Session

Fraud in banking is an arms race with criminals using machine learning to improve their attack effectiveness. Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way.

Lashon B. Booker is a senior principal scientist in MITRE’s Information Technology Technical Center. Previously, he worked at the Naval Research Laboratory, where he was eventually promoted to section head of the Intelligent Decision Aids section in the Navy Center for Applied Research in Artificial Intelligence. Lashon has published numerous technical papers in the areas of machine learning, probabilistic methods for uncertain inference, and distributed interactive simulation. He serves on the editorial boards of Evolutionary Intelligence and the Journal of Machine Learning Research and previously served as an associate editor of Adaptive Behavior, on the editorial boards of Machine Learning and Evolutionary Computation. He also regularly serves on the program committees for conferences in these areas. Lashon holds a PhD in computer and communication sciences from the University of Michigan.

Presentations

"Fairness Cases" as an accelerant and enabler for AI adoption Session

Lack of confidence in the fairness of an AI-based system will limit support for its use and likely preclude adoption, even if that adoption could provide significant benefits. Chuck Howell and Lashon Booker explore tools, techniques, and best practices from the safety-critical software community that can be adapted to provide a “Fairness Case” framework to address fairness concerns effectively.

Joseph Bradley is a software engineer working on machine learning at Databricks. Joseph is an Apache Spark committer and PMC member. Previously, he was a postdoc at UC Berkeley. Joseph holds a PhD in machine learning from Carnegie Mellon University, where he focused on scalable learning for probabilistic graphical models, examining trade-offs between computation, statistical efficiency, and parallelization.

Presentations

Integrating deep learning libraries with Apache Spark Session

Joseph Bradley and Xiangrui Meng share best practices for integrating popular deep learning libraries with Apache Spark, covering cluster setup, data ingest, configuring clusters, and monitoring jobs. Joseph and Xiangrui will demonstrate these techniques using Google’s TensorFlow library.

Cormac Brick is Director of Machine Intelligence in the Movidius group at Intel Corporation, where he builds new foundational algorithms for computer vision and machine intelligence to enhance the Myriad VPU product family. Cormac contributes to internal architecture, and helps customers build products using very latest techniques in deep learning and embedded vision through a set of advanced applications and libraries. Cormac has worked with Movidius since its early days and has contributed heavily towards the design of the ISA, hardware systems design, computer vision software development and tools. Cormac has a B.Eng. in Electronic Engineering from University College Cork.

Presentations

Anaerobic AI: Developing in a data-starved environment Session

Data is the “oxygen” of the AI revolution, but access to data on a large scale remains a luxury of an elite group of tech companies, effectively creating a “data wall” blocking smaller companies. David Moloney and Xiaofan Xu explore the problem of the data wall and offer a solution: synthetic datasets.

Patrick Buehler is a senior data scientist at Microsoft Boston and has been in the field for over 10 years. His main interests are machine learning and computer vision. He holds a PhD from the VGG group at Oxford.

Presentations

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines.

Yishay Carmiel is the head of Spoken Labs, a big data analytics unit that implements bleeding-edge deep learning and machine-learning technologies for speech recognition, computer vision, NLP, and data analysis. Yishay and his team are working on state-of-the-art technologies in artificial intelligence, deep learning, and large-scale data analysis. He has 15 years’ experience as an algorithm scientist and technology leader working on building large-scale machine-learning algorithms and serving as a deep learning expert.

Presentations

Conversational AI at large scale Session

There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge. Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate trainings, and create an end-to-end product.

Pau Carré is a deep learning software engineer at Gilt. Pau has 10 years of experience encompassing software security, IT management, microwave networks profiling, quality engineering, and deep learning and functional programming for the fashion industry. Over his career, he has lived and worked in cosmopolitan Barcelona, paradisiac Mallorca, and magnificent Vienna, and is now based in welcoming Dublin.

Presentations

Deep learning in the fashion industry Session

Pau Carré explains how Gilt is reshaping the fashion industry by leveraging the power of deep learning and GPUs to automatically detect similar products and identify facets in dresses.

Roger Chen is currently the program co-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, having spent a past life as an engineer and scientist prior to working in venture capital. He developed novel nanotechnology as a PhD researcher at UC Berkeley and spent stints as an engineer with Oracle, EMC and Vicor. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the first day of keynotes.

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the second day of keynotes.

Thursday opening remarks Keynote

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

Wednesday opening remarks Keynote

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

Soumith Chintala is a research engineer at Facebook AI Research, where he works on generative models and high-performance computing.

Presentations

Dynamic deep learning: A paradigm shift in AI research and tools Session

Soumith Chintala discusses paradigm shifts in cutting-edge AI research and applications such as self-driving cars, robots, and game playing.

Jennifer Chu-Carroll is a research scientist at Elemental Cognition, where she focuses on natural language semantics and dialogue management. Previously, Jennifer was a research staff member and manager at the IBM T.J. Watson Research Center, where her most notable accomplishment was serving as a key technical lead on the Watson project, in which a high-performing question-answering system defeated the two best human players at the game of Jeopardy!, and a member of the technical staff at Lucent Technologies Bell Laboratories focusing on spoken dialogue management. Throughout her career, Jennifer has maintained a strong focus on research and development in natural language processing and related areas. She has published extensively in top conferences and journals and is very engaged in her research community. Jennifer served as general chair of NAACL-HLT 2012, program committee cochair of NAACL-HLT 2006, as area chairs and program committees of many key conferences, and on the editorial boards of multiple journals. She holds a PhD in computer science from the University of Delaware.

Presentations

Beyond the state of the art in reading comprehension Session

Why is reading comprehension hard? Jennifer Chu-Carroll offers an overview of current approaches, explaining where they fall short and what our ultimate expectations should be.

Anca is an Assistant Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where she focuses on algorithms for human-robot interaction — algorithms that move beyond the robot’s function in isolation, and generate robot behavior that also accounts for interaction and coordination with end-users. The lab works across different applications, from assistive robots, to manufacturing, to autonomous cars, and draws from optimal control, planning, estimation, learning, and cognitive science. Anca also helped found and serves on the steering committee for the Berkeley AI Research (BAIR) Lab, and is a co-PI of the Center for Human-Compatible AI.

Presentations

Keynote by Anca Dragan Keynote

Keynote by Anca Dragan

Douglas Eck is a Research Scientist at Google working in the areas of music and machine learning. Currently he is leading Magenta, a Google Brain project to generate music, video, images and text using deep learning and reinforcement learning. A main goal of Magenta is to better understanding how AI can enable artists and musicians to express themselves in innovative new ways. Before Magenta, Doug led the Google Play Music search and recommendation team. Before joining Google in 2010, Doug was an Associate Professor in Computer Science at University of Montreal (MILA lab) where he worked on expressive music performance and automatic tagging of music audio.

Presentations

Keynote by Doug Eck Keynote

Keynote by Doug Eck

Jana Eggers is a tech executive focused on products and the messages surrounding them. Jana has started and grown SMBs and led large organizations within enterprises. She supports, subscribes to, and contributes to customer-inspired innovation, systems thinking, Lean analytics, and autonomy, mastery, and purpose-style leadership. Jana’s software and technology experience includes technology and executive positions at Intuit, Blackbaud, Basis Technology (internationalization technology), Lycos, American Airlines, Los Alamos National Laboratory (computational chemistry and supercomputing), Spreadshirt (customized apparel ecommerce), and acquired startups that you’ve never heard of. Jana is a frequent speaker, writer, and CxO educator on innovation, change, and technology. Jana holds a bachelor’s degree in mathematics and computer science from Hendrix College in Arkansas and pursued graduate studies in computer science at Rensselaer Polytechnic.

Presentations

From ∞ to 8: Translating abstract AI into real numbers for business Session

AI has infinite possibilities, but to be adopted by businesses beyond R&D, these solutions must show results. The challenge is that AI often presents new opportunities that aren't easily quantified. Jana Eggers shares lessons learned while taking AI from ideas to results-delivering production solutions at various organizations, including Global 500 enterprises, tech companies, and nonprofits.

Rana el Kaliouby is cofounder and CEO of Affectiva—a pioneer in emotion AI, the next frontier of artificial intelligence—where she leads the company’s award-winning emotion recognition technology, built on a science platform that uses deep learning and the world’s largest emotion data repository of nearly 4.9 million faces analyzed from 75 countries, amounting to more than 50 billion emotion data points. Previously, Rana was a research scientist at MIT Media Lab, where she spearheaded the applications of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including The New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, she was recognized by TechCrunch as a women founder who crushed it in 2016, by Entrepreneur magazine as one of the 7 most powerful women to watch in 2014, and on Ad Age’s 40 under 40 list. Rana has also been inducted into the Women in Engineering Hall of Fame and is a recipient of Technology Review’s 2012 Top 35 Innovators Under 35 award and Smithsonian magazine’s 2015 American Ingenuity Award for Technology. Rana holds a BSc and MSc in computer science from the American University in Cairo and a PhD from the Computer Laboratory at the University of Cambridge.

Presentations

The science and applications of the emerging field of artificial emotional intelligence Session

Emotion AI is a branch of artificial intelligence that brings emotional intelligence to AI systems. Rana el Kaliouby reviews the state of emotion AI, its commercial applications, its underlying deep learning methods, and the research roadmap, which includes multimodal emotion recognition and the idea of an emotion chip.

Susan Eraly is a software engineer at Skymind, where she contributes to Deeplearning4j. Previously, Susan worked as a senior ASIC engineer at NVIDIA and as a data scientist in residence at Galvanize.

Presentations

Neural networks for time series analysis using Deeplearning4J 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data.

Guy Ernest is a senior manager on the Solutions Architecture team at Amazon Web Services, where he helps customers take their first steps in the Amazon cloud and works with them on advanced use cases. Guy specializes in mobile development, big data, analytics, and machine learning. Previously, he founded startups in mobile search, personalization, and big data analytics.

Presentations

Scalable deep learning on AWS using Apache MXNet Session

AWS is democratizing AI, helping you build deep learning systems in any scale, in any team size and skill, and for every use case. Guy Ernest discusses the state of deep learning, the tools that can take advantage of its power, and best practices for building successful businesses in the cloud, including data handling, models learning, deployment, and integration to other parts of the business.

Tim Estes is the founder and CEO of Digital Reasoning. Tim first began applying AI to major human challenges in the shadow of 9/11. Technology developed by his firm was used to help United States defense in the fight against terrorism and in the field in Afghanistan. He is a leading influencer, advocate, and speaker for the use of technology and analytics to achieve social good in industry, government, healthcare, and academia. His guiding mission is to create technology that learns and gets smarter, with the goal of helping humans see the world more clearly so they can solve the world’s toughest problems. Tim holds a BA in philosophy from the University of Virginia, where he focused on the philosophy of language, mathematical logic, semiotics, epistemology, and phenomenology.

Presentations

We found a way Session

As AI moves from concept to reality, debates about ethics are evolving into excitement and the desire to learn more about AI and its promise of a better world. Digital Reasoning's Tim Estes discusses two customer use cases: Nasdaq, which found a way to use AI to help safeguard financial markets, and Thorn, which found a way to use AI to combat human trafficking and rescue children.

David Ferrucci is the founder of Elemental Cognition, a company focused on creating AI systems that autonomously learn from human language and interaction to facilitate complex decision making in areas ranging from healthcare to economics, and leads the Systematized Intelligence Lab at Bridgewater Associates, where he explores the application of AI in building explicable data-driven systems for optimal management and people analytics. An award-winning artificial intelligence researcher, David started and led the IBM Watson team from its inception in 2006 to its celebrated success in 2011, when Watson defeated the greatest Jeopardy players of all time—a landmark in AI. Previously, he pioneered Watson’s applications in health, which helped lay the technical foundation for a new Healthcare division at IBM, and led numerous projects prior to Watson, including AI systems for manufacturing, automated configuration, document assembly, and open source software and standards for large-scale text and multimodal analytics.

David has over 50 patents and published papers in the areas of AI, automated reasoning, NLP, intelligent systems architectures, automatic text generation, and automatic question answering. He has given keynotes at highly distinguished events around the world, including many of the top computing conferences. David has been interviewed by media and organizations ranging from the New York Times to Bloomberg West to the Computer History Museum. He is an IBM fellow and has won many awards for his work, including the Chicago Mercantile Exchange’s Innovation Award and the AAAI Feigenbaum Prize. David holds a PhD in computer science from Rensselaer Polytechnic Institute.

Presentations

Keynote by David Ferrucci Keynote

Keynote by David Ferrucci

Yarin Gal is a research fellow in computer science at St Catharine’s College at the University of Cambridge and a part-time fellow at the Alan Turing Institute, the UK’s national institute for data science. Yarin is working toward a PhD within the Cambridge Machine Learning group under Zoubin Ghahramani, funded by the Google Europe doctoral fellowship. Previously, he was a software engineer at IDesia Biometrics, where he developed code and UI for mobile platforms. Yarin holds an undergraduate degree in mathematics and computer science from the Open University in Israel and a master’s degree in computer science from Oxford under Phil Blunsom.

Presentations

Bayesian deep learning Session

Yarin Gal shares a new theory linking Bayesian modeling and deep learning and demonstrates the practical impact of the framework with a range of real-world applications. Yarin also explores open problems for future research—problems that stand at the forefront of this new and exciting field.

Codruta Gamulea is a business developer and data strategist with a passion for using AI technology to improve the quality of journalism. Codruta leads Orbit, an AI technology venture by leading Norwegian digital studio and venture builder Bakken & Bæck that provides artificial intelligence technology as a service. Orbit NLP uses advanced machine-learning algorithms to automatically categorize, enrich and tag large pieces of text-based content. Previously, Codruta led data strategy at Amedia, Norway’s largest local news publisher, where she oversaw the company’s efforts to monetize data for its over 70 titles. Codruta has over 12 years’ consulting experience at Accenture. She holds a master’s degree from BI Norwegian School of Management and studied news reporting at Harvard’s Nieman Journalism Lab.

Presentations

The AI-powered newsroom Session

The promise of AI in the newsroom is contradictory: NLG revolutionizes news writing, but robot journalists threaten jobs; NLP improves fact-checking but requires investments that slimmed-down newsrooms cannot afford. Drawing on Norwegian AI startup Orbit’s experience, Codruta Gamulea explains how AI can help solve the industry resource constraints and improve the quality of journalism.

Garrett Goh is a scientist in the Advanced Computing, Mathematics, and Data division at the Pacific Northwest National Lab (PNNL), where he holds the Pauling fellowship, which supports his research combining deep learning and artificial intelligence with traditional computational chemistry applications. His current interest is in AI-assisted computational chemistry—the application of deep learning to predict chemical properties and the discovery of new chemical insights, while using minimal expert knowledge. Previously, Garrett held a Howard Hughes Medical Institute (HHMI) fellowship, which supported his PhD in computational chemistry at the University of Michigan.

Presentations

AI-assisted computational chemistry: Predicting chemical properties with minimal expert knowledge Session

Garrett Goh demonstrates how to use deep learning to construct computational chemistry models that compare favorably to existing state-of-the-art models developed by expert practitioners—with virtually no expert knowledge—proving the potential of AI assistance to accelerate the scientific discovery process from a typical span of years to a matter of months.

Laura Graesser is a graduate student at New York University, where she is working toward a masters’ degree in computer science with a focus on machine learning. In her spare time, Laura enjoys experimenting with and writing about machine-learning techniques. Laura is particularly interested in neural networks and their application to computer vision problems, cross-fertilization between computer vision and NLP, and the representations perspective.

Presentations

Introduction to neural networks with Keras Tutorial

Laura Graesser offers a hands-on introduction to neural networks using the popular Python library Keras, focusing on building intuition for the core components of a neural network and what it means for a network to “learn.” You'll also get the opportunity to build and train your own network.

Eric Greene is a senior architect for Wells Fargo Digital Innovation labs, where he brings technical innovation to fruition by working with business and technical leaders across the organization. Eric has recently focused on cognitive computing applied to different domains within financial services and has designed and developed systems for real-time fraud detection, collections customer segmentation and automated prescription, customer account balance forecasting, and internal operations anomaly detectors. Eric has been with Wells Fargo for 15 years working on a variety of applications within the Architecture and Development groups.

Presentations

Deep learning and predictive payments in Wells Fargo AI Labs Session

Next-generation payment applications will soon be intelligent enough to automatically schedule payments for customers effortlessly. Eric Greene explores how Wells Fargo AI Labs developed accurate predictive models leveraging deep learning and terabytes of transaction history data that propose payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services.

Nadeem Gulzar is the head of advanced analytics and architecture at Danske Bank Group, a Nordic bank with strong roots in Denmark and a focus on becoming the most trusted financial partner in the Nordics. Nadeem has taken the lead in establishing advanced analytics and big data technologies within Danske. Previously, Nadeem worked with Credit and Marketrisk, where he headed a program to build-up capabilities to calculate risk using Monte Carlo simulation methods. Nadeem holds a BS in computer science, mathematics, and psychology and a master’s degree in computer science, both from Copenhagen University.

Presentations

Fighting financial fraud at Danske Bank with artificial intelligence Session

Fraud in banking is an arms race with criminals using machine learning to improve their attack effectiveness. Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way.

Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him.

Presentations

Running TensorFlow at scale in the cloud Session

Moving the heavy lifting of machine learning to the cloud is a great way to get large speed-ups. Yufeng Guo walks you through this process in detail so that you'll be ready to scale your own training and prediction services.

Scaling machine learning with TensorFlow Tutorial

TensorFlow is an increasingly popular open source machine intelligence library that is especially well suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. Yufeng Guo and Amy Unruh offer an introduction to TensorFlow concepts and walk you through using CloudML to do distributed training and scalable serving of your models.

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. Previously, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. Kris currently 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. Kris holds a PhD from Yale.

Presentations

Here and now: Bringing AI into the enterprise Tutorial

Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making, focusing on how they can be used, the requirements for doing so, and the expectations for their effectiveness.

What, how, and why: The dynamic of advanced NLG Session

Kristian Hammond offers an overview of advanced natural language generation (NLG), a subfield of artificial intelligence, and the assorted technical systems involved with this emerging technology along with the mechanisms that drive them.

Some are cognitive scientists; others are computer scientists and engineers. Mark Hammond is a cognitive entrepreneur bringing together both fields along with business acumen. He has a deep passion for understanding how the mind works, combined with an understanding of own human nature, and turns that knowledge into beneficial applied technology. As the founder and CEO of Bonsai, Mark is enabling AI for everyone. Mark has been programming since the first grade and started working at Microsoft as an intern and contractor while still in high school. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.

Presentations

Programming your way to explainable AI Session

As interactive and autonomous systems make their way into nearly every aspect of our lives, it is crucial to gain more trust in intelligent systems. Mark Hammond explores the latest techniques and research in building explainable AI systems. Join in to learn approaches for building explainability into control and optimization tasks, including robotics, manufacturing and logistics.

Tom Hanlon is currently an instructor at Cloudera where he delivers courses on the wonders of the hadoop ecosystem.

Before beginning his relationship with hadoop and large distributed data, he had a happy and lengthy relationship with MySQL with a focus on web operations.

He has been a trainer for MySQL, Sun , Percona.

Presentations

Neural networks for time series analysis using Deeplearning4J 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data.

Timothy J. Hazen is a principal data science manager in Microsoft’s Cloud and Enterprise Data group, where he leads a data science team in the development of customer-facing machine learning capabilities for the Microsoft Azure platform, primarily in the areas of image processing and natural language processing. Timothy has also developed natural language technology used within Microsoft’s Bing and Cortana products. Previously, he spent six years as a member of the Human Language Technology group at MIT’s Lincoln Laboratory and nine years as a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory. Timothy holds an SB, SM, and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.

Presentations

Customizing state-of-the-art deep learning models for new computer vision solutions Session

Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to adapt Microsoft's start-of-the-art DNNs for use in your own computer vision solutions.

Abraham Heifets is the CEO and co-founder of Atomwise, which uses machine learning to help discover new medicines. Abraham was a Massey Fellow at the University of Toronto and a Fellow of the Ontario Brain Institute. His doctoral work applied machine learning and classical artificial intelligence techniques to organic synthesis planning, a long-standing challenge in chemistry. Previously, Abraham researched high-performance data processing at IBM’s T.J. Watson Research Center and helped develop the strategy and control AI system of the world-champion robotic soccer team at Cornell University. Abraham created SCRIPDB, then one of the largest public databases of patented chemical structures, and LigAlign, a protein analysis tool used by researchers in 70 countries. He is an author on 19 papers, patents, and patent applications, and was named Time Magazine’s Person of the Year in 2006.

Presentations

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery Session

Deep convolutional neural networks (neural nets with a constrained architecture that leverages the spatial and temporal structure of the domain they model) achieve the best predictive performance in areas such as speech and image recognition. Such neural networks autonomously discover and hierarchically compose simple local features into complex models.

Michael B. Henry is the founder and CEO of Isocline, a venture-backed AI hardware company that makes it easy to put powerful, local speech and vision AI into any product, from wearables to cars. Under the hood, Isocline’s tech uses new methods of computing inside of flash memory arrays to deliver the processing capabilities of 10 mobile GPUs in a small, low-cost form factor.

Presentations

Software and hardware breakthroughs for deep neural networks at the edge Session

Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment.

Qirong Ho is vice president of technology at Petuum, Inc., an adjunct assistant professor at the Singapore Management University School of Information Systems, and a former principal investigator at A*STAR’s Institute for Infocomm Research. Qirong’s research focuses on distributed cluster software systems for machine learning at big data and big model scales, with a view toward theoretical correctness and performance guarantees, as well as practical needs like robustness, programmability, and usability. Qirong also works on statistical models for large-scale network analysis and social media, including latent space models for visualization, community detection, user personalization, and interest prediction. He is a recipient of the Singapore A*STAR National Science Search Undergraduate and PhD fellowships and the KDD 2015 Doctoral Dissertation Award (runner up).

Presentations

Software architectures for building enterprise AI Session

Petuum, Inc. builds software that lets enterprises develop AI solutions in multiple programming languages and deploy them at scale and with high performance to internal, private computing resources that include a heterogeneous mix of workstations, clusters, CPUs, and GPUs. Qirong Ho outlines the architectural design choices and technical foundation needed to achieve these targets.

Chuck Howell is the chief engineer for intelligence programs and integration at the MITRE Corporation, where he serves as the senior technical focal point for facilitating how MITRE addresses its intelligence customers’ key technical challenges. He contributes to oversight of technical activities across MITRE’s Intelligence programs, including participation in the development and integration of MITRE’s research program, direct technical support to projects, and review of technical aspects of Intelligence Community programs. Chuck has served as the chair of a DARPA panel refining a research agenda for building trustworthy systems, chair of a three-FFRDC study for DUSD (S&T) to develop a roadmap for S&T in software engineering, the MITRE lead for a team (MITRE, Aerospace, Johns Hopkins APL) that developed a recommended set of mission assurance program guidelines for the Missile Defense Agency, and a principal investigator on multiple MITRE research programs addressing various aspects of software assurance, safety cases, autonomy, and error handling. He was a member of the Institute of Electrical and Electronics Engineers (IEEE) Software Engineering Body of Knowledge industrial advisory board.

Presentations

"Fairness Cases" as an accelerant and enabler for AI adoption Session

Lack of confidence in the fairness of an AI-based system will limit support for its use and likely preclude adoption, even if that adoption could provide significant benefits. Chuck Howell and Lashon Booker explore tools, techniques, and best practices from the safety-critical software community that can be adapted to provide a “Fairness Case” framework to address fairness concerns effectively.

Anmol Jagetia is a software engineer at Media.net interested in web technologies, open source software, data science, shipping cool products, and introducing people to technology. He has authored popular open source projects, including Flatabulous, which received over 2.2K stars on GitHub and has received close to 1 Million downloads. Anmol holds a degree from the prestigious Indian Institute of Information Technology, Allahabad. He was part of HPCC as a Google Summer of Code Student in 2015 and interned on a scholarship at the prestigious Max Planck Institute for Software Systems, Germany in 2016. In his free time, he enjoys traveling, reading, and playing his guitar.

Presentations

Building game bots using OpenAI’s Gym and Universe Session

Anmol Jagetia explains how to use OpenAI's Gym and Universe to design bots that can become extremely smart using reinforcement learning. You'll create a bot that uses reinforcement learning to beat games and learn how to reuse code to beat a set of games that includes Atari classics (Pac-Man or Pong), a Candy Crush clone, and a racing game.

Oliver Jojic is a distinguished researcher in the Comcast Labs DC research group, where he leads deep learning and NLP research efforts.

Presentations

How AI powers the Comcast X1 voice interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and is how millions of its customers access their content on the TV. Jan Neumann, Ferhan Ture, and Oliver Jojic explain how AI enables Comcast to understand what you are looking for when you talk to the X1 voice remote and how Comcast scaled the voice interface to answer millions of voice queries every single night.

Arthur Juliani is a machine learning engineer at Unity Technologies. Arthur is a researcher working at the intersection of cognitive neuroscience and deep learning. He is currently working toward a PhD at the University of Oregon.

Presentations

Deep reinforcement learning tutorial Tutorial

Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments has been deep reinforcement learning. Arthur Juliani covers RL from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks.

Charles (Charlie) Killam, LP.D. is a Certified Instructor and Curriculum Designer at NVIDIA’s Deep Learning Institute. Though Charlie works across all verticals, his efforts focus primarily on the application of deep neural networks (DNNs) in the healthcare space. Prior to NVIDIA, Charlie’s experience includes delivering a data analytics bootcamp for Northeastern University; a geospatial Tableau project for Stanford University; and, working with MADlib – an open-source, machine learning algorithm library while at Pivotal.

Presentations

NVIDIA Deep Learning Institute bootcamp 2-Day Training

In this two-day workshop blending lecture and hands-on, real-world exercises, NVIDIA Deep Learning Institute-certified instructors will walk you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to talking on real-word problems using deep learning.

Jason Laska leads the machine learning efforts at Clara Labs. Previously, Jason spearheaded the computer vision program at Dropcam (acquired by Google in 2014), developing massive scale online vision systems for the product. Jason holds a PhD in electrical engineering from Rice University, where he focused on inverse problems, dimensionality reduction, and optimization. He briefly dabbled in publishing as a cofounder and editor of Rejecta Mathematica, a publication for previously rejected mathematical articles.

Presentations

Strategies for integrating people and machine learning in online systems Session

Clara Labs is fusing machine learning (ML) with distributed human labor for natural language tasks. The result is a virtuous cycle: ML predictions improve workers’ efficiency, and workers help improve prediction models. Jason Laska explores the challenges of building a real-time(ish) knowledge workforce, how to integrate automation, and key strategies Clara Labs learned that enable scale.

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Presentations

Deep learning with TensorFlow 2-Day Training

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

Yonghua Lin is the founder and leader of IBM’s SuperVessel innovation cloud, a senior member of the technical staff, and senior manager of cognitive systems and cloud in IBM Research. Yonghua has worked on system architecture, the cloud, and cognitive platform research for more than 15 years. She was the initiator of mobile infrastructure in the cloud (now network function virtualization) and led the IBM team that built up the first optimized cloud for 4G mobile infrastructure. Yonghua has spoken widely at industry events, including ITU and Mobile World Congress, holds more than 40 patents granted worldwide, and has authored papers for top conferences and journals.

Presentations

VisionBrain: Enabling deep learning-based visual analysis in edge and cloud environments Session

Yonghua Lin leads a deep dive into VisionBrain, a deep learning system from IBM for image and video analysis in both edge and cloud environments, exploring its system design, performance optimization, and large-scale capability for training and inference.

Shaoshan Liu is the cofounder and president of PerceptIn, working on developing the next-generation robotics platform. Previously, Shaoshan worked on autonomous driving and deep learning infrastructure at Baidu USA. He holds a PhD in computer engineering from the University of California, Irvine.

Presentations

The road to affordable AI-capable products Session

It is imperative to make high-profile technologies like AI affordable in order for these technologies to proliferate and to benefit the general public. Shaoshan Liu discusses PerceptIn's road to affordable AI-capable products.

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

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the first day of keynotes.

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the second day of keynotes.

Thursday opening remarks Keynote

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

Wednesday opening remarks Keynote

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

Nikita Lytkin is an engineering and data science leader and advisor. Nikita currently leads machine-learning teams building new monetization products at Facebook. Previously, Nikita led teams of machine-learning engineers and data scientists at LinkedIn working on making the LinkedIn News Feed highly personalized and engaging for over 400 million members and building novel data products empowering educational decision making by prospective college students. Before LinkedIn, Nikita led a team of machine learning experts in computational advertising at Quantcast. The team drove double-digit increases in performance of Quantcast’s online advertising products and company revenue by developing data-driven solutions for ad delivery, fraud detection, and campaign management. Nikita has coauthored over 20 US patent applications and continues to publish in top-tier machine-learning and data mining venues. He also advises companies on building data teams and products powered by machine learning and analytics. Nikita holds a PhD in computer science from Rutgers University, where his research focused on machine learning and its applications on textual and financial data.

Presentations

Recommending 1 billion products to 1.78 billion people on Facebook Session

Nikita Lytkin offers an overview of personalized digital advertising and explains how Facebook uses modern supervised machine-learning methods, such as factorization machines and deep neural networks, to recommend over a billion products to nearly two billion people.

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project, as well as a cofounder of Empirical Systems, a new venture-backed AI startup aimed at improving the credibility and transparency of statistical inference. Previously, Vikash cofounded a venture-backed startup based on his research that was acquired by Salesforce.com, was an advisor to Google DeepMind, and held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. He served on DARPA’s Information Science and Technology advisory board from 2010 to 2012 and currently serves on the editorial boards for the Journal of Machine Learning Research and Statistics and Computation. Vikash holds a PhD in computation, an MEng in computer science, and BS degrees in mathematics and computer science, all from MIT. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR.

Presentations

AI for structured business data Tutorial

Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians.

Probabilistic programming Tutorial

Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts.

Adam Marcus is a cofounder and CTO of B12, a company building a better future of creative and analytical work, starting with design. With Orchestra, its open source project management system for experts and machines, B12 automatically generates websites for clients (algorithmic design) and then recruits wonderful designers and art directors to fill in the details from the algorithmically generated starting points. (This summer, B12 announced the close of a $12.4M Series A funding round.) Previously, Adam was director of data at Locu, a startup that was acquired by GoDaddy. He has written widely on crowdsourcing and data management and processing, including coauthoring a book, Crowdsourced Data Management: Industry and Academic Perspectives. He is a recipient of the NSF and NDSEG fellowships and has worked at ITA, Google, IBM, and FactSet. Adam holds a PhD in computer science from MIT, where he researched database systems and human computation. In his free time, he builds course content to get people excited about data and programming.

Presentations

Human-assisted AI at B12: 10 lessons in giving humans superpowers Session

AI has a way to go before it replaces the jobs we know today. But long before AI automates away jobs, it will elevate expertise. B12 is building infrastructure that celebrates humans where they’re best while bringing machines in for the rest. Adam Marcus offers an overview of human-assisted AI and demonstrates how it is already changing creative (and fundamentally human) fields like design.

Dana Mastropole is a data scientist in residence at the Data Incubator and contributes to curriculum development and instruction. Previously, Dana taught elementary school science after completing MIT’s Kaufman teaching certificate program. She studied physics as an undergraduate student at Georgetown University and holds a master’s in physical oceanography from MIT.

Presentations

Deep learning with TensorFlow 2-Day Training

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

Jim McHugh is vice president and general manager at NVIDIA with over 25 years of experience as a marketing and business executive with startup, mid-sized, and high-profile companies. He currently leads DGX-1, the world’s first AI supercomputer in a box. Jim focuses on building a vision of organizational success and executing strategies to deliver computing solutions that benefit from GPUs in the data center. He has a deep knowledge and understanding of business drivers, market/customer dynamics, technology-centered products, and accelerated solutions. Prior to his role as VP and GM at NVIDIA, Jim held leadership positions with start-up, mid-sized and high-profile companies, including Cisco Systems, Sun Microsystems and Apple.

Presentations

NVIDIA Keynote Keynote

More details to come... *Sponsored By NVIDIA*

Ben Medlock is the head of product at SwiftKey, which was recently acquired by Microsoft. As cofounder and CTO of SwiftKey, Ben invented the intelligent keyboard for smartphones and tablets that has transformed typing on touchscreens, making it easy for everyone to create and communicate on mobile. Ben is a prominent figure in mobile technology. He is ranked among the 2015 Wired 100 and is regularly asked to deliver keynotes at conferences, including SXSW, WIRED2014, and the Global Webit Conference. Ben has a first class degree in computer science from Durham University and a PhD in natural language and information processing from the University of Cambridge.

Presentations

AI for good: Why this "scary" technology is making us more human Session

Ben Medlock explores the future of AI and explains why the possibilities are endless. . .and not at all frightening. Ben also shares key insights into how the mobile industry and beyond can better understand and support this massive worldwide movement.

Aida Mehonic is an engagement manager at ASI Data Science with a focus on financial services. Previously, she worked in investment banking for four years, most recently as a front office strategist at JPMorgan Investment Bank developing quantitative models and publishing investment research. Aida is a bronze medallist at the International Physics Olympiad. She holds a BA and MMath in mathematics from Cambridge University and a PhD in theoretical physics from UCL. Her research has been published in Nature.

Presentations

Transforming an investment firm with AI: A case study Session

Deploying AI across business functions brings benefits ranging from prosaic to game-changing, which in turn also depend on the overall digital and data maturity of the organization. Aida Mehonic shares a case study of an investment firm undergoing an AI transformation across several business units including trading, reporting, and marketing.

Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. Xiangrui has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Previously, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine-learning framework in Hadoop MapReduce. He holds a PhD from Stanford, where he worked on randomized algorithms for large-scale linear regression problems.

Presentations

Integrating deep learning libraries with Apache Spark Session

Joseph Bradley and Xiangrui Meng share best practices for integrating popular deep learning libraries with Apache Spark, covering cluster setup, data ingest, configuring clusters, and monitoring jobs. Joseph and Xiangrui will demonstrate these techniques using Google’s TensorFlow library.

Risto Miikkulainen is vice president of research at Sentient Technologies and a professor of computer science at the University of Texas at Austin. His recent research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision. Risto has published over 370 articles in these research areas and has 16 patents pending. He is an IEEE Fellow and a recipient of the 2017 Gabor Award of the International Neural Network Society. Risto holds an MS in engineering from the Helsinki University of Technology, Finland, and a PhD in computer science from UCLA.

Presentations

AI building AI: How evolutionary algorithms are revolutionizing deep learning Session

Risto Miikkulainen explains how to use massively distributed evolutionary algorithms to evolve the actual architectures of deep networks.

Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab in Berkeley.

Presentations

Ray: A distributed execution framework for emerging AI applications Session

AI applications are increasingly dynamic and interactive and work in real time. These properties impose new requirements on the distributed systems that support them. Philipp Moritz and Robert Nishihara offer an overview of Ray, a new system designed to support these emerging applications.

Jonathan Mugan is CEO of DeepGrammar. Jonathan specializes in artificial intelligence and machine learning, and his current research focuses on deep learning, where he seeks to allow computers to acquire abstract representations that enable them to capture subtleties of meaning. Jonathan holds a PhD in computer science from the University of Texas at Austin. His thesis work concerned developmental robotics and focused on the problem of how to build robots that can learn about the world in the same way that children do.

Presentations

Adding meaning to natural language processing Session

Jonathan Mugan surveys the field of natural language processing (NLP), both from a symbolic and a subsymbolic perspective, arguing that the current limitations of NLP stem from computers having a lack of grounded understanding of our world. Jonathan then outlines ways that computers can achieve that understanding.

Mohamed Musbah is vice president of product at Maluuba, a Canadian AI company that’s helping machines to think, reason, and communicate seamlessly with humans (acquired by Microsoft in January 2017). Maluuba’s technology is deployed in more than 50 million devices globally, and the company recently opened an R&D lab in Montréal, a global hub for AI research. Mo leads the development of products at Maluuba alongside the company’s language understanding and deep learning teams. Previously, Mo worked in product management at Microsoft and Facebook. He holds a BS in software engineering from the University of Waterloo.

Presentations

Bigger than bots: Machine reading and writing in enterprise Session

AI research in comprehension, communication, and modeling human-like thinking skills is heralding the dawn of literate machines. Although there has been a lot of recent hype around bots, we’re only just beginning to see the potential for language understanding. Mohamed Musbah explores key research areas and explains how they will power new products and services in language understanding.

Daryn Nakhuda is cofounder and CTO of Mighty AI, where he leads software development and data science. Previously, Daryn was a software engineer and manager at Amazon and spent many years in tech leadership roles at numerous internet startups.

Presentations

Building training data for autonomous driving Session

Autonomous vehicles must recognize objects in context, no matter the weather, time of day, or season. What does a cat in the road look like on a sunny summer day? How about on a snow-covered road at night? Daryn Nakhuda shares lessons Mighty AI has learned while creating a training dataset for autonomous driving, including workflow tips and guidance for engineers building computer vision models.

Sharan Narang is a researcher on the Systems team at Baidu’s Silicon Valley AI Lab (SVAIL), where he plays an important role in improving the performance and programmability of the deep learning framework used by researchers at SVAIL. Sharan’s research focuses on reducing the memory requirement of deep learning models, and he has explored techniques like pruning neural network weights and quantization to achieve this goal. He also proposed a DSD training flow that improved the accuracy of deep learning applications by ~5%. Previously, Sharan worked on next-generation mobile processors at NVIDIA.

Presentations

Challenges in deep learning inference Session

Artificial intelligence has had a tremendous impact on various applications at Baidu, including speech recognition and autonomous driving, although the performance requirements for all of these applications are very different. Sharan Narang outlines the challenges in inference for deep learning models and different workloads and performance requirements for various applications.

Paco Nathan leads the Learning Group at O’Reilly Media. Known as a “player/coach” data scientist, Paco led innovative data teams building ML apps at scale for several years and more recently was evangelist for Apache Spark, Apache Mesos, and Cascading. Paco has expertise in machine learning, distributed systems, functional programming, and cloud computing with 30+ years of tech-industry experience, ranging from Bell Labs to early-stage startups. Paco is an advisor for Amplify Partners and was cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise. He is the author of Just Enough Math, Intro to Apache Spark, and Enterprise Data Workflows with Cascading.

Presentations

AI within O'Reilly Media Session

Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video.

Jan Neumann manages the research group at Comcast Labs DC, where he and his team focus on using machine learning and large-scale computing for content discovery, multimedia information extraction, and big data analysis with the goal to innovate the TV and home consumer experience. Previously, he worked for Siemens Corporate Research on various computer vision related projects. He holds a PhD in computer science from the University of Maryland, College Park.

Presentations

How AI powers the Comcast X1 voice interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and is how millions of its customers access their content on the TV. Jan Neumann, Ferhan Ture, and Oliver Jojic explain how AI enables Comcast to understand what you are looking for when you talk to the X1 voice remote and how Comcast scaled the voice interface to answer millions of voice queries every single night.

Aileen Nielsen is a software engineer at One Drop, a company working on diabetes-management products. Aileen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented toward improving daily life for underserved populations—particularly groups who have yet to fully enjoy the benefits of mobile technology. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. She currently serves as a member of the New York City Bar Association’s Science and Law committee, where she chairs a subcommittee devoted to exploring and advocating for scientifically driven regulation (and deregulation) of new and existing technologies. Aileen holds degrees in anthropology, law, and physics from Princeton, Yale, and Columbia respectively.

Presentations

AI's legal history and some notions of the future Session

While the commercial use of AI in everything from hiring to medical diagnosis to work scheduling is exploding, legislation and case law alike have yet to make major statements about how AI will be treated by the American legal system. Aileen Nielsen offers a historical overview of how the law has dealt with decision-making technologies in the past and what this suggests about AI's legal future.

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

Presentations

Ray: A distributed execution framework for emerging AI applications Session

AI applications are increasingly dynamic and interactive and work in real time. These properties impose new requirements on the distributed systems that support them. Philipp Moritz and Robert Nishihara offer an overview of Ray, a new system designed to support these emerging applications.

Peter Norvig is a director of research at Google. Previously, he directed Google’s core search algorithms group. Peter is coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and coteacher of an artificial intelligence course that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a fellow of the AAAI, ACM, California Academy of Science, and American Academy of Arts & Sciences.

Presentations

Keynote by Peter Norvig Keynote

Keynote by Peter Norvig

Michael Nova is the chief innovation officer at Pathway Genomics and was a founding team member of the company. Michael is the inventor of the Pathway-IBM Watson machine-learning AI mobile application Panorama/OME as well as Pathway’s entire wellness (Pathway FIT, Healthy Weight, SkinFIT), cardiac, and mental health lines of genetic testing products. Michael has executed major corporate alliances with companies such as Medco/ESI, PepsiCo, DASA (Brazil), Biogenetika (Brazil), Achibadem (Turkey), IHH/Parkway (Singapore), IBM, Florida Hospital Group, Equinox Health, and many others. He began his scientific career as a research associate at the Salk Institute in the laboratory of Nobel Laureate Roger Guillemin, where he studied the genetics and proteomics of human growth factors and cancers.

Previously, Michael was the founder and CEO of Discovery Partners Inc., which completed a successful $150M IPO and marketed wireless drug discovery technology and radiofrequency combinatorial chemistry to large pharmaceutical companies, where he developed the original “barcode on bead” patents used by many diagnostic and DNA companies. He then founded the wireless sensory network company Graviton, where he was responsible for raising $60 million in capital from venture groups such as Kleiner Perkins and InQtel and large corporations, such as Motorola and Qualcomm. Michael won the 2005 World Economic Forum (WEF) Technology Pioneer Award and was the physician of record on the first person ever to have their entire genome sequenced by Illumina. He is a member of the IBM Watson Advisory Board, the Metagenics Scientific Advisory Board, the Salk Institute NeuroAI group, and the Longevity Org Advisory Board. Michael has over 30 issued and 45 pending patents; and he has numerous publications in peer-reviewed journals. He is a board-certified dermatologist and dermatopathologist, licensed in California, with degree’s in biochemistry, physics, computer science, and medicine. In his minimal spare time, Michael likes to surf big waves in Indonesia and Mexico, and he also helped build a WHO-sanctioned basic care clinic in the Fijian town of Nabila.

Presentations

Cognitive mobile health care for the patient and physician Session

Precision medicine is largely a big data and systems problem, especially with many different types of "siloed" health care information, such as lab results, genetic tests, IoT and wearables data, and insurance information. Michael Nova explains why cognitive computing and artificial intelligence that can dynamically learn using any healthcare data will dramatically impact precision health care.

Josh Patterson is the director of field engineering for Skymind. Previously, Josh ran a big data consultancy, worked as a principal solutions architect at Cloudera, and was an engineer at the Tennessee Valley Authority, where he was responsible for bringing Hadoop into the smart grid during his involvement in the openPDC project. Josh is a graduate of the University of Tennessee at Chattanooga with a master of computer science, where he did research in mesh networks and social insect swarm algorithms. Josh is a cofounder of the DL4J open source deep learning project and is a coauthor on the upcoming O’Reilly title Deep Learning: A Practitioner’s Approach. Josh has over 15 years’ experience in software development and continues to contribute to projects such as DL4J, Canova, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.

Presentations

Neural networks for time series analysis using Deeplearning4J 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data.

Christoph Peylo leads the Bosch Center for Artificial Intelligence, part of Corporate Research (CR) at Robert Bosch GmbH. Previously, he was VP at Deutsche Telekom Laboratories in Berlin and a member of the management board of Deutsche Telekom Innovation Laboratories at Ben Gurion University of the Negev. Christoph has also worked in the areas of artificial intelligence, (cyber)security, Industrie 4.0, and M2M in positions ranging from software engineer to managing director of a software company. Christoph studied computer science, computational linguistics, and artificial intelligence and holds a PhD in AI from the University of Osnabrück.

Presentations

Beyond the hype: Real AI contributions in industry and engineering Session

Generating commercial value from AI in a highly sophisticated industrial environment is a challenge. So far, AI accomplishments in this field stem mostly from marketing rather than systematic application to product lifecycles. Christoph Peylo shares examples of meaningful commercial IoT deployments and discusses obstacles that have to be overcome.

Delip Rao is the founder of Joostware, a San Francisco-based company specializing in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning that will be published by O’Reilly Media.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao explores natural language processing using a set of machine-learning techniques known as deep learning. Delip walks you through neural network architectures and NLP tasks and teaches you how to apply these architectures for those tasks.

Tackling the fake news problem with AI Session

Not a single day goes by without a mention of "fake news" or the problems it causes. Delip Rao offers a nonpartisan overview of fake news, briefly exploring the technology landscape surrounding the content verification and validation problem and diving deeper into the Fake News Challenge and the stance detection problem.

Naveen’s fascination with computation in synthetic and neural systems began around age 9 when he began learning about circuits that store information along with some AI themes prevalent in sci-fi at the time. He went on to study electrical engineering and computer science at Duke, but continued to stay in touch with biology by modeling neuromorphic circuits as a senior project. After studying computer architecture at Stanford, Naveen spent the next 10 years designing novel processors at Sun Microsystems and Teragen as well as specialized chips for wireless DSP at Caly Networks, video content delivery at Kealia, Inc, and video compression at W&W Comms. Armed with intimate knowledge of synthetic computation systems, Naveen decided to get a PhD in Neuroscience to understand how biological systems do computation better. He studied neural computation and how it relates to neural prosthetics in the lab of John Donoghue at Brown. After a stint in finance doing algorithmic trading optimization at ITG, Naveen was part of the Qualcomm’s neuromorphic research group leading the effort on motor control and doing business development. Naveen was the founder and CEO of Nervana, which was acquired by Intel. Naveen strongly believes that it’s in Intel Nervana’s DNA to bring together engineering disciplines and neural computational paradigms to evolve the state-of-the-art and make machines smarter.

Presentations

Intel Keynote Keynote

Keynote by Naveen Rao (Intel)

Ruslan Salakhutdinov is an associate professor in the Machine Learning Department at Carnegie Mellon University. Previously, he was an assistant professor in the Departments of Statistics and Computer Science at the University of Toronto and and spent two years as a postdoc at the Massachusetts Institute of Technology’s Artificial Intelligence Lab. Ruslan’s primary interests are deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and has served on the senior program committee of several learning conferences, including NIPS and ICML. He is an Alfred P. Sloan research fellow, Microsoft Research faculty fellow, Canada research chair in statistical machine learning, a senior fellow of the Canadian Institute for Advanced Research, and a recipient of the Early Researcher Award, Google Faculty Award, and the NVIDIA Pioneers of AI award. Ruslan holds a PhD in computer science from the University of Toronto.

Presentations

Session by Russ Salakhutdinov Session

Session by Russ Salakhutdinov

Tuomas Sandholm is professor in the Computer Science Department at Carnegie Mellon University, with affiliate professor appointments in the Machine Learning Department, the PhD program in algorithms, combinatorics, and optimization (ACO), and the CMU-Pitt joint PhD program in computational biology. He is the founder and director of the Electronic Marketplaces Laboratory.
He is founder and CEO of Optimized Markets, Inc., which is bringing a new expressive optimization-powered paradigm to advertising campaign sales and scheduling in TV (linear and digital), streaming, internet display, mobile, game, radio, and cross-media advertising.
He is founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning under imperfect information. The company has exclusively licensed from his CMU laboratory the Libratus technology, which was the first to beat top professional poker players at Heads-Up No-Limit Texas Hold’em, and a host of other technologies. The company targets a broad set of applications ranging from poker to other recreational games to business strategy, negotiation, strategic pricing, finance, auctions, cybersecurity, physical security, military applications, political campaigns, and medical treatment planning.

Previously, Tuomas was founder, chairman, and CTO/chief scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world’s largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. His algorithms also run the UNOS kidney exchange, which includes 66% of the transplant centers in the US. He has served as market design consultant or board member for a number of companies, including Baidu, Yahoo, Google, Chicago Board Options Exchange, Swap.com, and Granata Decision Systems. Tuomas has published over 450 papers. His many honors include the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, and Computers and Thought Award. He is fellow of the ACM, AAAI, and INFORMS and holds an honorary doctorate from the University of Zurich. He holds a PhD and MS in computer science and a Dipl. Eng. with distinction in industrial engineering and management science.

Presentations

Keynote by Tuomas Sandholm Keynote

Keynote by Tuomas Sandholm

Suchi Saria is an assistant professor of computer science, health policy, and statistics at Johns Hopkins University. Her research interests are statistical machine learning and “precision” healthcare—specifically designing novel data-driven computing tools for optimizing care delivery. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and to individualize disease management in complex, chronic diseases. Suchi’s work has been recognized in Science Translational Medicine, by paper awards by the the Association for Uncertainty in Artificial Intelligence and the American Medical Informatics Association, an Annual Scientific Award by the Society of Critical Care Medicine, and competitive awards from the Gordon and Betty Moore Foundation and Google Research, and with a Rambus fellowship and an NSF Computing Innovation fellowship. She has been selected by IEEE Intelligent Systems as one of 2015’s AI’s 10 to watch, a 2016 DARPA Young Faculty awardee, and one of Popular Science’s 2016 brilliant 10. Suchi holds a PhD from Stanford University, where she worked under Daphne Koller.

Presentations

Keynote by Suchi Saria Keynote

Keynote by Suchi Saria

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

Presentations

Deep learning with TensorFlow 2-Day Training

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

Gary Short is a data solution architect for Microsoft, where he specializes in machine learning and big data on the Azure Platform. Gary is interested in data science in all forms, especially computational linguistics and social network analysis.

Presentations

Learn how to gain business insights from unstructured data by leveraging NERs, graphs and conversational interfaces Tutorial

Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework.

Richard Socher is chief scientist at Salesforce, where he leads the company’s research efforts and works on bringing state-of-the-art artificial intelligence solutions to Salesforce. Previously, Richard was the CEO and founder of MetaMind (acquired by Salesforce in April 2016). MetaMind’s deep learning AI platform analyzes, labels, and makes predictions on image and text data so businesses can make smarter, faster, and more accurate decisions than ever before. He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo Key Scientific Challenges Award, a Microsoft Research PhD fellowship, a “Magic Grant” from the Brown Institute for Media Innovation, and the 2014 GigaOM Structure Award. Richard holds a PhD in deep learning from Stanford, where he worked with Chris Manning and Andrew Ng. His research won the Best Stanford CS PhD Thesis award.

Presentations

Tackling the limits of deep learning Keynote

AI presents a huge opportunity for businesses to personalize and improve customer experiences and improve efficiency, but the technical complexity of AI puts it out of reach for most companies. Richard Socher explains how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to Salesforce customers.

Tackling the limits of deep learning Session

Deep learning has made great progress in a variety of language tasks. However, there are still many practical and theoretical problems and limitations. Richard Socher shares some solutions.

Joseph Spisak manages deep learning product management at AWS. Joseph has experience driving strategies and technical and business engagements around machine learning-based cloud workloads, such as computer vision, natural language processing, video summarization and analysis, and speech recognition. Joseph has more than 15 years’ experience delivering products and services in digital video, cloud-based media transcoding, image processing, and machine- and deep learning in the consumer mobile, broadcast, and cloud segments. Joseph holds a bachelor’s degree in electrical engineering from Michigan State University and an MBA and MS in finance from the University of Denver. He is a proud graduate of the Entrepreneurial and Innovation certificate program at Stanford University’s Graduate School of Business.

Presentations

Distributed deep learning on AWS using Apache MXNet Tutorial

Joseph Spisak and Anima Anandkumar offer an introduction to the powerful and scalable deep learning framework Apache MXNet. You'll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to quickly spin up AWS GPU clusters to train at record speeds.

Rupert Steffner is the founder of WUNDER.AI – an E-commerce AI start-up that is giving customers a joy ride to the products they love. Before that Rupert has been Chief Platform Architect of Otto Group’s new Business Intelligence Platform BRAIN and Head of BI at Groupon EMEA&APAC. He worked for several European and US companies as Business Intelligence Leader (e-commerce, retail, finance, telco). Rupert has over 25 years of experience in designing and implementing highly sophisticated technical and business solutions with a focus on customer centric marketing. He holds a Master of Business Administration of the WU Vienna and was Head of Marketing Department at the University of Applied Sciences, Salzburg.

Presentations

Deep shopping bots: Building machines that think and sell like humans Session

70% of consumers do NOT feel that online offers resonate with their personal interests and needs. Rupert Steffner explains how cognitive AI can help create deep shopping bots based on true personal relevance. This shift in the shopping paradigm is built upon deep symbolic reinforcement learning, the psychometry of shopping, a new breed of playful UI, and cognified product metadata.

Barbara Stortz is a principal software manager at Microsoft working on data science customer projects running on Microsoft Azure and Cortana Intelligence, including machine learning and deep learning technologies. Previously, Barbara was a senior vice president for SAP Labs LLC, a founding member of SAP HANA, and head of SAP’s EIM products and the SAP Healthcare platform.

Presentations

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines.

Matt Taylor is the Open Source Community Manager for the Numenta Platform for Intelligent Computing, where he spends most of his time managing, encouraging, and interacting with the NuPIC OS community. Matt has been working with and on open source projects for years. Originally from a farming community in rural Missouri, Matt now lives in California and increasingly finds it hard to leave.

Presentations

The biological path toward strong AI Session

Today's wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical temporal memory (HTM) is a realistic biologically constrained model of the pyramidal neuron reflecting today's most recent neocortical research. Matthew Taylor offers an overview of core HTM concepts, including sparse distributed representations, spatial pooling, and temporal memory.

Presentations

Building machines that learn and think like people Keynote

Josh Tenenbaum explains how to build machines that learn and think like people.

Riva-Melissa Tez is a managing partner at Permutation Ventures, an early-stage VC fund focused on applied-machine-learning companies. She has held teaching positions at the DAB and HTW business schools and has guest-lectured at Oxford, Birkbeck, and Stanford. Riva has written about artificial intelligence, finance, and philosophy, and represented AI in a 2016 Microsoft campaign for the Economist and was one of Forbes’s 2017 30 under 30 for finance.

Presentations

Demystifying AI hype Session

Riva-Melissa Tez explores the potential advantages and disadvantages of the AI hype bubble and offers practical tips on how to navigate between real innovation and total nonsense.

Richard Tibbetts is CEO of Empirical Systems, an MIT spinout building an AI-based data platform for organizations that use structured data to provide decision support. Previously, he was was founder and CTO at StreamBase, a CEP company that merged with TIBCO in 2013, as well as a visiting scientist at the Probabilistic Computing Project at MIT.

Presentations

AI for structured business data Tutorial

Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians.

Probabilistic programming Tutorial

Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts.

Anusua Trivedi is a data scientist on Microsoft’s Advanced Data Science & Strategic Initiatives team, where she works on developing advanced predictive analytics and deep learning models. Previously, Anusua was a data scientist at the Texas Advanced Computing Center (TACC), a supercomputer center, where she developed algorithms and methods for the supercomputer to explore, analyze, and visualize clinical and biological big data. Anusua is a frequent speaker at machine learning and big data conferences across the United States, including Supercomputing 2015 (SC15), PyData Seattle 2015, and MLconf Atlanta 2015. Anusua has also held positions with UT Austin and University of Utah.

Presentations

Building a medical imaging bot in Azure Session

Anusua Trivedi outlines an AI bot that uses deep vision and advanced cognitive analytics to analyze medical imaging scans to help radiologists and emergency department physicians recognize hard-to-spot abnormalities and make better decisions.

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines.

Ferhan Ture is a member of the Comcast Labs DC research group, where he focuses on combining deep learning and natural language processing to build data-driven solutions for various Comcast products involving language, including the algorithms behind the voice-enabled remote controller for the X1 entertainment system. He is a frequent speaker at conferences worldwide.

Presentations

How AI powers the Comcast X1 voice interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and is how millions of its customers access their content on the TV. Jan Neumann, Ferhan Ture, and Oliver Jojic explain how AI enables Comcast to understand what you are looking for when you talk to the X1 voice remote and how Comcast scaled the voice interface to answer millions of voice queries every single night.

Amy Unruh is a developer programs engineer for the Google Cloud Platform, with a focus on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, done industrial R&D, and published a book on App Engine.

Presentations

Scaling machine learning with TensorFlow Tutorial

TensorFlow is an increasingly popular open source machine intelligence library that is especially well suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. Yufeng Guo and Amy Unruh offer an introduction to TensorFlow concepts and walk you through using CloudML to do distributed training and scalable serving of your models.

Ben Vigoda is the founder, CEO, and CTO of Gamalon Machine Intelligence. 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 (acquired by Analog Devices). The company 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, and Wired, among others. Ben also cofounded 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. He has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation/National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. He has authored over 120 patents and academic publications. He recently served on the DARPA Information Science and Technology (ISAT) steering committee. Ben holds a PhD from MIT, where he worked on developing circuits for implementing machine-learning algorithms natively in hardware.

Presentations

Eliminating the human cost of training AI: Bayesian program synthesis Session

Ben Vigoda offers an overview of Bayesian program synthesis (BPS), outlines the significant advantages it provides over deep learning technologies, and explains how it removes some of the biggest obstacles preventing AI from being adopted in the enterprise.

Yiheng Wang is a software development engineer on the Big Data Technology team at Intel working in the area of big data analytics. Yiheng and his colleagues are developing and optimizing distributed machine-learning algorithms (e.g., neural network and logistic regression) on Apache Spark. He also helps Intel customers build and optimize their big data analytics applications.

Presentations

BigDL: Distributed deep learning on Apache Spark Tutorial

Yiheng Wang offers an overview of BigDL, a distributed deep learning library on Apache Spark that helps users easily integrate most advanced deep learning algorithms (CNN, RNN, etc.) into popular big data platforms. Yiheng demonstates how to develop with BigDL and shares some practical use cases.

Galiya Warrier is a data solution architect at Microsoft, where she helps enterprise customers along their journey of adoption of Microsoft Azure Data technologies from big data workloads to machine learning and chatbots.

Presentations

Learn how to gain business insights from unstructured data by leveraging NERs, graphs and conversational interfaces Tutorial

Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework.

Francisco Webber is the CEO and cofounder of Cortical.io, a company that develops natural language processing solutions for big text data. Francisco’s medical background in genetics combined with his more than two decades of experience in information technology inspired him to create semantic folding, a groundbreaking technology based on the latest findings on the way the human neocortex processes information. Prior to Cortical.io, Francisco founded Matrixware Information Services, a company that developed the first standardized database of patents. Francisco also initiated the Information Retrieval Facility, a nonprofit research institute, with the goal to bridge the gap between science and industry in the information retrieval domain.

Presentations

AI-powered natural language understanding applications in the financial industry Session

Financial industries are under increased pressure due to regulations that demand extended information management capabilities. Information largely consists of text data, which forces companies to increase headcount to keep up with the growing workload. Francisco Webber demonstrates how Cortical.io’s semantic folding, a neuroscience-based approach to NLU, helps automate these uses cases.

Thomas Wiecki is the lead data science researcher at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University.

Presentations

Bayesian Deep Learning in PyMC3 Session

Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. Thomas Wiecki demonstrates how to embed deep learning in the probabilistic programming framework PyMC3 to address uncertainty and nonstationarity.

Xiaofan Xu is a research engineer at Intel specializing in artificial intelligence and robotics. Previously, Xiaofan worked in the CTO office at Movidius on various research projects, including 3D volumetric object recognition using convolutional neural networks and training neural networks using synthetic data.

Presentations

Anaerobic AI: Developing in a data-starved environment Session

Data is the “oxygen” of the AI revolution, but access to data on a large scale remains a luxury of an elite group of tech companies, effectively creating a “data wall” blocking smaller companies. David Moloney and Xiaofan Xu explore the problem of the data wall and offer a solution: synthetic datasets.

Reza Bosagh Zadeh is on the faculty at Stanford, where he teaches Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms, and is the founder and CEO of Matroid. His work focuses on machine learning, distributed computing, and discrete applied mathematics. As part of his research, Reza built the machine-learning algorithms behind Twitter’s who-to-follow system, the first product to use machine learning at Twitter. Reza is the initial creator of the linear algebra package in Apache Spark and his work has been incorporated into industrial and academic cluster computing environments. Reza serves on the technical advisory board of Microsoft and Databricks. His awards include a KDD Best Paper award and the Gene Golub Outstanding Thesis award. Reza received his PhD in computational mathematics from Stanford University under the supervision of Gunnar Carlsson.

Presentations

Scaling computer vision in the cloud Session

Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale. Reza Zadeh offers an overview o Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services.

Matthew Zeiler is an artificial intelligence expert with a Ph.D. in machine learning from NYU. His groundbreaking research in computer vision, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, has propelled the image recognition industry from theory to real-world practice. As the founder of Clarifai, Matt is applying his award-winning research to create the best visual recognition solutions for businesses and developers and power the next generation of intelligent apps. Reach him @MattZeiler.

Presentations

Risks, hidden costs, and how to escape the black hole of machine-learning technical debt Session

AI-powered machine-learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning.