Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Speakers

Learn new skills and techniques from these experts and leading practitioners. New speakers are added regularly. Please check back to see the latest updates to the agenda.

Filter

Search Speakers

Sameer Wadkar is a senior principal architect for machine learning at Comcast NBCUniversal, where he works on operationalizing machine learning models to enable rapid turnaround times from model development to model deployment and oversees data ingestion from data lakes, streaming data transformations, and model deployment in hybrid environments ranging from on-premises deployments to cloud and edge devices. Previously, he developed big data systems capable of handling billions of financial transactions per day arriving out of order for market reconstruction to conduct surveillance of trading activity across multiple markets and implemented natural language processing (NLP) and computer vision-based systems for various public and private sector clients. He is the author of Pro Apache Hadoop and blogs about data architectures and big data.

Presentations

Machine learning meets DevOps: Paying down the high-interest credit card Session

Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments.

Nidhi Aggarwal is the coauthor of the the Medium publication Radical Product. An entrepreneur who is passionate about building radical products, most recently Nidhi led product, strategy, marketing, and finance at data integration company Tamr. Previously, she cofounded cloud configuration management startup qwikLABS (acquired by Google), which remains the exclusive platform used by AWS customers and partners worldwide to create and deploy on-demand lab environments in the cloud, and worked at McKinsey & Company, where she focused on big data and cloud strategy. She holds six US patents. Nidhi holds a PhD in computer science from the University of Wisconsin-Madison.

Presentations

Customer-centered AI: A radical strategy Tutorial

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

Mary Beth Ainsworth is an AI and language analytics strategist at SAS, where she leads global marketing efforts for text analytics and artificial intelligence. Previously, she was an intelligence analyst and senior instructor in the US Department of Defense and the intelligence community, primarily supporting expeditionary units and special operations.

Presentations

Bringing AI into the wild (sponsored by SAS) Keynote

Comprehensive and sustainable wildlife monitoring technologies are key to maintaining biodiversity. Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that can rapidly analyze animal footprints to help map wildlife presence and scale conservation efforts around the world.

Improving wildlife conservation with artificial intelligence (sponsored by SAS) Session

Indigenous trackers all over the world can look at a single footprint in the dirt and intuitively know what animal species that print belongs to. Mary Beth Ainsworth explains how biologists, zoologists, machine learning and computer vision experts have come together to develop, automate, and scale a noninvasive approach to monitoring endangered wildlife by analyzing where animals have walked.

Alasdair Allan is a scientist and researcher who has authored more than 80 peer-reviewed papers and eight books and has been involved with several standards bodies. Originally an astrophysicist, Alasdair now works as a consultant and journalist, focusing on open hardware, machine learning, big data, and emerging technologies, with expertise in electronics, especially wireless devices and distributed sensor networks, mobile computing, and the internet of things. He runs a small consulting company and has written for Make magazine, Motherboard/VICE, Hackaday, Hackster.io, and the O’Reilly Radar. In the past, he has mesh-networked the Moscone Center, caused a US Senate hearing, and contributed to the detection of what was at the time the most distant object yet discovered.

Presentations

Do-it-yourself artificial intelligence Session

The AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan explains how to set up and build the kits and how to use the Python SDK to use machine learning both in the cloud and locally on the Raspberry Pi.

Robbie Allen is the cofounder and CEO of InfiniaML, a company helping organizations implement machine learning across the enterprise. Previously, Robbie led Automated Insights (acquired in 2015 by Vista Equity Partners), one of the first companies to deploy natural language generation (NLG) solutions in the enterprise.

Presentations

Best practices for machine learning in the enterprise Session

Drawing on his experience leading two successful AI companies that implemented machine learning and NLP solutions in over a hundred organizations, Robbie Allen details patterns and characteristics of successful machine learning implementations (and those that predict failure) and explains how to build and cultivate ML talent within your organization in an increasingly competitive job market.

Anish Athalye is a graduate student at the Massachusetts Institute of Technology.

Presentations

Fooling neural networks in the physical world Session

Andrew Ilyas, Logan Engstrom, and Anish Athalye share an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.

Peter Bailis is an assistant professor of computer science at Stanford University. Peter’s research in the Future Data Systems group and DAWN project focuses on the design and implementation of postdatabase data-intensive systems. He is the recipient of the ACM SIGMOD Jim Gray Doctoral Dissertation Award, an NSF Graduate Research Fellowship, a Berkeley Fellowship for Graduate Study, best-of-conference citations for research appearing in both SIGMOD and VLDB, and the CRA Outstanding Undergraduate Researcher Award. He holds a PhD from UC Berkeley and an AB from Harvard College, both in computer science.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

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

Presentations

Deep learning with TensorFlow 2-Day Training

TensorFlow is an increasingly popular tool for deep learning. Dylan Bargteil offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Dylan covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

Deep learning with TensorFlow (Day 2) Training Day 2

TensorFlow is an increasingly popular tool for deep learning. Dylan Bargteil offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Dana covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

David Barrett is founder and CEO of Expensify, where he’s relieving the world’s frustrations, one expense report at a time. Expensify processes billions of dollars in reimbursements every day across 50,000 companies and more than five million users around the world. A pioneer in the fintech space, David started programming at age of six. Previously, he wrote 3D graphics engines for the video game industry and joined Uber’s Travis Kalanick to build Red Swoosh, a peer-to-peer file transfer technology (acquired by Akamai in 2007). David attended the University of Michigan, where he worked in the Virtual Reality Lab.

Presentations

AI and the future of customer service: Meet Expensify’s new AI-assistant, Concierge Session

Expensify is using AI to streamline and improve customer service, reducing customer wait time from 15 hours to 3 minutes. David Barrett leads a deep dive into the process of building Concierge, a hybrid machine learning-driven chatbot, covering the challenges faced, results to date, and what he sees for the future of AI and customer service.

Ian Beaver is the lead research engineer at Next IT – Verint, a provider of conversational AI systems for enterprise businesses. Ian has been publishing and presenting discoveries in the field of AI since 2005 on topics related to human-computer interactions, such as gesture recognition, user preference learning, and detecting and preventing miscommunication with multimodal automated assistants. Ian holds a BS, MS, and PhD in Computer Science. He can often be found hiking, snowboarding, and spending time with his wife and son.

Presentations

From here to "Her": Evolving chatbot interactions to meet the relational needs of humans Session

Conversation is emerging as the next great human-machine interface. Ian Beaver and Cynthia Freeman outline the challenges faced by the AI industry to relate to humans in the way they relate to each other and highlight findings from a recent study to demonstrate relational strategies used by humans in conversation and explain how virtual assistants must evolve to communicate effectively.

Chris Benson is Chief Scientist of Artificial Intelligence and Machine Learning for Safety and Productivity Solutions, one of the four global strategic business groups at Honeywell, where he is responsible for all AI initiatives across all product lines. Chris is an AI strategist, solution architect, public speaker, and evangelist specializing in deep learning. Chris frequently speaks about AI topics at conferences and is the founder and organizer of the Atlanta Deep Learning Meetup, one of the largest AI communities in the world.

Presentations

Artificial intelligence strategy: Delivering deep learning Session

Deep learning is the driving force behind the current AI revolution and will impact every industry on the planet. However, success requires an AI strategy. Chris Benson walks you through creating a strategy for delivering deep learning into production and explores how deep learning is integrated into a modern enterprise architecture.

William Benton leads a team of data scientists and engineers at Red Hat, where he has applied machine learning to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His current focus is investigating the best ways to build and deploy intelligent applications in cloud-native environments, but he has also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.

Presentations

Containers and the intelligent application revolution Session

Intelligent applications learn from data to provide improved functionality to users. William Benton examines the confluence of two development revolutions: almost every exciting new application today is intelligent, and developers are increasingly deploying their work on container application platforms. Join William to learn how these two revolutions benefit one another.

Lori Bieda is head of the Bank of Montreal’s Analytics Centre of Excellence, where she oversees analytics, including revenue, risk, and price trade-off decisions, product analytics, customer optimization, database marketing, predictive analytics, customer experience, sales, and service optimization. She also leads enterprise customer journey analytics, cross-channel client experience analytics, and the monetization of journeys. An analytics, technology, and marketing executive with 20+ years experience driving profitable business growth through the strategic use of data and analytics, Lori has worked around the globe helping Fortune 500 companies across financial services, telecom, technology, healthcare, retail, and manufacturing advance their businesses through the strategic use of data and insights.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.

Ron Bodkin is a technical director on the applied artificial intelligence team at Google, where he provides leadership for AI success for customers in Google’s Cloud CTO office. Ron engages deeply with Global F500 enterprises to unlock strategic value with AI, acts as executive sponsor with Google product and engineering to deliver value from AI solutions, and leads strategic initiatives working with customers and partners. Previously, Ron was the founding CEO of Think Big Analytics, a company that 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. When Think Big was acquired by Teradata, Ron led global growth, the development of the Kylo open source data lake framework, and the company’s expansion to architecture consulting; he also helped create Teradata’s artificial intelligence incubator.

Presentations

Using artificial intelligence to enhance the digital experience Session

Ron Bodkin explains how Google is using AI internally to enhance understanding and experiences for its digital customers and enabling external businesses, such as Spotify and Netflix, to do the same. Along the way, Ron shares examples of deep learning use cases that enable improved recommendations, help companies better understand their customers, and drive engagement in the customer lifecycle.

WTT: What the tensor? (sponsored by Google Cloud) Keynote

Ron Bodkin explains WTF a tensor is and why you should care. Along the way, Ron details some real AI products from Google. No cats or dogs.

Chris Butler is the director of AI at Philosophie, where he leads the firm in human-centered AI engagements. Chris has over 18 years of product and business development experience at companies like Microsoft, KAYAK, and Waze. He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University and has worked on AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He has created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Presentations

Design thinking for AI Tutorial

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

Faizan Buzdar is the senior director of platform product management at Box, where he leads the charge of custom machine learning for enterprises. He also oversees partner integrations with companies like Google and Microsoft. Faizan has a relentless drive to transform and simplify the modern workplace. Previously, he was the founder and CEO of Convo, a technology startup and a SaaS platform for enterprise collaboration for mission-critical enterprise use cases like product design, healthcare/emergency care, and editorial content creation. Fazian is a passionate executive, entrepreneur, product guy, and photography enthusiast. He holds several patents and has designed and taught workshops and classes on human interaction and object-oriented programming at top engineering universities. He was also highlighted as a case study for immigration reforms and startup visas by President Obama. He is currently a cochair of FWD.us.

Presentations

AI and the future of work Session

AI will fundamentally change (and power) the way the world works together. So what does the future of AI in the enterprise look like? Faizan Buzdar explains how intelligence is being applied to enterprise content in practical ways that will revolutionize the most important business processes for companies of all sizes and across all industries.

Presentations

Deploy MXNet and TensorFlow deep learning models with AWS Lambda, Google Cloud Functions, and Azure Functions Tutorial

Greg Werner walks you through using MXNet and TensorFlow to train deep learning models and deploy them using the leading serverless compute services in the market: AWS Lambda, Google Cloud Functions, and Azure Functions. You'll also learn how to monitor and iterate upon trained models for continued success using standard development and operations tools.

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

Presentations

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.

Tuesday opening remarks Keynote

Artificial Intelligence program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Understanding automation Keynote

Keynote by program chairs Ben Lorica and Roger Chen

Wednesday opening remarks Keynote

Artificial Intelligence program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Pramit Choudhary is a lead data scientist at DataScience.com, where he focuses on optimizing and applying classical machine learning and Bayesian design strategy to solve real-world problems. Currently, he is leading initiatives on figuring out better ways to explain a model’s learned decision policies to reduce the chaos in building effective models and close the gap between a prototype and operationalized model.

Presentations

Model evaluation in the land of deep learning Session

Predicting the target label for computer vision machine learning problems is not enough. You must also understand the why, what, and how of the categorization process. Pramit Choudhary offers an overview of ways to faithfully interpret and evaluate deep neural network models, including CNN image models to understand the impact of salient features in driving categorization.

George Church is professor at Harvard and MIT, where he has developed methods used for the first genome sequence in 1994 and genome recoding, leading to million-fold cost reductions. He co-initiated the BRAIN Initiative and Genome Projects to provide and interpret the world’s only open-access personal precision medicine data. He is the coauthor of 450 papers, 95 patent publications, and the book Regenesis.

Presentations

Hybrid bio-opto-electronics for AI Keynote

The IARPA MICrONS project aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. George Church offers an overview of this work and explains how his team has accelerated in vitro growth of many brain architectures, which might enable us to build new hybrid bio-opto-electronic artificial computational platforms.

Gerard de Melo is an assistant professor of computer science at Rutgers University, where he heads a team of researchers working on big data analytics, natural language processing, and web mining. Gerard’s research projects include UWN/MENTA, one of the largest multilingual knowledge bases, and Lexvo.org, an important hub in the web of data. Previously, he was a faculty member at Tsinghua University, one of China’s most prestigious universities, where he headed the Web Mining and Language Technology Group, and a visiting scholar at UC Berkeley, where he worked in the ICSI AI Group. He serves as an editorial board member for Computational Intelligence, the Journal of Web Semantics, the Springer Language Resources and Evaluation journal, and the Language Science Press TMNLP book series. Gerard has published over 80 papers, with best paper or demo awards at WWW 2011, CIKM 2010, ICGL 2008, and the NAACL 2015 Workshop on Vector Space Modeling, as well as an ACL 2014 best paper honorable mention, a best student paper award nomination at ESWC 2015, and a thesis award for his work on graph algorithms for knowledge modeling. He holds a PhD in computer science from the Max Planck Institute for Informatics.

Presentations

Deep sentiment analysis across language boundaries Session

Across the globe, people are voicing their opinion online. However, sentiment analysis is challenging for many of the world's languages, particularly with limited training data. Gerard de Melo demonstrates how to exploit large amounts of surrogate data to learn advanced word representations that are custom-tailored for sentiment and shares a special deep neural architecture to use them.

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

Presentations

Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters Session

Deep learning has fueled the emergence of many practical applications and experiences. Meanwhile, container technologies have been maturing, allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean as they walk you through using the Cognitive Toolkit (CNTK) with Kubernetes clusters.

Abhijit Deshpande is the global head of Digitate’s Product Specialist Group, where he is responsible for working with customers to drive adoption of ignio. A technology leader in the distributed systems area, Abhijit regularly works with various teams in Digitate’s customer organizations, such as enterprise architecture, engineering, and operations groups, on topics such as data centers, network operations, and information security. He has 17 years of experience in sales, presales, and solutions in the products and services areas across many industries. Previously, Abhijit was part of the TCS Strategic Solutions Group responsible for large and strategic IT services (applications and infrastructure services) deals. In the last five years, Abhijit has managed deals worth $900M across various industries like banking and financial services, insurance, consumer packaged goods, travel, transportation and hospitality, manufacturing, and telecommunications (ESPs and CSPs).

Presentations

Using machine learning in workload automation (sponsored by Digitate) Keynote

We live in a world of constantly changing business environments across various business units, limited end-to-end visibility, and high alerts. Abhijit Deshpande details how to use machine learning to identify root causes of problems in minutes instead of hours or days to free up valuable time by automating routine tasks without scripting or preprogramming.

Greg Diamos leads computer systems research at Baidu’s Silicon Valley AI Lab (SVAIL), where he helped develop the Deep Speech and Deep Voice systems. Previously, Greg contributed to the design of compiler and microarchitecture technologies used in the Volta GPU at NVIDIA. Greg holds a PhD from the Georgia Institute of Technology, where he led the development of the GPU-Ocelot dynamic compiler, which targeted CPUs and GPUs from the same program representation.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

Radhika Dutt is is the coauthor of the Medium publication Radical Product and a cocreator of the Radical Product practical toolkit. Radhika is a product executive who has participated in four exits, two of which were companies she founded, including Lobby7, a venture-backed company that created an early version of Siri back in 2000 (acquired by Scansoft/Nuance). Most recently, she led product management at Allant, where she and her team built a SaaS product for TV advertising. (Allant’s TV division was subsequently acquired by Acxiom.) Previously, she worked at Avid, growing its broadcast business by building a product suite to address pain points of broadcasters worldwide as they were moving from tape to digital media; led strategy at the telecom startup Starent Networks (acquired by Cisco for $2.9B); and founded Likelii, a company that offered consumers a “Pandora for wine” (acquired by Drync). Too long ago to admit, Radhika graduated from MIT with an SB and MEng in electrical engineering. She speaks nine languages.

Presentations

Customer-centered AI: A radical strategy Tutorial

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

Jana Eggers is CEO of Nara Logics, a neuroscience-inspired artificial intelligence company providing a platform for recommendations and decision support. A math and computer nerd who took the business path, Jana has had a career that has taken her from a three-person business to 50,000+-person enterprises. She opened the European logistics software offices as part of American Airlines, dove into the internet in ’96 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory. Her passions are working with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.

Presentations

Executive Briefing: Building a learning organization is AI's hat trick Session

AI scores points for providing better answers to your company's challenges and for requiring you to get your data house in order. Jana Eggers explains why AI's hat trick is how it can transform your company into a learning organization. Jana reviews the benefits of a learning org and details how to build an AI program that can support you in achieving those benefits.

Logan Engstrom is an undergraduate student at the Massachusetts Institute of Technology.

Presentations

Fooling neural networks in the physical world Session

Andrew Ilyas, Logan Engstrom, and Anish Athalye share an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.

Sergey Ermolin is a software solutions architect for deep learning, Spark analytics, and big data technologies at Intel. A Silicon Valley veteran with a passion for machine learning and artificial intelligence, Sergey has been interested in neural networks since 1996, when he used them to predict aging behavior of quartz crystals and cesium atomic clocks made by Hewlett-Packard. Sergey holds an MSEE and a certificate in mining massive datasets from Stanford and BS degrees in both physics and mechanical engineering from California State University, Sacramento.

Presentations

Build deep learning-powered big data solutions with BigDL Session

Sergey Ermolin details the latest features, real-world use cases, and what's in store for 2018 for BigDL on Intel Xeon processor-based data center and cloud deployments.

Susan Etlinger is an industry analyst at Altimeter. Her research focuses on the impact of artificial intelligence, data and advanced technologies on business and culture and is used in university curricula around the world. Susan’s TED talk, “What Do We Do With All This Big Data?,” has been translated into 25 languages and has been viewed more than 1.2 million times. She is a sought-after keynote speaker and has been quoted in such media outlets as the Wall Street Journal, the BBC, and the New York Times.

Presentations

Executive Briefing: The conversational business—Use cases and best practices for chatbots in financial services and media Session

Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots, covering the risks and opportunities of conversational interfaces, the strategic implications for customer experience, business models, brand strategy, and recent innovations.

AI Innovation Lead for Testing

Presentations

How artificial intelligence helps advance day-to-day quality and maintenance decisions Session

In manufacturing, software development, and aerospace, tech-op teams need to make critical decisions on the spot with very little information. In this session, presented by Intel Saffron, the speakers share actual use cases of cognitive AI-based applications helping technical professionals make more confident decisions to solve the pressing issues in their day-to-day work.

Justin Fier is the director of cyber intelligence and analytics at Darktrace. With over 10 years of experience in cyberdefense, Justin has supported various elements in the US intelligence community, holding mission-critical security roles with Lockheed Martin, Northrop Grumman Mission Systems, and Abraxas. He is a highly skilled technical officer and a specialist in cyber operations across both offensive and defensive arenas.

Presentations

Lessons learned building an AI company from the ground up Session

Although AI technology seems to be everywhere, implementing AI in practice is a real challenge. The technology needs to be scalable, trusted by the humans that use it, and easily accessible for those with limited AI expertise. Nicole Eagan shares the unique insights on building practical and successful AI applications Darktrace has gained from its 4,000+ deployments.

Cynthia Freeman is a research and software engineer at Next IT corporation, a developer of conversational AI systems. She is currently a graduate student in computer science at the University of New Mexico. She holds an MS in applied mathematics from the University of Washington and a BS in mathematics from Gonzaga University.

Presentations

From here to "Her": Evolving chatbot interactions to meet the relational needs of humans Session

Conversation is emerging as the next great human-machine interface. Ian Beaver and Cynthia Freeman outline the challenges faced by the AI industry to relate to humans in the way they relate to each other and highlight findings from a recent study to demonstrate relational strategies used by humans in conversation and explain how virtual assistants must evolve to communicate effectively.

Aurélien Géron is a machine learning consultant at Kiwisoft. Previously, he led YouTube’s video classification team and was founder and CTO of two successful companies (a telco operator and a strategy firm). Aurélien is the author of several technical books, including the O’Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow.

Presentations

Predicting the stock market using LSTMs Session

The stock market is well known to be extremely random, making investment decisions difficult, but deep learning can help. Drawing on a concrete financial use case, Aurélien Géron explains how LSTM networks can be used for forecasting.

Zoubin Ghahramani is a professor at the University of Cambridge, where he leads the Machine Learning Group, and the chief scientist at Uber. His research focuses on probabilistic approaches to machine learning and AI. Zoubin is also deputy director of the Leverhulme Centre for the Future of Intelligence and was a founding Cambridge director of the Alan Turing Institute. In 2015, he was elected a fellow of the Royal Society.

Presentations

Deep dive into probabilistic machine learning Session

Zoubin Ghahramani explores the foundations of the field of probabilistic, or Bayesian, machine learning and details current areas of research, including Bayesian deep learning, probabilistic programming, and the Automatic Statistician. Zoubin also explains how Uber organizes AI research and where probabilistic machine learning fits in.

The frontiers of machine learning and AI Keynote

Zoubin Ghahramani discusses fundamental concepts and recent advances in artificial intelligence, highlighting research on the frontiers of deep learning, probabilistic programming, Bayesian optimization, and AI for data science. Zoubin concludes by considering the societal implications of this work.

Dario Gil is a leading technologist and senior executive at IBM. As vice president of AI and IBM Q, Dario is responsible for IBM’s artificial intelligence research efforts and for IBM’s commercial quantum computing program, IBM Q. Previously, he was the vice president of science and solutions, directing a global organization of 1,500 researchers across 12 laboratories with a broad portfolio of activities spanning the physical sciences, the mathematical sciences, and industry solutions based on AI, IoT, blockchain, and quantum technologies. His research results have appeared in over 20 international journals and conferences, and he is the author of numerous patents. Dario is an elected member of the IBM Academy of Technology. He holds a PhD in electrical engineering and computer science from MIT.

Presentations

AI and quantum computing for business (sponsored by IBM Watson) Session

Over the last five years, AI has become more capable thanks to the availability of data, algorithms, and models. Companies are exploring ways to leverage these advances, and soon AI technology will touch every industry worldwide. Dario Gil explores the challenges faced by companies building AI solutions for enterprise applications and areas of research required to drive this field forward.

The physics of AI (sponsored by IBM Watson) Keynote

The extraordinary progress in AI over the last few years has been enabled, in part, by modern advancements in computing. Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.

Saar Golde is the chief data scientist at Via Transportation, a ride-sharing company focused on shared rides. Previously, Saar built and led the data science practice at information and technology consultancy Knowledgent, was the first analytics solution architect for Revolution Analytics (now part of Microsoft), and served as the chief economist of the virtual world of Gaia Online. He holds a PhD in economics from Stanford University, an MSc in management science from Tel Aviv University, and a BSc in physics and math from the Hebrew University in Jerusalem. Saar is currently on leave from his adjunct position at the engineering school at NYU, where he usually teaches a class on big data in finance.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.

Bruno Gonçalves is a Moore-Sloan fellow at NYU’s Center for Data Science. With a background in physics and computer science, Bruno has spent his career exploring the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts, epidemiological reports, and census data to analyze and model human behavior and mobility. More recently, he has been focusing on the application of machine learning and neural network techniques to analyze large geolocated datasets.

Presentations

word2vec and friends Tutorial

Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture.

Enhao Gong is a PhD student in electrical engineering at Stanford, where he is advised by John Pauly (electrical engineering) and Greg Zaharchuk (radiology). As founder and researcher at Subtle Medical, he is pushing the performance of deep learning methods to boost the efficiency and value for medical imaging. His research focuses on applying machine learning, deep learning, and optimization for medical imaging reconstruction and processing. Recently, Enhao has been working to bridge deep learning methods with MRI reconstruction, such as enhancing image quality with deep learning and multicontrast information, solving quantitative imaging (water-fat separation, QSM, parameter mapping) using deep learning frameworks, and using generative adversarial networks (GANs) for compressed sensing MRI.

Presentations

Deep learning and AI is making clinical neuroimaging faster, safer, and smarter Session

What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making.

Jake Graham is the Director of Product Management for Intel Saffron AI. He has extensive experience developing and implementing solutions leveraging artificial intelligence and the internet of things.

Presentations

How artificial intelligence helps advance day-to-day quality and maintenance decisions Session

In manufacturing, software development, and aerospace, tech-op teams need to make critical decisions on the spot with very little information. In this session, presented by Intel Saffron, the speakers share actual use cases of cognitive AI-based applications helping technical professionals make more confident decisions to solve the pressing issues in their day-to-day work.

Jonathan Greenberg is a senior solutions engineer at Kinetica, where he revels in the pace of change in software and hardware for analytics, the introduction of the GPU-enhanced database, and the business impact around convergence of ML and BI that Kinetica brings to this challenged big data (science) space. Jonathan has spent the last three years at startups exploring modern and innovative analytic technologies and platforms. In his 20-year career conceiving, developing, and delivering effective business intelligence solutions for a broad range of industries, Jonathan has worked for Cognos, BMW, and IBM.

Presentations

Don't get stung by extreme data (or honey bees) (sponsored by Kinetica) Session

Daniel Raskin and Jonathan Greenberg explain what the extreme data economy is about and how machine learning advances along with accelerated parallel computing will play a key role in translating data into instant insight to power business in motion.

Will Griffith is a senior industry consultant within Teradata’s Think Big Analytics division, where he is responsible for driving positive business outcomes in the areas of financial crime and fraud through the application of AI and deep learning. He has more than 25 years’ experience working at the intersection of business and technology in the financial services and payments industry, including 12 years of executive leadership at the largest US credit card issuer. Will’s focus has always been translating complex emerging technologies into opportunities and capabilities that business users can understand and helping organizations develop and implement transformative solutions.

Presentations

Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) Session

Analytic techniques leveraging artificial intelligence can result in dramatic improvements in crime detection and interdiction across diverse attack modalities. Will Griffith and Ben MacKenzie share AI models and operational techniques they’ve used with major banking clients to substantially strengthen and accelerate their responses to criminal attacks.

Funda Gunes is a senior machine learning developer at SAS, where she researches and implements new data mining and machine learning approaches. Her research interests include regularization in machine learning algorithms, Bayesian statistical modeling, mixed models, stacked ensemble models, and using classical statistical methods to enhance deep learning models. Funda holds a PhD in statistics from North Carolina State University.

Presentations

Combining well-established statistical techniques with modern machine learning algorithms Session

As machine learning algorithms and artificial intelligence continue to progress, we must take advantage of the best techniques from various disciplines. Funda Gunes demonstrates how combining well-proven methods from classical statistics can enhance modern deep learning methods in terms of both predictive performance and interpretability.

James Guszcza is chief data scientist at Deloitte and a pioneering member of Deloitte’s original data science practice, where he has applied statistical and machine learning methods to such diverse business problems as healthcare utilization, customer and employee retention, talent management, customer segmentation, insurance pricing and underwriting, credit scoring, child support enforcement, patient safety, claims management, and fraud detection. He also spearheaded Deloitte’s use of behavioral nudge tactics to more effectively act on model indications. A frequent author and conference speaker, Jim designs and teaches hands-on business analytics training seminars for both the Society of Actuaries and the Casualty Actuarial Society, of which he is a fellow and a member of its board of directors. Jim is a former professor at the University of Wisconsin-Madison business school. He holds a PhD in the philosophy of science from the University of Chicago.

Presentations

Executive Briefing: Why AI needs human-centered design Session

AI is about more than automating tasks; it's about augmenting and extending human capabilities. James Guszcza discusses principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-centered design has been crucial to the economic success of an AI project.

Moses Guttmann is CTO and founder of Seematics, a company building a holistic deep learning platform. A 15-year computer vision and deep learning specialist, Moses is a seasoned business and product leader with a two-decade track record in leading and driving execution of large-scale, complex products in a multitude of disciplines. Previously, he founded an innovative semiautomatic 3D conversion company and built face recognition technologies and wavelet-based compression on embedded systems for various companies. He holds an MSc in computer science (cum laude) from Tel Aviv University.

Presentations

Why data management for deep learning computer vision is challenging Session

One of the most important aspects of deep learning is the quality and quantity of the data used in the learning process. Moses Guttmann explores the problem and offers approaches to solve it.

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

Presentations

Bringing AI into the enterprise Tutorial

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

Mark Hammond is cofounder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works. He has been thinking about AI throughout his career, which has included positions at Microsoft and startups such as Numenta and in academia, including a turn in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.

Presentations

Deep reinforcement learning’s killer app: Intelligent control in real-world systems Session

Reinforcement learning is a powerful machine learning technique for solving problems in dynamic and adaptive environments. Mark Hammond dives into two real-world case studies and demonstrates how to build and deploy deep reinforcement learning models for industrial applications.

Tom Hanlon is a senior instructor at Functional Media, 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, and 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? Tom Hanlon demonstrates how to use Deeplearning4j to build recurrent neural networks for time series data.

Neural networks for time series analysis using Deeplearning4j (Day 2) Training Day 2

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? Tom Hanlon demonstrates how to use Deeplearning4j to build recurrent neural networks for time series data.

John Hebeler is the chief data scientist and principal engineer for the RMS Division of Lockheed Martin, where he just finished a five-year program to analyze large, diverse data streams to form complex policy determinations in a big data event-driven architecture. John holds three patents and is the coauthor of two technical books on networking and data semantics. He presents at technical and business conferences throughout the world. Previously, he served as an adjunct professor for both Loyola University and University of Maryland. John holds a BS in electrical engineering, an MBA, and a PhD in information systems. In his free time, he’s an avid tennis player and beer brewer.

Presentations

Determining normal (and abnormal) using deep learning Session

Determining abnormal conditions depends on maintaining a useful definition of normal. John Hebeler offers an overview of two deep learning methods to determine normal behavior, which when combined further improve performance.

Justin Herz was named Executive Vice President, Digital Product, Platform, and Strategy in January 2017.

As head of Warner Bros. Digital Herz oversees emerging technology, new platforms, and direct-to-consumer technology working across all divisions of the studio identifying and spearheading technology-related innovations intersecting with new digital products, production, distribution, and marketing.

Digital’s portfolio includes digital innovation, technology development, standard setting for the studio, direct to consumer platforms, and Warner’s consumer intelligence/data activities designed to generate value by creating and enhancing consumer engagement through direct interaction with Warner Bros. products, programs and partners.

Presentations

Increasing business results through AI in the entertainment industry Keynote

In this fireside chat, Justin Herz and Fiaz Mohammed discuss how artificial intelligence can improve content discovery and monetization. In collaboration with Intel AI technologies, Warner Bros. is just scratching the surface of what’s possible.

Drew Hodun is an ML specialist on the Google Cloud team, where he advises financial, autonomous, and tech customers implementing cutting-edge ML use cases and systems on Google Cloud and in hybrid environments. His work ranges from operationalizing ML to TensorFlow to GPU/TPU perf tuning.

Presentations

Democratizing AI (sponsored by Google Cloud) Session

Drew Hodun explores the progress Google is making to decrease the amount of work needed to go from "zero to AI."

Kathryn Hume is vice president of product and strategy for integrate.ai, a SaaS startup applying AI to drive growth and customer satisfaction for large enterprises, and a venture partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. Previously, Kathryn was the director of sales and marketing at Fast Forward Labs (Cloudera), where she helped Fortune 500 companies accelerate their machine learning and data science capabilities, and a principal consultant in Intapp’s Risk practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. She has given lectures and taught courses on the intersections of technology, ethics, law, and society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.

Presentations

Executive Briefing: Building an AI-first enterprise culture Session

Large enterprises struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. AI requires a radical shift. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture.

Randall Hunt is a Los Angeles-based senior technical evangelist and software engineer at Amazon Web Services. Python is his favorite language, but he can sometimes be found in the dark realm of C++. Randall is the author of a number of open source projects and a contributor to MongoDB, Homebrew, boto, and several other tools and libraries. Previously, Randall launched rockets at NASA and SpaceX, but he found his programming passion at MongoDB. He is a total space nerd.

Presentations

Building scalable machine learning workflows with Amazon SageMaker (sponsored by Amazon Web Services) Session

Amazon SageMaker is a fully managed machine learning platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models in the cloud, at any scale. Randall Hunt offers an overview of SageMaker and demonstrates an end-to-end machine learning workflow by building an ML-powered Twitter bot that you can interact with in real time.

Andrew Ilyas is an undergraduate student at the Massachusetts Institute of Technology.

Presentations

Fooling neural networks in the physical world Session

Andrew Ilyas, Logan Engstrom, and Anish Athalye share an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.

Jamie Irza is a senior principal systems engineer at Raytheon Integrated Defense Systems, where her recent work has been focused on the application of machine learning techniques to implement activity-based intelligence (ABI) algorithms to enhance the performance of sensors such as radars and imaging systems. Jaime’s technical specialization includes systems engineering, signal processing, and machine learning. Jamie holds a BSEET with a minor in mathematics from Roger Williams University and an MSEE from the University of Rhode Island.

Presentations

Using machine learning to enhance activity-based intelligence Session

Activity-based intelligence (ABI) is the art and science of understanding normal patterns of life to enhance the ability of a system to detect anomalous behavior (e.g., to identify cases of credit card fraud). Jamie Irza demonstrates how machine learning can be used to implement ABI for detecting threatening behavior from unmanned aerial systems, commonly known as drones.

Dominique Izbicki is a senior director of product management in the technology and product organization at Comcast, where she focuses on using artificial intelligence to enhance Xfinity products, specifically concentrating on advancement of content discovery across Comcast products and creating custom viewing experiences. Dominique is a thought leader who is helping to develop and mobilize the company’s initial artificial intelligence strategy. Previously, she was a director of strategy and planning in strategic development, focusing on overall portfolio management of new growth businesses and initial operationalization of those initiatives, and was part of the X1 team responsible for business operations for new Xfinity TV products. Dominique started her career as a member of General Electric’s prestigious Information Management Leadership Development Program and spent her time at GE in application development, management, and supplier management, in both the US and Ireland. Dominique holds an MBA from the Wharton School of the University of Pennsylvania and a BS in information sciences and technology from Pennsylvania State University. She currently resides in Philadelphia with her husband, Matt, and two children.

Presentations

How Comcast uses AI to reinvent the customer experience Session

Jan Neumann and Jeanine Heck explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers' questions or resolve their concerns.

Steven Rennie is the director of research at Fusemachines, an AI solutions and services company whose mission is to make AI accessible to everyone through education, software, and services. Previously, Steve worked at the IBM TJ Watson Research Center, where he led the Multimodal Group in the Watson Division. He has published over 50 peer-reviewed papers on machine learning and AI applications, including source separation, robust automatic speech recognition (ASR), multitalker speech recognition, LVCSR, graphical models, data-driven computational auditory scene analysis, machine translation, probabilistic array processing, reinforcement learning, and image captioning. He has served as a committee member for a number of leading conferences, including ICLR, AI-STATS, ACL, COLING, SIGGRAPH, INTERSPEECH, ICASSP, and ASRU, TASL, ICML, and NIPS. Steve was recently elected to the IEEE’s prestigious Speech and Language Technology Committee (SLTC) and has advanced the state-of-the-art in performance on several AI challenges, including the Pascal Speech Separation and Recognition Challenge, the Aurora 4 Noise Robust ASR Database, the Switchboard LVCSR Evaluation Benchmark, and most recently, the MSCOCO Image Captioning Challenge. He holds a PhD in electrical and computer engineering from the University of Toronto, with a dissertation titled Graphical Models for Speech Recognition in Adverse Environments. His primary research interest is in developing novel, practical algorithms for information processing that leverage graphical modeling and deep, reinforcement, and adversarial learning techniques.

Presentations

Building winning AI technology: The anatomy of a champion Session

Over the last year, Steve Rennie and his colleagues have significantly advanced the state of the art in performance on two flagship challenges in AI: the Switchboard Evaluation Benchmark for Automatic Speech Recognition and the MSCOCO Image Captioning Challenge. Steve shares the innovations in deep learning research that have most advanced performance on these and other benchmark AI tasks.

Alejandro (Alex) Jaimes is vice president of AI and data science at Nauto, where he leads the company’s efforts in deep learning, computer vision, machine learning, and data science. His work focuses on mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. Alex is a scientist and innovator with 15+ years of international experience in research leading to product impact at companies including Yahoo, KAIST, Telefónica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs. Previously, Alex was head of R&D at DigitalOcean, CTO at AiCure, and director of research and video products at Yahoo, where he managed teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. He was also a visiting professor at KAIST. He has published widely in top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc.) and is a frequent speaker at international academic and industry events. He holds a PhD from Columbia University.

Presentations

Deploying deep learning in the cloud Session

Alex Jaimes explains how the cloud can be used effectively to deploy deep learning and the factors that allow you to do so cost effectively. Along the way, Alex shares examples of when and how to deploy deep learning in the cloud as well as the corresponding benefits, challenges, and opportunities.

Mustafa Kabul is a data scientist in the Analytic Server Division of R&D at SAS, where he leads innovative projects for SAS’s next-generation AI-enabled analytics products, including applications of deep learning. His current focus is on applying deep reinforcement learning to operational problems in the CRM and IoT spaces. An operations research expert working at the interface of machine learning and optimization, previously, he developed distributed, large-scale integer optimization algorithms for marketing optimization problems. Ever the optimization enthusiast, Mustafa always looks into ways to improve the algorithms. Nowadays his favorites are the distributed stochastic gradient and online learning methods. Mustafa holds a PhD from the University of North Carolina at Chapel Hill, where his research focused on game theory models of supply chains selling to strategic customers.

Presentations

Long-term time series forecasting with recurrent neural networks Session

Forecasting the long-term values of time series data is crucial for planning. But how do you make use of a recurrent neural network when you want to compute an accurate long-term forecast? How can you capture short- and long-term seasonality or discover small patterns from the data that generate the big picture? Mustafa Kabul shares a scalable technique addressing these questions.

Kathleen Kallot is a product marketing manager at Intel, where she leads marketing and business enablement for the Intel Movidius neural compute platforms within Intel’s Artificial Intelligence Product Group. An entrepreneur, developer, maker, and innovator, Kathleen believes that given the right tools, everybody can create and develop innovative products. Previously, she served in various roles within Intel mainly focused on fostering and developing new businesses and ecosystems around the IoT, robotics, wearables, AR/VR, and drones.

Presentations

AI at the edge with Intel Movidius technology Session

Kathleen Kallot and Augustin Marty explain how Intel Movidius technology is reducing the complexity of developing custom circuit boards and allowing developers and companies to prototype AI applications with the Intel Movidius Neural Compute Stick. They also demonstrate how the newly announced Intel AI: In Production program makes it easier to bring these designs to market.

Presentations

Explaining machine learning for consumer loans Session

Historically, the consumer loan industry has restricted itself to using relatively simple machine learning models and techniques to accept or deny loan applicants. However, more powerful (but also more complicated) methods can significantly improve business outcomes. Sean Kamkar shares a framework for evaluating, explaining, and managing these more complex methods.

Manas Ranjan Kar is a senior manager at US healthcare company Episource, where he leads the NLP and Data Science practice, works on semantic technologies and computational linguistics (NLP), builds algorithms and machine learning models, researches data science journals, and architects secure product backends in the cloud. He has architected multiple commercial NLP solutions in the area of healthcare, food and beverages, finance, and retail. Manas is deeply involved in functionally architecting large-scale business process automation and deep insights from structured and unstructured data using NLP and ML. He has contributed to NLP libraries including Gensim and Conceptnet5 and blogs regularly about NLP on forums like Data Science Central, LinkedIn, and his blog, Unlock Text. Manas speaks regularly about NLP and text analytics at conferences and meetups, such as PyCon India and PyData, has taught hands-on sessions at IIM Lucknow and MDI Gurgaon, and has mentored students from schools including ISB Hyderabad, BITS Pilani, and the Madras School of Economics. When bored, he falls back on Asimov to lead him into an alternate reality.

Presentations

Building a healthcare decision support system for ICD10/HCC coding through deep learning Session

Episource is building a scalable NLP engine to help summarize medical charts and extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. Manas Ranjan Kar offers an overview of the wide variety of deep learning algorithms involved and the complex in-house training-data creation exercises that were required to make it work.

Srinivasa Manohar Karlapalem is a software engineer at Intel, where he develops and optimizes novel deep learning-based applications on compute and memory-constrained edge and IoT hardware architectures. He has extensive experience optimizing programming language VM runtimes and OS stacks on modern, high-performance Intel architectures. He holds an MS in computer science from the Georgia Institute of Technology.

Presentations

High-throughput single-shot multibox object detection on edge devices using FPGAs Session

Srinivasa Karlapalem demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance.

Murali Kaundinya is a senior strategist for technology and architecture. Murali has extensive leadership and management consulting experience and has served in leadership roles conceiving, executing, and delivering transformational programs with Fortune 100 enterprises within financial services, health and life sciences, insurance, and advanced technology. Previously, he was a technology fellow at Goldman Sachs, where he transformed the firm’s distributed software engineering practices into a centrally managed platform optimizing on innovation, productivity, risk management, and compliance; held leadership roles at Sun Microsystems (now part of Oracle), where he provided strategy consulting services to CxOs of Sun’s top clients across the world; and started his career at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. He has published and presented extensively on many technology topics and holds several patents in RFID and in the field of telemedicine.

Presentations

An open extensible AI platform implementing four use cases for the enterprise Session

Murali Kaundinya outlines an InnerSource model to curate and operationalize machine learning and deep learning algorithms with a common workflow and engaging user experience. Focusing on patterns and practices, Murali then shares lessons learned implementing four enterprise scale use cases: optical character recognition, release engineering, virtual customer assistants, and data unification.

Serina Kaye is a principal program manager in the Cloud AI Group at Microsoft. She works on Azure Machine Learning, creating tools and services to help data scientists be more productive. Serina has over 20 years of industry experience delivering globally scaled products and services. Previously, she led the creative products team for Xbox advertising and was a group manager at MSDN and MSN. She holds an MBA from the Michael G. Foster School of Business. She grew up in Toronto, Canada.

Presentations

Computational creativity: Making music with AI technologies (sponsored by Microsoft) Session

Erika Menezes and Serina Kaye share a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.

Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic, a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.

Presentations

Customer-centered AI: A radical strategy Tutorial

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

Stephanie Kim is a developer evangelist at Algorithmia, where she enjoys writing accessible documentation, tutorials, and scripts to help developers find fun and useful ways to incorporate machine learning into their smart applications. Stephanie is the founder of Seattle PyLadies and a co-organizer of the Seattle Building Intelligent Applications Meetup. She enjoys machine learning projects, particularly ones where she gets to dive into unstructured text data to discover friction points in the UI or find out what users are thinking with natural language processing techniques. Her passions include machine learning, NLP, and writing helpful and fun articles that make machine learning accessible to anyone. She has spoken at a number of conferences, including PyData and ACT-W, a women’s tech conference, where she gave a talk that was turned into a blog post.

Presentations

Racial bias in facial recognition software Session

Stephanie Kim discusses the basics of facial recognition and the importance of having diverse datasets when building out a model. Along the way, she explores racial bias in datasets using real-world examples and shares a use case for developing an OpenFace model for a celebrity look-alike app.

Myles Kirby is a commercial principal at ASI. Miles has extensive experience in data and digital transformation in the private and public sectors. Over his career, he has worked with executive teams across six industries in four continents, including advising the UK government on its National Digital Transformation strategy and developing a new data strategy for a FTSE 10 oil and gas company. Previously, he was a manager at Accenture, where he cofounded the Digital Strategy practice. His policy research on innovation and entrepreneurship has been covered in international media outlets such as Wired, the Wall Street Journal, and the Financial Times.

Presentations

AI for managers 2-Day Training

Richard Sargeant and Myles Kirby offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

AI for managers (Day 2) Training Day 2

Richard Sargeant and Myles Kirby offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

David Kiron is the executive editor of MIT Sloan Management Review, where he directs the Big Ideas Initiative, a content platform examining macrotrends that are transforming the practice of management. David has coedited two books on economics; coauthored 20+ journal articles and research reports on analytics, sustainability, and digital technology; and written 50+ Harvard Business School case studies. He currently serves as an expert panelist on the Fraunhofer Institute’s Future of Operating Procedures project. David holds a PhD in philosophy from the University of Rochester and a BA from Oberlin College.

Presentations

Executive Briefing: The adoption of artificial intelligence in business—Why leaders forge ahead and laggards fall behind Session

Few organizations have mastered integrating AI technology into their business processes and offerings, and many who want to don’t fully understand the work that lies ahead. David Kiron shares surprising insights about businesses’ appetite for and approach to AI, drawn from global collaborative research conducted by MIT Sloan Management Review and The Boston Consulting Group.

Max Kleiman-Weiner is a cofounder and chief scientist of Diffeo as well as a PhD student in computational cognitive science at MIT, funded by the NSF and the Hertz Foundation. He won best paper at RLDM 2017 for models of human cooperation and the William James Award at SPP for computational work on moral learning. Previously, he was a Fulbright fellow in Beijing. Max holds an MSc in statistics from Oxford, where he was a Marshall scholar, and an undergraduate degree from Stanford, where he was a Goldwater scholar.

Presentations

Collaborative machine intelligence: Accelerating human knowledge Session

Recent advances have made machines more autonomous, but much work remains for AI to collaborate with people. Emily Pavlini and Max Kleiman-Weiner share new insights inspired by the way humans accumulate knowledge and naturally work together that enable machines and people to work and learn as a team, discovering new knowledge in unstructured natural language content together.

Kavya Kopparapu is the founder and CEO of GirlsComputingLeague. She is dedicated to sharing her passion for computer science with others, especially young girls, as the field has given her a world of opportunities. Kavya has been recognized by such organizations as the White House and the National Center for Women in Information Technology (NCWIT). Recently, she spoke on computer science for all at the March for Science in Washington, DC, and presented at a TEDx Conference. Kavya is a senior at Thomas Jefferson High School for Science and Technology.

Presentations

Fireside chat with Peter Norvig and Kavya Kopparapu Keynote

Fireside chat with Peter Norvig and Kavya Kopparapu

Neeyanth Kopparapu is a Sophomore at Thomas Jefferson High School for Science and Technology. First introduced to computer science in seventh grade, Neeyanth has been unable to stop coding, whether it be for his artificial intelligence class at school or for his numerous AI interfaces. Neeyanth is also a co-founder of GirlsComputingLeague, a nonprofit dedicated to sharing his passion of computer science to underprivileged students through the use of lectures, workshops, and free classes.

Presentations

Using Artificial Intelligence in the field of Diagnostics Session

With the improvement of medical devices in the technological era, doctors have access to an enormous amount of unharnessed medical data. Artificial Intelligence is a tool that can be used to process this data to solve problems that are considered hard or impossible as a doctor. These AI tools is what Neeyanth used to help the field of diagnostics enter the digital age.

Tbd

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.

Ryan Kottenstette is cofounder and CEO of Cape Analytics, a leading computer vision and machine learning company backed by DCVC, Formation8, and XL Innovate, which ingests geospatial imagery and extracts property data for millions of homes at massive scale. A former principal at Khosla Ventures, Ryan has been at the forefront of using AI to disrupt traditional industries: he was a lead investor in Blue River Technologies, which sold to John Deere in 2017 for $300M.

Presentations

How artificial intelligence is transforming traditional industries, from property insurance to agriculture Session

There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries. Ryan Kottenstette shares lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture.

Tim Kraska is an associate professor of electrical engineering and computer science at MIT’s Computer Science and Artificial Intelligence Laboratory. Currently, his research focuses on building systems for machine learning and using machine learning for systems. Tim spent the majority of 2017 at Google Research, where he invented the concept of learned index structures with the MLX and Brain teams. Tim was recently selected as a 2017 Alfred P. Sloan Research Fellow in computer science. He has also received the 2017 VMware Systems Research Award, NSF CAREER Award, an Air Force Young Investigator award, two Very Large Data Bases (VLDB) conference best demo awards, and a best paper award from the IEEE International Conference on Data Engineering (ICDE).

Presentations

Learned index structures Session

Tim Kraska explains how fundamental data structures can be enhanced using machine learning with wide-reaching implications even beyond indexes, arguing that all existing index structures can be replaced with other types of models, including deep learning models (i.e., learned indexes).

Machine learning just ate algorithms in one large bite Keynote

Recent results show that machine learning has the potential to significantly alter the way basic data structures and algorithms are implemented and the performance they can provide. Tim Kraska explains the basic intuition behind learned data structures and outlines the potential consequences of this technology for industry.

Harsh Kumar is a business development manager at Intel, where he focuses on system simulation products for the IoT, autonomous cars, the cloud, and memory subsystems.

Presentations

An end-to-end video analytics solution for surveillance and securing high-value assets Session

Harsh Kumar explains one way the energy industry is using AI and computer vision for security surveillance: a video analytics solution that can be optimized for the functional safety of workers in the loading and unloading zone of an oil and gas offshore rig.

Tolga Kurtoglu is CEO of PARC, a Xerox company, which provides custom R&D services, technology, specialized expertise, best practices, and intellectual property to Xerox’s business groups, Fortune 500 and Global 1000 companies, startups, and government. Tolga oversees PARC’s R&D investments for Xerox and its innovation portfolio for commercial clients and government agencies in a diverse set of focus areas and competencies, including human-centered innovation services, intelligent agents and systems, clean energy, smart packaging, machine learning and analytics, security and privacy, printed electronics, and digital manufacturing. In his early years at PARC, he pioneered the formation of PARC’s digital design and manufacturing (DDM) program. Later he created and led the System Sciences Laboratory, building a technology portfolio across hardware, software, and process technologies. In both roles, he managed multimillion-dollar R&D investments and product strategy encompassing several platforms and market offerings and led successful transition of inventions from an R&D output to commercial software systems and services. Prior to PARC, he was a researcher at NASA’s Ames Research Center and a mechanical design engineer at Dell Corporation.

Tolga’s research focuses on computation and artificial intelligence applied to design and manufacturing of complex systems, and application of preventive and predictive analytics techniques to engineered systems. He has published over 80 peer-reviewed articles and papers in leading journals and conferences in his field and regularly serves in organizational leadership roles for the ASME, AIAA, AAAI, Design Society, and Prognostics and Health Management Society. He is the recipient of the IEEE Best Professional Paper Award at the Prognostics and Health Management Conference, IEEE Best Application Paper Award from IEEE Robotics and Automation Society, NASA Ames Technical Excellence Award, PARC Excellence Award, PARC Golden Acorn Award, and the Best Design Award in “Dexterous Robot Hand” Design Competition. Tolga holds a PhD from the University of Texas at Austin, an MS from Carnegie Mellon University, and a bachelor’s degree from Orta Dogu Technical University (ODTU)—all in mechanical engineering.

Presentations

Executive Briefing: Making reliable and trustworthy AI systems a reality Session

Tolga Kurtoglu walks you through the advanced technology needed to implement cyberphysical systems, covering the right hardware to sense the right data, explainable AI, and designing security for trustworthy operations. Along the way, Tolga shares case studies and examples of advanced tech deployments.

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

Presentations

Democratizing deep reinforcement learning Session

Danny Lange offers an overview of deep reinforcement learning—an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life—and explains how the crossroads between machine learning and gaming offers innovations that are applicable in other fields of technology, such as the robotics and automotive industries.

Shane Lewin is principal manager of AI and research at Microsoft. Shane has been shipping products in technology and AI for over 10 years, with experience spanning everything from large companies such as Netflix and Microsoft to early-stage startups, including Gliimpse and OpenTalent, to companies making the transition from smaller company to large organization, such as Powerset and Shutterstock. Most recently, he was vice president of product for data science at Lumiata. His portfolio includes a medical AI engine that identifies high-risk patients to enable early intervention; an image search engine that allows customers to search by emotion, mood, and context; a self-learning customer communication and email platform that increased retention while reducing total emails; a data optimization platform that saved over $1M per year on a nearly 10x cost reduction; and the massive distributed document summarization engine that generates all the text you see on Bing. Shane holds an MS in computational and mathematical engineering from Stanford University and degrees in molecular biology and mathematics from the University of Colorado at Boulder.

Presentations

Executive Briefing: Lean AI product development (and common pitfalls) Session

Great AI products are more than technology; they are built on a clear (computationally tractable) model of customer success. Getting that model right can be more challenging than building the AI models themselves; and getting it wrong is very expensive. Shane Lewin outlines common pitfalls in defining AI products and explains how to organize teams to solve them.

Michael Li is the founder and CEO of the Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to fellows and routinely accepts just 1% of applicants. Employers engage with the Incubator as hiring partners. Previously, Michael was a data scientist 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, so he decided to build a startup that lets him focus on what he really loves. He holds a PhD from Princeton, where he was a Hertz fellow, and read Part III Maths at Cambridge, where he was a Marshall scholar. Michael enjoys the opera, rock climbing, and attending geeky data science events.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.

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.

Tuesday opening remarks Keynote

Artificial Intelligence program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Understanding automation Keynote

Keynote by program chairs Ben Lorica and Roger Chen

Wednesday opening remarks Keynote

Artificial Intelligence program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Andre Luckow is a project manager and researcher at the BMW IT Research Center in Greenville, South Carolina, where his work focuses on interdisciplinary research and applications at the intersection of data infrastructure, data science, and machine learning in the automotive domain. His specialty is the application of computing technologies to problems in business and science bridging cross-functional gaps to create value via process improvements or the enablement of new types of products. He is particularly interested in deep learning applications and system-level challenges related to deep learning, streaming, and edge computing. Previously, Andre served in a number of positions at BMW Group IT in Munich, Germany. He holds a PhD in the field of distributed computing from the University of Potsdam, Germany.

Presentations

AI applications, best practices, and lessons learned in the automotive domain Session

AI delivers value to many facets of the automotive value chain, including smart manufacturing, supply chain management, and customer engagement. Andre Luckow discusses how to assess AI technologies, validate use cases, and foster fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications.

Zhenxiao Luo is an engineering manager at Uber, where he runs the interactive analytics team. Previously, he led the development and operations of Presto at Netflix and worked on big data and Hadoop-related projects at Facebook, Cloudera, and Vertica. He holds a master’s degree from the University of Wisconsin-Madison and a bachelor’s degree from Fudan University.

Presentations

Caching big data for machine learning platform at Uber Session

From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Zhenxiao Luo explains how Uber tackles data caching in large-scale machine learning, exploring Uber's machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speed up AI jobs.

Presentations

Deploying AI in the fight against financial crime in the banking industry (sponsored by Teradata) Session

Analytic techniques leveraging artificial intelligence can result in dramatic improvements in crime detection and interdiction across diverse attack modalities. Will Griffith and Ben MacKenzie share AI models and operational techniques they’ve used with major banking clients to substantially strengthen and accelerate their responses to criminal attacks.

Jennifer Marsman is the principal software engineer for Microsoft’s AI for Earth Group, where she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. Previously, Jennifer was a software developer in Microsoft’s Natural Interactive Services Division, where she authored two patents related to search and data mining algorithms. She has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Since 2016, Jennifer has been recognized as one of the top 100 most influential individuals in artificial intelligence and machine learning by Onalytica, reaching the #2 slot in 2018, and in 2009 was chosen as the “techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the Best in Role award for technical evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity and Inclusion Award, Gold Club, and Platinum Club. Jennifer is a frequent speaker at software development conferences around the world. She holds a bachelor’s degree in computer engineering and a master’s degree in computer science and engineering from the University of Michigan in Ann Arbor, where she specialized in artificial intelligence and computational theory. To learn more, check out her blog.

Presentations

Using machine learning, the IoT, drones, and networking to reduce world hunger (sponsored by Microsoft) Keynote

Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, machine learning, and networking research in television whitespaces and discusses the AI for Earth grant program, which supports similar work in the areas of clean water, agriculture, biodiversity, and climate change.

David Martin works at IBM Watson, where he is currently investigating the full stack and full lifecycle of cognitive agents using the scenario of eldercare assistance. A pioneer and early adopter in the web, the cloud, ecommerce, and data sciences, David holds a number of patents.

Presentations

The cognitive IoT and eldercare Session

David Martin explores cognitive function in conjunction with edge computing and IoT sensors and actuators for eldercare scenarios—specifically the identification of individuals, daily activity monitoring, and aberration detection performed on-premises using HomeAssistant, the Intu open source project, and IBM's Watson cognitive services.

Augustin Marty is cofounder and CEO of Deepomatic, a company developing a software platform allowing any worker from any company to build image or video recognition applications and exploit them locally, in embedded systems. Previously, he worked in operations, sales, and technologies for large infrastructure companies in India, China, and France. Augustin studied mathematics and economics at École des Ponts ParisTech and UC Berkeley.

Presentations

AI at the edge with Intel Movidius technology Session

Kathleen Kallot and Augustin Marty explain how Intel Movidius technology is reducing the complexity of developing custom circuit boards and allowing developers and companies to prototype AI applications with the Intel Movidius Neural Compute Stick. They also demonstrate how the newly announced Intel AI: In Production program makes it easier to bring these designs to market.

Peter Mattson is a staff engineer at Google Brain, where he originated and coordinates the multi-organization MLPerf benchmarking effort. Previously, he led the Programming Systems and Applications Group at NVIDIA Research, was VP of software infrastructure for Stream Processors Inc (SPI), and was a managing engineer at Reservoir Labs. He has authored more than a dozen technical papers as well as four patents. His research focuses on accelerating and understanding the behavior of machine learning systems by applying novel benchmarks and analysis tools. Peter holds a PhD and MS from Stanford University and a BS from the University of Washington.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

Dan Romuald Mbanga is a global lead business development manager at AWS, where he leads business and technical initiatives involving Amazon AI platforms such as Amazon SageMaker, designed to provide end-to-end machine learning environments for AWS’s customers. He helps AWS customers in all GEOs, as well as internal AWS stakeholders across data science, product development, marketing, sales, and technical support achieve success with AWS’s machine and deep learning technologies. Previously, Dan was a big data and DevOps engineering manager at AWS, where he built and led two teams of specialized engineers on the Hadoop ecosystem and in CI/CD technologies. Dan holds BS degrees in physics and computer science from the University of Buea. In his spare time, he enjoys traveling, hacking hardware electronics, and learning new languages.

Presentations

Rapid AI experimentation and innovation on Amazon Web Services (sponsored by Amazon Web Services) Keynote

For more than 20 years, Amazon has invested in experimenting and deploying AI at scale. Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go and how developers and data scientists from startups to large-scale enterprises have benefited from this innovation.

Brian McMahan is a research engineer at Joostware, a San Francisco-based company specialized in consulting and building intellectual property in natural language processing and deep learning. He is also a cofounder at R7 Speech Sciences, a company focused on understanding spoken conversations. Brian is wrapping up his PhD in computer science from Rutgers University, where his research focuses on Bayesian and deep learning models for grounding perceptual language in the visual domain. Brian has also conducted research in reinforcement learning and various aspects of dialogue systems.

Presentations

Natural language processing with deep learning 2-Day Training

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

Tatiana Mejia is head of AI product marketing and strategy at Adobe, where she leads AI product marketing and strategy for Adobe Sensei, the company’s AI and machine learning framework focused on solving digital experience challenges across the creative, marketing, and digital document realms. She has more than 15 years of experience in machine learning, digital marketing, social marketing, and SaaS. Tatiana holds a MBA from the Stanford Graduate School of Business.

Presentations

AI in the digital age: Friend, not foe (brought to you by Adobe) Session

AI will change—and in some ways already is changing—the way we work, live, and play at a scale the world has never experienced. Join Tatiana Mejia to see how marketers, designers, and creative professionals can gain huge benefits in productivity, content scale, and workflow efficiencies while unleashing expanded career opportunities for workers in these industries.

Chad Meley is vice president of marketing at Teradata, where he is responsible for Teradata’s analytical ecosystem and artificial intelligence products and solutions. Chad understands trends in the analytics and big data space and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata engineers, consulting teams, and technology partners. Previously, he led Electronic Arts’s Data Platform organization, which supported game development, finance, and marketing, and held a variety of other leadership roles at Dell and FedEx centered around data and analytics. He has been recognize with the Best Practice Award for Driving Business Results in Data Warehousing from the Data Warehouse Institute, the Marketing Excellence Award from the Direct Marketing Association, and the Marketing Gold Award from MarketingSherpa. Chad is a regular speaker at conferences, including the O’Reilly AI Conference, Strata, DataWorks, and Teradata Partners. He holds a BA in economics from the University of Texas and an MBA from Texas Tech University and performed postgraduate work at the University of Texas.

Presentations

How AI produces high-impact business outcomes in the finance, manufacturing, travel, transportation, and pharmaceutical industries (sponsored by Teradata) Session

AI has already begun to demonstrate its value in large enterprises, even outside of Silicon Valley and other West Coast digital giants. Fortune 500 companies in industries like finance, manufacturing, travel, transportation, and pharmaceuticals have begun to leverage its power. Chad Meley shares insights from real-world client engagements using deep learning.

Erika Menezes is a software engineer in the Cloud AI Group at Microsoft where she builds deep learning and AI solutions that leverage Microsoft’s AI technologies. Erika holds an MS from Carnegie Mellon University, where she worked as a research assistant on several machine learning and NLP projects. She is part of multiple efforts to champion diversity and inclusion in the tech industry.

Presentations

Computational creativity: Making music with AI technologies (sponsored by Microsoft) Session

Erika Menezes and Serina Kaye share a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.

Taniya Mishra is the lead speech scientist at Affectiva, where her current research focuses on developing techniques for estimating human emotion from spoken utterances, with a goal to improve human-machine or human-human communication. These techniques involve training deep learning models from speech, either alone or in conjunction with other information streams, such as text or facial expressions, to estimate a speaker’s emotion about the topic at hand, their engagement in a task, their confidence, or their stress level. Taniya’s past research includes text-to-speech synthesis, voice search, and usage of the latter in child-directed and accessibility applications. Taniya has been a coauthor on more than 25 technical publications and has been awarded more than 12 patents related to speech technology. She is passionate about STEM education and mentoring. Taniya holds a PhD in computer science from the OGI School of Science and Engineering at OHSU.

Presentations

Humanizing technology: Emotion detection from face and voice Session

Drawing on Affectiva's experience building a multimodal emotion AI that can detect human emotions from face and voice, Taniya Mishra outlines various deep learning approaches for building multimodal emotion detection. Along the way, Taniya explains how to mitigate the challenges of data collection and annotation and how to avoid bias in model training.

Fiaz Mohamed leads business development for the Artificial Intelligence Products Group at Intel. In his role, Fiaz works with Fortune 500 organizations to help deploy AI solutions at scale. Fiaz has previously held senior sales, marketing, and business development roles at several successful enterprise software companies and also advised clients in the technology sector on growth and go-to-market strategy as a management consultant at McKinsey & Company.

Presentations

Increasing business results through AI in the entertainment industry Keynote

In this fireside chat, Justin Herz and Fiaz Mohammed discuss how artificial intelligence can improve content discovery and monetization. In collaboration with Intel AI technologies, Warner Bros. is just scratching the surface of what’s possible.

Intel AI for the enterprise ecosystem Keynote

The Intel AI portfolio includes hardware and software solutions that span use cases and edge-to-cloud implementations, rooted in extensive expertise in data science and research. Fiaz Mohamed explains how Intel AI solves today’s business problems and how Intel’s partner ecosystem is accelerating the adoption of solutions built on Intel technology.

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.

Presentations

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms.

Jayanti Murty is technology evangelist at Digitate. An experienced IT leader with nearly two decades of industry experience, Jayanti has held a variety of IT leadership roles with a focus on business development, innovation, and technology consulting at Digitate and its parent company TCS. Previously, he was a product specialist for the company’s ignio and Cloud Plus platforms, and his successful tenure leading the TCS IT performance consulting practice for North America included defining a future vision and strategy for the group and delivering solid growth. He has also led and delivered large, high-profile engagements for customers in the telecom, banking, retail, healthcare, transportation, logistics, and government sectors in markets including the Americas, the UK, the Asia-Pacific, and India. Jayanti’s key areas of interest include analytics, high-performance computing, architecture and design, modeling, and optimization for large IT systems. He has authored whitepapers on performance engineering and capacity planning that have been published by ROSATEA and CMG and has worked as evaluator and chair for the TCS Technical Architects Conference. Jayanti holds a bachelor of technology in mechanical engineering from the National Institute of Technology, Warangal in India.

Presentations

Removing complexity for workload automation with machine learning (sponsored by Digitate) Session

Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms and struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Drawing on real-world examples, Jayanti Murty explains how ignio can reduce operational risks and outages, enabling you to more quickly adapt to change.

Yacin Nadji is a research scientists at the Georgia Institute of Technology. An expert in computer security, he has worked at numerous companies building and improving machine learning-based fraud and abuse detection systems at scale. Yacin is the author of 16 academic publications with over 600 citations, has served as a reviewer for academic security conferences and journals, and has given talks at several industry conferences and symposia. He holds a PhD in computer science from the Georgia Institute of Technology.

Presentations

Adversarial ML: Practical attacks and defenses against graph-based clustering Session

The adversarial nature of security makes applying machine learning complicated. If attackers can evade signatures and heuristics, what is stopping them from evading ML models? Yacin Nadji evaluates, breaks, and fixes a deployed network-based ML detector that uses graph clustering. While the attacks are specific to graph clustering, the lessons learned apply to all ML systems in security.

Mridu Narang is a senior engineer at Microsoft, where she builds foundational algorithms and scalable machine learning systems focused on solutions for natural language question-answering systems. In her time at Microsoft, Mridu has contributed on entity linking, temporal fact extraction, and photosynth projects.

Presentations

From answering questions to questioning answers: Challenges of large-scale QnA systems Session

In a world of information overload and manipulation, knowledge acquisition techniques are expected to provide instant, precise, and succinct answers. Question-answering (QnA) systems must serve answers with high accuracy and be backed by strong verification techniques. Mridu Narang offers an overview of the challenges of and approaches taken by large-scale QnA systems.

Arshak Navruzyan is chief technology officer at Sentient, where he is responsible for leading the engineering direction and vision for Sentient’s core distributed artificial intelligence (DAI) platform and leads the data science team in support of Sentient’s intelligent commerce offerings and trading for Sentient Investment Management. Arshak has delivered AI solutions for multibillion dollar quantitative hedge funds, venture-funded startups, and some of the largest telecoms in the world. Previously, he held technology leadership roles at Argyle Data, Alpine Data Labs, and Endeca/Oracle. He’s also the founder of Fellowship.AI, a machine learning fellowship program.

Presentations

Scaling your data science experiments from Jupyter notebooks to 6,000 GPUs Session

Data scientists and machine learning professionals face a quandary of choices when trying to figure out how to scale their data science experiments. Arshak Navruzyan details the landscape of available options and explains how to make best use of the free and open source tools available.

Marc Nehme is the chief architect in the Watson Embed organization at IBM, where his primary focus is helping strategic partners achieve significant business value by embedding and scaling Watson solutions across their organizations. Previously, Marc focused his efforts in the Watson Delivery organization, where he drove various customer engagements from concept to production.

Presentations

How to improve, enhance, and automate your business processes with Watson offerings for Salesforce and Box (sponsored by IBM Watson) Session

Marc Nehme demonstrates how you can quickly and easily use Watson with CRM solutions like Salesforce and cloud storage solutions like Box to improve and enhance your business processes.

Paul Nemitz is principal adviser of the European Commission on strategic justice issues. Previously, he was director for human rights and citizenship, leading reform of privacy law in Europe, and lead negotiator of the EU-US Privacy Shield Framework and of the code of conduct against hate speech and incitement to violence on the internet.

Presentations

Democracy, human rights, and the rule of law by design for artificial intelligence Session

The rise of AI has shown the importance of implementing the basic rules of democracy, human rights, and the rule of law into the innovation process and the programs of artificial intelligence by design and default. Paul Nemitz outlines justice-oriented AI development processes and shares a model for globally sustainable development and deployment of artificial intelligence in the future.

Jan Neumann leads Comcast’s Applied Artificial Intelligence Research Group, which combines large-scale machine learning, deep learning, NLP, and computer vision to develop novel algorithms and product concepts such as voice interfaces, virtual assistants, and video and IoT analytics that improve the experience of Comcast’s customers. Previously, Jan worked for Siemens Corporate Research on various computer vision-related projects, such as driver assistance systems and video surveillance. He has published over 20 papers in scientific conferences and journals and is a frequent speaker on machine learning and data science. He holds a PhD in computer science from the University of Maryland, College Park.

Presentations

How Comcast uses AI to reinvent the customer experience Session

Jan Neumann and Jeanine Heck explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers' questions or resolve their concerns.

Alan Nichol is cofounder and CTO of leading open source conversational AI company Rasa, where he helps create the software that enables developers to build conversational software that really works, and is a maintainer of Rasa NLU and Rasa Core, the leading open source libraries for building conversational AI. He is also the author of the DataCamp course Building chatbots in Python. He holds a PhD in machine learning from the University of Cambridge and has years of experience building AI products in industry.

Presentations

Building conversational AI in-house in the Fortune 500 Session

Fortune 500 companies are building conversational AI in-house to create a competitive edge. Alan Nichol shares a case study of a successful customer acquisition chatbot built by a large corporation and demonstrates how to build a useful, engaging conversational AI bot based entirely on machine learning using Rasa NLU and Rasa Core, the leading open source libraries for building conversational AI.

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

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms.

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

Fireside chat with Peter Norvig and Kavya Kopparapu Keynote

Fireside chat with Peter Norvig and Kavya Kopparapu

Maurice Nsabimana is a statistician focusing on national accounts and macroeconomic indicators in the World Bank’s Development Data Group. Previously, Maurice worked in the private sector and civil society and at a think tank. His research interests lie at the intersection of computational economics, machine learning, and public policy and in the development of new, practical methods and information technologies that can be directly applied to strengthen local capacity. He holds an MA in international affairs from the School of International and Public Affairs at Columbia University and a BSc in computer science from Vesalius College in Brussels, Belgium.

Presentations

Classifying images in Spark Session

Yulia Tell and Maurice Nsabimana walk you through getting started with BigDL and explain how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia and Maurice detail a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world.

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

Presentations

Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support 2-Day Training

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support (Day 2) Training Day 2

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

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

Presentations

PyTorch: A flexible approach for computer vision models Tutorial

Computer vision has led the artificial intelligence renaissance, and pushing it further forward is PyTorch, a flexible framework for training models. Mo Patel and Neejole Patel offer an overview of computer vision fundamentals and walk you through PyTorch code explanations for notable objection classification and object detection models.

David Patterson is a professor emeritus at UC Berkeley, a distinguished engineer in Google Brain, and vice chair of the board of the RISC-V Foundation. His most successful research projects are reduced instruction set computers (RISC), redundant arrays of inexpensive disks (RAID), and network of workstations, which together led to multibillion-dollar industries, seven books, and about 40 honors, including election to the National Academy of Engineering, the National Academy of Sciences, and the Silicon Valley Engineering Hall of Fame. He also shared the ACM Turing award, the IEEE von Neumann Medal, and NEC C&C prize with John Hennessy, past president of Stanford University and coauthor of two of his books. David holds an AB, MS, and PhD, all from UCLA.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

Joshua Patterson is the director of applied solutions engineering at NVIDIA. Previously, Josh worked with leading experts across the public and private sectors and academia to build a next-generation cyberdefense platform. He was also a White House Presidential Innovation Fellow. His current passions are graph analytics, machine learning, and GPU data acceleration. Josh also loves storytelling with data and creating interactive data visualizations. He holds a BA in economics from the University of North Carolina at Chapel Hill and an MA in economics from the University of South Carolina’s Moore School of Business.

Presentations

GPU-accelerating AI for cyberthreat detection Session

Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Aaron Sant-Miller explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools.

Emily Pavlini leads user experience at Diffeo. Emily has a long-standing interest in how people perceive and digest complex information. Previously, she cofounded Meta, a search engine for your personal files, which is now part of the Diffeo platform. Emily has won best pitch and demo awards.

Presentations

Collaborative machine intelligence: Accelerating human knowledge Session

Recent advances have made machines more autonomous, but much work remains for AI to collaborate with people. Emily Pavlini and Max Kleiman-Weiner share new insights inspired by the way humans accumulate knowledge and naturally work together that enable machines and people to work and learn as a team, discovering new knowledge in unstructured natural language content together.

Brian Pearce is senior vice president of enterprise artificial intelligence at Wells Fargo. During his time at Wells Fargo, he and his teams have led large, multichannel efforts such as mobile remote deposit, Apple Pay, P2P payments, transfers, bill pay, and the launch of an online brokerage platform, and he previously served as the head of the mobile banking function. Over his 25-year career in financial services, Brian has led a variety of business development, product management, project management, business analysis, and product operations functions. He has worked for industry leaders including First Data Corporation and Anderson Consulting as well as an internet startup. Brian lives in the East Bay with his wife and three sons. In his spare time, he’s a Scoutmaster, plays golf, and likes to ride his bikes (mountain and road).

Presentations

AI in personal finance: More than just chatbots Session

Chatbots are having a moment, and banks across the world are utilizing them for everything from basic customer service to assisting internal IT support. But chatbots only skim the AI landscape. Brian Pearce explains how AI helps Wells Fargo use data in a smarter way, from developing custom experiences to uncovering new insights—with customers and employees at the center of it all.

Nick Pentreath is a principal engineer in IBM’s Cognitive Open Technology Group, where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations. He has also worked at Goldman Sachs, Cognitive Match, and Mxit. He is a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.

Presentations

Recurrent neural networks for recommendations and personalization Session

In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.

Stephen Piron is employee zero at DeepLearni.ng, which he cofounded with a vision to build deep learning systems that produce powerful results for enterprises in diverse industry sectors. Stephen is a serial tech entrepreneur who has owned successful companies on both sides of the Atlantic, which have been featured in dozens of publications including Wired, Fortune, and the Wall Street Journal. Between startups, Stephen also designed computer-based trading algorithms at one of the world’s largest hedge funds. Stephen is a passionate public speaker and once presented at an event at 10 Downing Street before David Cameron. Stephen studied computer science at the University of Toronto and spent a summer studying artificial intelligence at Stanford in the early 2000s—a time when neural networks were hardly acknowledged.

Presentations

Evolving your enterprise with AI: How to create transformative business impact (sponsored by Deeplearni.ng) Session

Is your enterprise striving to build AI applications that produce transformative business value? Stephen Piron shares real-world examples of AI applications that are evolving the way enterprises work from the ground up as well as a framework for enterprise leaders to use to ensure their team’s AI initiatives lay the foundation for genuine business impact.

Jake Porway is the founder and executive director of DataKind, a nonprofit that harnesses the power of data science in the service of humanity. He is an alum of the New York Times R&D Lab and has worked at Google and Bell Labs. A recognized leader in the Data for Good Movement, he has spoken at IBM, Microsoft, Google, and the White House. Jake is also a PopTech Social Innovation fellow and a National Geographic Emerging Explorer. He holds a BS in computer science from Columbia University and an MS and PhD in statistics from UCLA.

Presentations

AI: A force for good Session

Jake Porway explores AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges you may face in using AI to tackle complex humanitarian issues.

Raghav Ramesh is a machine learning engineer at DoorDash working on its core logistics engine, where he focuses on core AI problems: vehicle routing, Dasher assignments, delivery time predictions, demand forecasting, and pricing. Previously, Raghav worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics. He holds an MS from Stanford University, where he focused on artificial intelligence and operations research.

Presentations

How DoorDash leverages AI in its world-class on-demand logistics engine Session

DoorDash is a last-mile delivery platform, and its logistics engine powers fulfillment of every delivery on its three-sided marketplace of consumers, Dashers, and merchants. Raghav Ramesh highlights AI techniques used by DoorDash to enhance efficiency and quality in its marketplace and provides a framework for how AI can augment core operations research problems like the vehicle routing problem.

Mike Ranzinger is a senior research engineer at Shutterstock, where he and a team of researchers and engineers have invented a number of AI search technologies and collaborated on multiple patent filings. Previously, Mike held a variety of software developer roles at New Century Software, Boulder Imaging, and AlchemyAPI (acquired by IBM Watson), where he spearheaded a natural scene optical character recognition (OCR) project that provided an API to extract text from images and was a member of the larger machine vision group that launched the industry’s first commercial image tagging and similarity API. Mike first became enamored with ray tracers and machine vision while studying at Colorado State University. Mike is passionate about cycling and spends most of his free time training for races as a new domestic pro. He holds a BS in computer science from Colorado State University.

Presentations

The quest for a new visual search beyond language Session

Mike Ranzinger shares his research on composition-aware search and explains how the research led to the launch of AI technology that allows Shutterstock’s users to more precisely find the image they need within the company's collection of more than 150 million images.

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

Presentations

Gamifying strategy: Enterprise AI use cases on agent-based simulation and learning Session

There are many enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially for new products or when entering new markets—there are very few successful use cases. Anand Rao presents four successful use cases on gamifying strategy and applying agent-based simulation in the auto, payments, medical devices, and airlines industries.

Delip Rao is the founder of R7 Speech Science, a San Francisco-based company focused on building innovative products on spoken conversations. Previously, Delip was the founder of Joostware, which specialized 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 from O’Reilly.

Presentations

Natural language processing with deep learning 2-Day Training

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

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

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

Daniel Raskin is the chief marketing officer at Kinetica, where he is responsible for leading all aspects of worldwide marketing. Daniel has approximately 20 years of experience building brands and driving product leadership. Previously, he was vice president of marketing and senior vice president of product management at digital identity management company ForgeRock, chief identity strategist at Sun Microsystems, and a senior executive at McGraw-Hill, NComputing, and Agari. Daniel holds a master’s degree in international management from Thunderbird School of Global Management and a master’s degree in publishing from Pace University.

Presentations

Don't get stung by extreme data (or honey bees) (sponsored by Kinetica) Session

Daniel Raskin and Jonathan Greenberg explain what the extreme data economy is about and how machine learning advances along with accelerated parallel computing will play a key role in translating data into instant insight to power business in motion.

Brian Ray is the cognitive team lead at Deloitte Consulting, where he heads the mission of solving complex analytical problems for major businesses worldwide though the power of data science. A big-picture strategist, team builder, and influential top technologist, Brian has extensive expertise in enabling products with cognitive data science— from engineering and architecture to hands-on integration and deployment of best-in-class solutions. Highly recognized in the industry, he was named one of Crain’s Chicago Business’s top “Tech 25” in 2011. He was an early presenter at Google, speaking on Python technology in 2006, and is a frequent speaker at major conferences, businesses, and universities across the country, including Georgia Tech, Emory, the GOTO conference, the INFORMS Business Analytics Conference, Culver Academies, and local technology user groups.

Presentations

People, process, and platforms deliver AI (sponsored by Deloitte Analytics) Session

Brian Ray unveils the secrets behind the execution of Deloitte's framework for AI summarized in "Artificial Intelligence for the Real World," recently published in the January–February 2018 issue of Harvard Business Review. Join in to learn how to go from data to delivering real and measurable predictive value.

Thomas Reardon is cofounder and CEO of CTRL-Labs.

Presentations

Neural interfaces: Connecting humans and artificial intelligence Keynote

Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs's transformative and noninvasive neural interface approach. Along the way, he discusses the near-term opportunities for practical applications that will soon revolutionize daily life and the industries they touch.

Neural interfaces: Connecting humans and artificial intelligence Session

Expanding his keynote, Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs's transformative and noninvasive neural interface approach. Along the way, he discusses the near-term opportunities for practical applications that will soon revolutionize daily life and the industries they touch.

Sid Reddy is chief scientist at Conversica. A recognized expert in natural language processing (NLP) and computational linguistics, Sid has designed, developed and contributed to dozens of NLP systems used in production in a wide array of use cases and industry verticals from healthcare, business intelligence, and life sciences to legal and ecommerce, including creating text-mining infrastructures from scratch at startups and at the Mayo Clinic and founding an NLP lab at Northwestern University. Most recently, Sid was a principal applied scientist at Microsoft. His research ranges from using functional theories of grammar for association extraction and question-answering to acquiring lexical resources through distributed word vector representations learned from big data and applying them to improve the state of the art in sequential labeling tasks. He is a patented inventor, sought-after industry speaker, and published author with research featured in over 50 peer-reviewed publications and technical conferences. Sid holds a bachelor’s degree in computer science from the Indian Institute of Technology and a PhD from Arizona State University.

Presentations

Conversational AI: What we’ve learned from millions of AI conversations with thousands of customers Session

Sid Reddy shares Conversica's artificial intelligence approach to creating, deploying, and continuously improving an automated sales assistant that engages in a genuinely human conversation at scale with every one of an organization’s sales leads.

Dima Rekesh is a senior distinguished engineer at Optum, a division of UnitedHealth Group, where he works on technology strategy with an emphasis on deep learning. Previously, Dima spent many years as a distinguished engineer at IBM, where he was involved in a wide variety of deep learning projects related to analytics in the cloud and at the edge.

Presentations

Imputing medical conditions based on a patient's medical history with deep learning Session

Julie Zhu and Dima Rekesh share a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras.

Ofer Ronen is the general manager of Chatbase, a conversational analytics service brought to you by Area 120 (a products incubator operated by Google). Previously, he was CEO of Pulse.io, an app performance monitoring service (acquired by Google), and CEO of ad network Sendori (acquired by IAC). Ofer is a startup mentor at Stanford and an angel investor in Lyft, Palantir, and Klout. He holds an MS in artificial intelligence from Michigan and an MBA from Cornell.

Presentations

Things nobody told you about building conversational UIs Session

Chatbots are expected to make machine communication feel human, but high-quality bot experiences are very hard to build. Ofer Ronen explores the challenges in optimizing chatbots and shares ways for developers to address them quickly and efficiently.

Kayvaun Rowshankish is a partner in McKinsey’s New York office and a leader in both Digital McKinsey and McKinsey’s Risk practice, where he serves all types of financial services firms, including universal banks, investment banks, securities firms, regional banks, asset managers, and financial-market infrastructure players on a broad range of digital risk, finance, data, technology, and operations-related issues. Kayvaun, along with his data transformation teams, has had tremendous impact helping financial services firms realize new revenue opportunities, achieve far-reaching regulatory requirements (including GDPR), and enhance efficiency and effectiveness, accomplished through large-scale transformations that establish strategies and drive execution of initiatives to both address regulatory mandates and also surpass competitors with advanced risk and data capabilities. Such capabilities include the automation of CCAR, operations risk, compliance (for example, BSA/AML, KYC), and credit processes as well as designing and implementing data governance, data use cases, and data architecture at a rapid pace with groundbreaking levels of adoption and impact. He convenes leading forums on risk data and technology and leads much of McKinsey’s preeminent knowledge efforts on these topics, including annual benchmarking that spans over 100 banks globally. Kayvaun speaks regularly at industry forums, publishes, and is quoted in leading journals on these topics.

Presentations

Executive Briefing: GDPR—Implications for AI Session

Kayvaun Rowshankish and Alexis Trittipo explore the extent to which firms have addressed the EU's General Data Protection Regulation (GDPR) (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.

Olga Russakovsky is an assistant professor in the Computer Science Department at Princeton University, where her research focuses on computer vision closely integrated with machine learning and human-computer interaction. In addition to her research, Olga cofounded the Stanford AI Laboratory’s outreach camp SAILORS to educate high school girls about AI and cofounded and serves as a board member of the AI4ALL foundation, dedicated to educating a diverse group of future AI leaders. She was awarded the PAMI Everingham Prize as one of the leaders of the ImageNet Large Scale Visual Recognition Challenge and the NSF Graduate Fellowship and was recognized by MIT Technology Review as one of its “35 under 35” innovators. She holds a PhD from Stanford University, after which she completes a postdoctoral fellowship at Carnegie Mellon University.

Presentations

AI4ALL: AI will change the world, but who will change AI? Keynote

Keynote with Olga Russakovsky

Fairness and bias in computer vision Session

Session with Olga Russakovsky

Carolina Sanchez Hernandez is a senior research analyst on the research and innovation team within Customer Solutions at NATS in the UK. Carolina has worked within research and innovation for the past 15 years in both private and public industry. Her background is in geography, environmental science, remote sensing, and machine learning.

Presentations

Revolutionizing aviation with AI Session

New technologies have the potential to revolutionize the aviation industry. Airports in particular are perfect candidates for AI and machine learning concepts. Carolina Sanchez Hernandez discusses how National Aviation Technical Services (NATS) is collaborating with several companies and institutes to change the way that data is captured and processed to transform airport operations.

Aaron Sant-Miller is a lead data scientist at Booz Allen Hamilton, where he specializes in applied mathematics, machine learning, and statistical modeling. He has architected, developed, and deployed data science solutions and machine learning suites across a wide range of domains, including tax fraud detection, climate science trend forecasting, cybersecurity risk scoring, and professional athlete performance prediction. Aaron’s current areas of research are focused on Bayesian modeling design, synthetic data generation, and optimized algorithm training design. He holds a BS and an MS in applied and computational mathematics and statistics from the University of Notre Dame.

Presentations

GPU-accelerating AI for cyberthreat detection Session

Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Aaron Sant-Miller explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools.

Richard Sargeant is the chief commercial officer at ASI. Richard has board-level experience helping senior leaders across a variety of sectors transform their businesses to use AI effectively. Previously, he was director of transformation at the Home Office, where he oversaw the creation of the second-most advanced in-house machine learning capability in the UK government, was one of the founding directors of the UK Government Digital Service, and worked at Google. He has also worked at the Prime Minister’s Strategy Unit and HM Treasury and is the non-executive board member at Exeter University with responsibility for digital, data, and technology.

Presentations

AI for managers 2-Day Training

Richard Sargeant and Myles Kirby offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

AI for managers (Day 2) Training Day 2

Richard Sargeant and Myles Kirby offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

Nabeel Sarwar is a machine learning engineer at Comcast NBCUniversal, where he operationalizes machine learning pipelines under the banner of improving customer experience, operations, field, and anything in between. He also oversees data ingest, feature engineering, and the generation and deployment of the AI models. Nabeel holds a BA in astrophysics from Princeton University.

Presentations

Machine learning meets DevOps: Paying down the high-interest credit card Session

Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments.

Kaz Sato is a staff developer advocate on the Cloud Platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He is a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and has hosted FPGA meetups since 2013.

Presentations

TensorFlow Lite: How to accelerate your Android and iOS app with AI Session

TensorFlow Lite—TensorFlow’s lightweight solution for Android, iOS, and embedded devices—enables on-device machine learning inference with low latency and a small binary size. Kazunori Sato walks you through using TensorFlow Lite, helping you overcome the challenges to bring the latest AI technology to production mobile apps and embedded systems.

Jorge Silva is a principal machine learning developer at SAS. Previously, he was an adjunct professor at Instituto Superior de Engenharia de Lisboa (ISEL) and a senior research scientist at Duke University. His research interests include statistical models applied to large-scale problems, such as manifold learning, computer vision, and recommender systems. He holds multiple US patents and has authored numerous scholarly papers. Jorge holds a PhD in electrical and computer engineering from Instituto Superior Técnico (IST), Lisbon.

Presentations

Online and active learning for recommender systems Session

Recommender systems suffer from concept drift and scarcity of informative ratings. Jorge Silva explains how SAS uses a Bayesian approach to tackle both problems by making the learning process online and active. Active learning prioritizes the most informative users and items by quantifying uncertainty in a principled, probabilistic framework.

Rachel Silver is senior product manager for data science at MapR Data Technologies, where she is responsible for driving ML and AI initiatives within the Product Management and Strategy Group. Rachel also manages the MapR Ecosystem Packs. She is passionate about open source technologies. Previously, Rachel was a solutions architect and applications engineer.

Presentations

Executive Briefing: A new taxonomy of machine learning Session

With all the buzz around machine learning, it can be difficult to distinguish what is disruptive from what is merely a marginal improvement. Rachel Silver shares a new taxonomy of machine learning approaches that categorizes both models and learning algorithms with respect to technical complexity and explains how to use it to identify approaches that provide compelling competitive advantage.

Kaarthik Sivashanmugam is a principal software engineer in the AI Infrastructure and Tools Group at Microsoft, where he is building a platform for scale-out deep learning to unlock the full potential of GPU cloud, data, and ML techniques in addressing complex AI challenges and enabling magical end-user experiences in various Microsoft services powered by AI. Previously, Kaarthik was the tech lead for the Mobius project and used it to implement Spark Streaming workloads for timely, high-fidelity processing of Bing logs at scale. Before joining Microsoft, Kaarthik was a senior software engineer at a semantic technology startup, where he built an ontology-based semantic metadata platform and used it to implement solutions for KYC/AML analytics.

Presentations

Distributed DNN training: Infrastructure, challenges, and lessons learned Session

Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure.

Ashok N. Srivastava is the senior vice president and chief data officer at Intuit, where he is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world—and in the process is hiring hundreds of people in machine learning, AI, and related areas at all levels. Ashok has extensive experience in research, development, and implementation of machine learning and optimization techniques on massive datasets and serves as an advisor in the area of big data analytics and strategic investments to companies including Trident Capital and MyBuys. Previously, Ashok was vice president of big data and artificial intelligence systems and the chief data scientist at Verizon, where his global team focused on building new revenue-generating products and services powered by big data and artificial intelligence; senior director at Blue Martini Software; and senior consultant at IBM. He is an adjunct professor in the Electrical Engineering Department at Stanford and is the editor-in-chief of the AIAA Journal of Aerospace Information Systems. Ashok is a fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA). He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, the IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a PhD in electrical engineering from the University of Colorado at Boulder.

Presentations

Using AI to solve complex economic problems Session

Entrusted with the financial data of 42 million customers, Intuit is in a unique position to take advantage of AI to solve some of its customers’ biggest financial pains. Ashok Srivastava discusses technology’s role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world.

Rupert Steffner is the founder of WUNDER, a cognitive AI startup that is helping consumers find the products they love. Rupert has over 25 years of experience in designing and implementing highly sophisticated technical and business solutions, with a focus on customer-centric marketing. Previously, Rupert was chief platform architect of Otto Group’s new business intelligence platform BRAIN and head of BI at Groupon EMEA and APAC. He also served as business intelligence leader for several European and US companies in the ecommerce, retail, finance, and telco industries. He holds an MBA from WU Vienna and was head of the Marketing Department at the University of Applied Sciences in Salzburg.

Presentations

The long and winding road to AI: Lessons from implementing cognitive AI Session

The road to real-world AI is long and winding. All we've heard from reputable experts turned out to be true, including the need for better data, a new UX, and new ways of learning. To help you along the way, Rupert Steffner highlights lessons learned implementing cognitive AI applications to help consumers find the products they love.

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

Presentations

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms.

Ambika Sukla heads Morgan Stanley’s AI and ML Center of Excellence, where he works on applying machine learning techniques to algorithmic trading, risk management, operations and compliance, and wealth and investment management and helps set the firm’s AI strategy. He has extensive experience in machine learning, including recommendation systems, classification and regression, clustering, anomaly detection, and optimal control. Ambika is a big proponent of unsupervised and semisupervised learning methods. His background is in signal processing and information theory. He holds a master’s degree in telecommunication engineering from NJIT.

Presentations

Automatic financial econometrics with AI Session

Financial econometric models are usually handcrafted using a combination of statistical methods, stochastic calculus, and dynamic programming techniques. Ambika Sukla explains how recent advancements in AI can help simplify financial model building by carefully replacing complex mathematics with a data-driven incremental learning approach.

John Sumser is the HR technology industry’s leading independent analyst. For the past 23 years, he’s been following and prodding the evolution of HR tech. Through his website, Hrexaminer.com, John documents and critiques the state of the industry. With liberal arts degree in hand, John joined the engineering world, learning coding and electronics design in the defense industry. He’s merged that technical underpinning with a fascination with the human dynamics in organizations to build an analyst’s practice. He’s recently published a comprehensive analysis of AI (and its subordinate technologies) in the HR sector.

Presentations

Executive Briefing: AI in human resources—Use cases and ethical issues Session

AI and its related subtechnologies are being introduced into operational decision making throughout the enterprise. The most promising and risky experiments involve the way people are selected and utilized, but the use of AI in HR raises the specter of software product liability. John Sumser offers an overview of the available use case solutions and the accompanying ethical issues.

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

Presentations

Scalable deep learning Session

While deep learning has enjoyed widespread empirical success, fundamental bottlenecks exist when attempting to develop deep learning applications at scale. Ameet Talwalkar shares research on addressing two core scalability bottlenecks: tuning the knobs of deep learning models (i.e., hyperparameter optimization) and training deep models in parallel environments.

Ophir Tanz is the CEO and founder of GumGum, the leading computer vision platform for marketers. Under his leadership, GumGum created the largest in-image advertising platform, revolutionizing the industry. Ophir was named one of Adweek’s “Young Influentials,” was featured on the cover of Entrepreneur magazine, and received the Siemer Summit Innovation in Advertising Award. Previously, Ophir was the CEO and cofounder of Mojungle.com, a mobile media-sharing platform (acquired by Shozu.com in 2007), and cofounder of Fluidesign, an award-winning interactive and branding agency. Ophir holds a BS and MS from Carnegie Mellon University. He currently lives in Los Angeles.

Presentations

Three examples of computer vision in action Session

Advancements in computer vision are creating new opportunities across business verticals, from programs that help the visually impaired to extracting business insights from socially shared pictures, but the benefits of applied AI in computer vision are only beginning to emerge. Ophir Tanz explores the tools and image technology utilizing AI that you can apply to your business today.

Omar Tawakol is the CEO of Voicera, a venture focused on AI that leverages data to help people become more productive. Previously, Omar was the founder and CEO of BlueKai (acquired by Oracle), which built the world’s largest consumer data marketplace and DMP, and SVP and GM of the Oracle Data Cloud (ODC), which powered 97 of the top 100 marketers as well as an ecosystem of AI applications. Omar holds an MS in CS from Stanford, where he researched and published work on AI agents, and a BS from MIT.

Presentations

AI building blocks: Speech technologies Session

Regardless of industry, every executive is concerned with the same thing: their customers. Omar Tawakol details the building blocks of speech technologies, including natural language processing, automatic speech recognition, and neural networks, that are necessary to implement voice-activated artificial intelligence and more importantly, enable a customer-centric enterprise.

Yulia Tell is a technical program manager on the big data technologies team within the Software and Services Group at Intel, where she is working on several open source projects and partner engagements in the big data domain. Yulia’s work is focused specifically on Apache Hadoop and Apache Spark, including big data analytics applications that use machine learning and deep learning. Yulia holds an MSc in computer science from Moscow Power Engineering Technical University and has completed executive training on market driving strategies at London Business School.

Presentations

Classifying images in Spark Session

Yulia Tell and Maurice Nsabimana walk you through getting started with BigDL and explain how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia and Maurice detail a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world.

Richard Tibbetts is CEO of Empirical Systems, an MIT spinout building an AI-based data platform that provides decision support to organizations that use structured data. Previously, he 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

Making business Bayesian: From uncertainty to action Session

New technologies make Bayesian inference and generative modeling more accessible to business analysts, but this also creates new communications challenges. Richard Tibbetts shares techniques for capturing domain knowledge and making findings actionable for decision makers utilizing the explanatory powers of transparent AI.

Wee Hyong is Head of AI Prototyping and Innovation and AI for Earth Engineering and Data science teams at Microsoft.

He leads a multi-disciplinary team of engineers and data scientists, working on cutting-edge AI capabilities that are infused into products and services. In all this work, they continuously push the boundaries of deep learning at cloud scale. His team works extensively with deep learning frameworks, ranging from TensorFlow, CNTK, Keras, and PyTorch.

Wee Hyong has worn many hats in his career – developer, program/product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique super powers to be a trusted AI advisor to many companies.

Wee Hyong co-authored several books on artificial intelligence – including the first book on “Predictive Analytics Using Azure Machine Learning”, and “Doing Data Science with SQL Server”.

Wee Hyong holds a Ph.D. in computer science from the National University of Singapore.

Presentations

Distributed DNN training: Infrastructure, challenges, and lessons learned Session

Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure.

Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters Session

Deep learning has fueled the emergence of many practical applications and experiences. Meanwhile, container technologies have been maturing, allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean as they walk you through using the Cognitive Toolkit (CNTK) with Kubernetes clusters.

Alexis Trittipo is an associate partner in McKinsey’s New York office, where she is a core member of both the Financial Institutions practice and the Risk practice. Alexis serves global and regional banks on topics related to risk and regulation, resolution planning, and operational risk and compliance as well as a wider set of industries on risk and compliance topics, including GDPR. Alexis holds an MBA from the University of Chicago Booth School of Business and a BA from Northwestern University.

Presentations

Executive Briefing: GDPR—Implications for AI Session

Kayvaun Rowshankish and Alexis Trittipo explore the extent to which firms have addressed the EU's General Data Protection Regulation (GDPR) (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.

Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses 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

Getting up and running with TensorFlow Tutorial

Amy Unruh walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you'll gain insight into deep learning and how it can be applied to complex problems in science and industry.

Len Usvyat is a vice president of integrated care analytics at Fresenius Medical Care’s North America Medical Office, where he is responsible for supporting analytical efforts for Fresenius’s integrated care assets such as its pharmacy, vascular care centers, urgent care facilities, hospitalist group, and the Fresenius health plan. These efforts vary and include activities such as routine and custom reporting, predictive modeling, outcomes analysis, and research. He also chairs FMCNA’s Predictive Analytics Steering Committee. Len has over 15 years of experience in data management, analytics, research, and epidemiology. Previously, he worked with Renal Research Institute, an FMCNA subsidiary, on a variety of research projects related to patient outcomes and quality reporting. Len has published over 40 manuscripts in peer reviewed journals. He holds a master’s degree from the University of Pennsylvania and a PhD from the University of Maastricht in the Netherlands.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions.

Manuela M. Veloso is the Herbert A. Simon University Professor in the School of Computer Science at Carnegie Mellon University, where she is the head of the Machine Learning Department. Manuela’s research, undertaken with her students, focuses on artificial intelligence, particularly for a variety of autonomous robots, including mobile service robots and soccer robots. She is a fellow of the ACM, IEEE, AAAS, and AAAI and the author of numerous publications.

Presentations

Autonomy and human-AI interaction Keynote

Autonomy—consisting of extensive data processing, decision making and execution, and learning from experience—creates the need for a new interaction between humans and AI. Manuela Veloso delves into the roles humans can have in such interactions, as well as the underlying challenges to AI, in particular in terms of collaboration and interpretability.

Benjamin Vigoda is the CEO of Gamalon Machine Intelligence. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor (acquired by Analog Devices), a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing whose products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. 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, Wired, and other media. 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. Ben has authored over 120 patents and academic publications. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.

Presentations

When machines have ideas Session

Ben Vigoda offers an overview of idea learning, a new approach to deep learning that has been funded since 2013 as one of DARPA's largest investments in next-generation machine learning. Ben details the process of teaching machines with ideas instead of labeled data and demonstrates use cases with state-of-the-art performance on applications in unstructured enterprise data.

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

Presentations

AI for the public sector: Benefitting citizens through cognitive solutions Session

Drawing on his experience bringing AI to the public sector, Sumeet Vij offers perspectives on public sector AI trends, dispelling myths around barriers to entry and sharing approaches and opportunities as he highlights examples of successful AI adoptions.

Ashwin Vijayakumar is lead developer evangelist and an embedded systems architect working on robotics, IoT, and automotive electronics at Intel. A results-oriented hands-on engineering leader, an entrepreneur, and an innovator with extensive experience in bringing embedded products to market, Ashwin is passionate about deploying products and sustaining them at every stage of product development lifecycle. He is currently focused on the front and rear end of the cycle (i.e., requirements gathering, analysis, prototyping, deployment, training and sales support, and maintenance and technical support).

Presentations

Accelerate deep neural networks at the edge with the Intel Movidius Neural Compute Stick Tutorial

Ashwin Vijayakumar gives you a hands-on overview of Intel's Movidius Neural Compute Stick, a miniature deep learning hardware development platform that you can use to prototype, tune, and validate your AI programs (specifically deep neural networks).

Meihong Wang is an engineering director at Facebook, where he leads the news feed team, which helps deliver the most relevant content to billions of users in their news feeds. Meihong and his team are building a large-scale machine learning system to improve news feed personalization.

Presentations

Serving billions of personalized news feeds with AI Keynote

Everyone's Facebook news feed experience is unique and highly personalized. Meihong Wang explains how Facebook solves the personalization problem with machine learning techniques and offers an overview of its large-scale machine learning system that models every user and delivers them the most relevant content in real time.

Serving billions of personalized news feeds with AI Session

Everyone's Facebook news feed experience is unique and highly personalized. In this extension of his keynote, Meihong Wang explains how Facebook solves the personalization problem with machine learning techniques and offers an overview of its large-scale machine learning system that models every user and delivers them the most relevant content in real time.

Christopher Watkins is a machine learning specialist at the Commonwealth Scientific and Industrial Research Orgainsation (CSIRO). He as been a technical assistant at the Creative Destruction Lab Quantum Machine Learning incubator program, a lecturer in parallel computing at Monash University, and a researcher at the inaugural Frontier Development Lab. Currently, he is working toward his PhD in computational quantum physics at Monash University.

Presentations

A reliable and robust classification pipeline for protein crystallization imaging Session

The achievement of human-level accuracy in image classification through the use of modern AI algorithms has renewed interest in its application to automated protein crystallization imaging. Christopher Watkins explores the development of the deep tech pipeline required for the robust operation of an online classification system in CSIRO's GPU cluster and shares lessons learned along the way.

Gu-Yeon Wei is the Robert and Suzanne Case Professor of Electrical Engineering and Computer Science at Harvard University. Gu’s research spans a broad range of areas from mixed-signal I/O circuits to power electronics to accelerator-centric SoC architectures and chip design. To name just one project, his group developed a “BrainSoC” to control Harvard’s RoboBees. Gu’s recent efforts have focused on developing energy-efficient neural network accelerators leveraging codesign opportunities from algorithms through architecture to circuit design. He holds BS, MS, and PhD degrees in electrical engineering from Stanford University.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

Scott Weller is cofounder and CTO of SessionM. Scott has over 18 years of development, operational, and leadership experience turning ideas into reality and leading technology teams through the challenges of early-stage growth. Previously, he was vice president of product and technology for Scientific Games (SGMS), where he oversaw the development and integration of interactive technologies into MDI’s products and services; vice president of product and technology at GameLogic (acquired by Scientific Games in 2010); cofounder and GM of SnapYap.com; principal software engineer at Terra/Lycos, where he spent several years innovating data and advertising platform technologies; and senior software engineer at Gamesville.com (acquired by Lycos in 1999). At the age of 16, he joined a team of eight motivated geeks to help build the country’s first internet service provider, later acquired by Conversant Communications. Scott holds a BS in computer science from the University of Rhode Island.

Presentations

The vital role of failure in machine learning Session

In video games, players learn by failing, sometimes “dying” hundreds of times before learning how to succeed. By enabling us to simulate scenarios and predict outcomes, AI has essentially made the world like a game. Scott Weller explores the role of failure in machine learning, explaining how to set realistic expectations and sharing examples of good and bad AI deployments in the wild.

Greg Werner is the founder and CEO of 3Blades. 3Blades develops and maintains IllumiDesk (https://illumidesk.com) and Cup of Data (https://cupofdata.com). Greg has built information technology businesses his entire career. Previously, he cofounded Certsuperior, currently one of the largest web security companies by sales in Latin America, and Reachcore, a leading business-to-business supplier of document exchange services for the oil and gas, insurance, telco, and financial verticals. Greg is a co-organizer of the PyData Meetup group in Atlanta. He frequently contributes to open source projects that help the scientific community, particularly those within the Python ecosystem. Greg holds a BA in economics from Emory University, an MBA in international management from Thunderbird, and a master’s degree in computer science from the University of Illinois.

Presentations

Deploy MXNet and TensorFlow deep learning models with AWS Lambda, Google Cloud Functions, and Azure Functions Tutorial

Greg Werner walks you through using MXNet and TensorFlow to train deep learning models and deploy them using the leading serverless compute services in the market: AWS Lambda, Google Cloud Functions, and Azure Functions. You'll also learn how to monitor and iterate upon trained models for continued success using standard development and operations tools.

Megan Yetman is a machine learning engineer at the Center for Machine Learning at Capital One. Megan has production experience with natural language processing and neural networks as well as data migration and data science. She holds a BA and MS in statistics from the University of Virginia.

Presentations

Using NLP, neural networks, and reporting metrics in production for continuous improvement in text classifications Session

Pensieve is a natural language processing (NLP) project that classifies reviews for their sentiment, reason for sentiment, high-level content, and low-level content. Megan Yetman offers an overview of Pensieve as well as ways to improve model reporting and the ability for continuous model learning and improvement.

Onur Yilmaz is a deep learning solution architect at NVIDIA, where he works on deep learning use cases for finance and helps researchers and data scientists adopt deep learning and GPU technology. Onur holds a PhD in computer engineering from New Jersey Institute of Technology; his dissertation focused on traditional machine learning and high performance signal processing for finance.

Presentations

NVIDIA Deep Learning Institute: Computer vision and finance training 2-Day Training

Onur Yilmaz walks you through the fundamentals of deep learning—training neural networks and using results to improve performance and capabilities. Once you’ve learned the basics, you'll apply deep learning to finance to make predictions and exploit arbitrage.

NVIDIA Deep Learning Institute: Computer vision and finance training (Day 2) Training Day 2

Onur Yilmaz walks you through the fundamentals of deep learning—training neural networks and using results to improve performance and capabilities. Once you’ve learned the basics, you'll apply deep learning to finance to make predictions and exploit arbitrage.

Cliff Young is a data scientist on the Google Brain team, where he works on codesign for deep learning accelerators. He is one of the designers of Google’s Tensor Processing Unit (TPU), which is used in production applications including Search, Maps, Photos, and Translate. TPUs also powered AlphaGo’s historic 4-1 victory over Go champion Lee Sedol. Previously, Cliff built special-purpose supercomputers for molecular dynamics at D. E. Shaw Research and worked at Bell Labs. A member of ACM and IEEE, he holds AB, MS, and PhD degrees in computer science from Harvard University.

Presentations

MLPerf: A benchmark suite for machine learning from an academic-industry cooperative Session

Join in to explore MLPerf, a common benchmark suite for training and inference for systems from workstations through large-scale servers. In addition to ML metrics like quality and accuracy, MLPerf evaluate metrics such as execution time, power, and cost to run the suite.

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

Presentations

Deep learning and AI is making clinical neuroimaging faster, safer, and smarter Session

What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making.

Julie Zhu is director of data science at Optum.

Presentations

Imputing medical conditions based on a patient's medical history with deep learning Session

Julie Zhu and Dima Rekesh share a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras.

Xiaoyong Zhu is a program manager at Microsoft, where he focuses on distributed machine learning and its applications.

Presentations

Scaling up deep learning-based superresolution models more efficiently using the cloud Session

Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. Xiaoyong Zhu shares the latest academic progress in superresolution using deep learning and explains how it can be applied in various industries, including healthcare. Along the way, Xiaoyong demonstrates how the training can be done in a distributed fashion in the cloud.

Scott Zoldi is chief analytics officer at FICO, where he is responsible for the analytic development of FICO’s product and technology solutions, including the FICO Falcon Fraud Manager, which protects about two-thirds of the world’s payment card transactions from fraud. While at FICO, Scott has authored 80 analytic patents (40 granted and 40 in process). He is actively involved in the development of new analytic products utilizing artificial intelligence and machine learning technologies, many of which leverage new streaming artificial intelligence innovations such as adaptive analytics, collaborative profiling, deep learning, and self-learning models, and has recently been focused on the applications of streaming self-learning analytics for real-time detection of cybersecurity attacks and money laundering. Scott serves on the boards of directors of Tech San Diego and the Cyber Center of Excellence. He holds a PhD in theoretical physics from Duke University.

Presentations

Innovations in explainable AI in the context of real-world business applications Session

Scott Zoldi discusses innovations in explainable AI, such as Reason Reporter, which explains the workings of neural network models used to detect fraudulent payment card transactions in real time, and offers a comparative study with local interpretable model-agnostic explanations (LIME) that demonstrates why the former are better at providing explanations.

Lindsey Anderson-Zuloaga is director of data science at HireVue. She is very interested in how AI can help humans make better decisions. Lindsey holds a PhD in experimental physics.

Presentations

Avoiding biased algorithms: Lessons from the hiring space Session

We're all familiar with the highly publicized stories of algorithms displaying overtly biased behavior toward certain groups, but what actually happens behind the scenes, and how can these situations be avoided? Lindsey Zuloaga shares experiences and lessons learned in the hiring space to help others prevent unfair modeling and explains how to establish best practices.