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

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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.

Michael Balint is a senior manager of applied solutions engineering at NVIDIA. Previously, Michael was a White House Presidential Innovation Fellow, where he brought his technical expertise to projects like Vice President Biden’s Cancer Moonshot program and Code.gov. Michael has had the good fortune of applying software engineering and data science to many interesting problems throughout his career, including tailoring genetic algorithms to optimize air traffic, harnessing NLP to summarize product reviews, and automating the detection of melanoma via machine learning. He is a graduate of Cornell and Johns Hopkins University.

Presentations

GPU-accelerating AI for cyber threat detection Session

Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Michael Balint 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.

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 and MS in computer science and is a doctoral candidate at the University of New Mexico. 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 & 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 is an analytics, technology and marketing executive with 20+ years experience driving profitable business growth through the strategic use of data and analytics. Having worked around the globe, she’s helped Fortune 500 companies across financial services, telecom, technology, health care, retail and manufacturing advance their business through the strategic use of data and insights.

Today, as Head of BMO’s Analytics Centre of Excellence, Lori oversees analytics for the retail bank including: revenue/risk/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, and cross-channel client experience analytics, and the monetization of journeys.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, and Len Usvyat—to answer these questions.

Ron Bodkin is technical director for applied artificial intelligence at Google, where he helps Global Fortune 500 enterprises unlock strategic value with AI, acts as executive sponsor for Google product and engineering teams to deliver value from AI solutions, and leads strategic initiatives working with customers and partners. Previously, Ron was vice president and general manager of artificial intelligence at Teradata; the founding CEO of Think Big Analytics (acquired by Teradata in 2014), which 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; vice president of engineering at Quantcast, where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making; founder of enterprise consulting firm New Aspects; and cofounder and CTO of B2B applications provider C-Bridge. Ron holds a BS in math and computer science with honors from McGill University and a master’s degree in computer science from MIT.

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.

Chris Butler is Director of AI at Philosophie, where he is responsible for strategy and execution of top client initiatives and relationships across field service operations, education, retail, fashion, and pharmaceuticals. Chris also develops workshop programs to elevate client success, including Strategy Kernel Canvas, Design Thinking for AI/ML. He has over 18 years of experience working in product management and business development for large to small companies (including founding one of his own) and has worked with Microsoft, Waze, Horizon Ventures, and KAYAK, among others. He was named the Best Product Person of 2016 by the Product Guy.

Presentations

Design thinking for AI Tutorial

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

Roger Chen is Co-Founder & CEO of Computable Labs, and he serves as Program Chair for the Artificial Intelligence Conference. Previously, he was a Principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology; before he worked in venture capital, he was an engineer at Oracle, EMC, and Vicor and developed novel nanotechnology as a Ph.D. researcher at UC Berkeley. Roger holds a BS from Boston University and a Ph.D. 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.

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 & MIT, co-author of 450 papers, 95 patent publications & the book Regenesis. He developed methods used for the first genome sequence (1994) & genome recoding & million-fold cost reductions since. He co-initiated the BRAIN Initiative (2011) & Genome Projects (1984, 2005) to provide & interpret world’s only open-access personal precision medicine data.

Presentations

Keynote by George Church Keynote

Keynote by George Church

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, for 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) 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.

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.

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

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.

Jana’s a math and computer nerd who took the business path for a career. Today, she’s CEO of Nara Logics, a neuroscience-inspired artificial intelligence company, providing a platform for recommendations and decision support. Her career has taken her from 3-person business beginnings 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.

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

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

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.

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 at 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

Keynote by Zoubin Ghahramani Keynote

Keynote by Zoubin Ghahramani

Session by Zoubin Ghahramani Session

Session by Zoubin Ghahramani

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? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, 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), and the 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.

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.

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. Previously, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. 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 and has been thinking about AI throughout his career. Upon graduating from Caltech with a degree in computation and neural systems, Mark went on to positions at Microsoft and numerous startups and academia, including turns at Numenta and the Yale neuroscience department.

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 Skymind, 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? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate 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 that combined further improve performance.

Jeanine Gubler Heck is executive director of product management in the Technology and Product organization of Comcast Cable, where she leads the company’s efforts to bring artificial intelligence into XFINITY products. Jeanine was the founding product manager for the X1 voice remote, which allows customers to discover TV content through intuitive voice commands. Launched in 2015, the remote is currently used in more than 10 million Comcast customers’ homes, with over 4 billion total voice commands made since launch. Jeanine also led the launch of a cloud-based TV search engine and the company’s first TV recommendations engine. In 2012, Jeanine was the founding leader of Comcast’s Women’s Network for its inaugural three years. She is a frequent speaker at conferences about voice recognition, artificial intelligence, and women in tech. Jeanine spends her time outside of work encouraging young people to pursue careers in technology. She is a mentor to the BambieBotz, a FIRST Robotics team at her high school alma mater, St. Hubert’s High School for Girls, and she sits on Comcast’s FIRST Robotics steering committee, which sponsors 40 teams nationwide. Additionally, Jeanine frequently speaks to high schools in the Philadelphia area about careers in computer science and product management. Jeanine holds a bachelor’s degree in computer science and engineering from the University of Pennsylvania and an MBA from Columbia University. She lives in the Philadelphia area with her husband and four 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.

Glenn Hofmann is the chief data scientist at New York Life and leads the fast-growing and highly innovative team at the Center for Data Science and Analytics (CDSA), which supports many areas of the company with data and advanced analytics, such as underwriting, actuarial, marketing, agency distribution, service, and product. Previously, he built and lead analytics teams in property and casualty insurance at Verisk, TransUnion, Allstate, Claritas, ID Analytics, and HSBC. He also served as a professor of statistics at the University of Concepcion in Chile. Glenn holds a PhD in statistics from Ohio State University and an MBA from the Wharton School at the University of Pennsylvania.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, and Len Usvyat—to answer these questions.

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.

Andrew Ilyas is an undergraduate engineering 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.

Steven J. Rennie is the director of research at Fusemachines, a 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.

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 a 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.

David Kale is a deep learning engineer at Skymind and a PhD candidate in computer science at the University of Southern California, where he is advised by Greg Ver Steeg of the USC Information Sciences Institute. His research uses machine learning to extract insights from digital data in high-impact domains, such as healthcare, and he collaborates with researchers from Stanford Center for Biomedical Informatics Research and the YerevaNN Research Lab. Recently, David pioneered the application of deep learning to modern electronic health records data. At Skymind, he works with clients and partners to develop and deploy deep learning solutions for real world problems. David co-organizes the Machine Learning for Healthcare Conference (MLHC) and has served as a judge in several XPRIZE competitions, including the upcoming IBM Watson AI XPRIZE. He is the recipient of the Alfred E. Mann Innovation in Engineering Fellowship.

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

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.

Murali Kaundinya is a director at Merck. A senior strategist with technology and architecture with extensive leadership and management consulting experience, Murali 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.

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.

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 junior 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

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 like insurance, finance or agriculture. 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, which we term 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 is in "the business of breakthroughs.” Practicing open innovation since being incorporated in 2002, PARC 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. Motivated by his passion for bringing innovations to market, Tolga specializes in leading cross-functional teams to apply science and technology to develop creative solutions to real-world problems. Since joining PARC in 2010, he has held various leadership roles focusing on R&D management, product strategy, and technology commercialization. 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 multi-million-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 vice president of product for data science at Lumiata. Shane has been shipping products in technology and AI for over 10 years, with experience spanning 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. 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 lives in New York, where he enjoys the opera, rock climbing, and attending geeky data science events. You can find out more at the Data Incubator’s webiste or @thedatainc.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, 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.

Program Chair keynote Keynote

Keynote by program chair Ben Lorica

Tuesday opening remarks Keynote

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

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 the 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.

Angie Ma is cofounder and COO of ASI Data Science, a London-based AI tech startup that offers data science as a service, which has completed more than 120 commercial data science projects in multiple industries and sectors and is regarded as the EMEA-based leader in data science. Angie is passionate about real-world applications of machine learning that generate business value for companies and organizations and has experience delivering complex projects from prototyping to implementation. A physicist by training, Angie was previously a researcher in nanotechnology working on developing optical detection for medical diagnostics.

Presentations

AI for managers 2-Day Training

Angie Ma offers 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.

Ben MacKenzie is director of AI engineering at Think Big Analytics, where he leads the team helping enterprise customers build and deploy deep learning models to drive business value. In addition to a solid hands-on experience and theoretical understanding of deep learning practices, Ben draws on years of experience building solutions using big data and public cloud technologies for a broad array of enterprise and startup customers.

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. Simon Moss 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.

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.

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

Presentations

Deep learning with TensorFlow 2-Day Training

TensorFlow is an increasingly popular tool for deep learning. Dana Mastropole 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.

Erika Menezes is a software engineer on the cloud AI platform team at Microsoft, where she creates innovative end-to-end concepts that help illustrate the power of Microsoft’s data and AI technologies. She is part of multiple efforts to champion diversity and inclusion in the tech industry. Erika holds an MS from Carnegie Mellon University, where she worked as a research assistant on several machine learning and NLP projects.

Presentations

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

Erika Menezes shares 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 discusses how to build multimodal emotion detection using various deep learning approaches. Along the way, Taniya explains how to mitigate the challenges of data collection and annotation and how to avoid bias in model training.

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.

Simon Moss is vice president of industry consulting and solutions for the Americas at Teradata. Simon has extensive leadership and entrepreneurial experience in banking and financial technology. Previously, he was a managing director at Grant Thornton responsible for FinTech in the Americas and was founder and chief executive officer for Mantas Corporation, leading the company to become a market leader in global anti-money laundering and compliance technology, as well as to its sale to Oracle for $127 million in cash. Simon’s other experience includes founding the IBM Risk Management Practice, working as a partner at PWC, and serving on multiple technology company boards of directors.

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. Simon Moss 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.

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.

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 that improve the experience of Comcast’s customers such as the voice interfaces, virtual assistants, and video and IoT analytics. 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.

Yamini Nimmagadda is an engineer at Intel. She holds a PhD in electrical and computer engineering from Purdue.

Presentations

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

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

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, 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 walks you through getting started with BigDL and explains how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia details 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, detailing the components of BigDL and explaining how to implement machine learning algorithms, with a focus on neural networks.

Jeetu Patel is chief product officer at Box, where he leads the company’s overall product and platform strategy and drives Box’s long-term roadmap and vision for cloud content management in the enterprise. Previously, as chief strategy officer and senior vice president of platform, Jeetu led the creation of the Box Platform business unit, overseeing product strategy, marketing, and developer relations. He grew the team from a nascent product to a revenue generating business line and key element on Box’s overall suite of offerings. He also led corporate development & M&A strategy as well as Box for Industries. Before joining Box, Jeetu was general manager and chief executive of EMC’s Syncplicity business unit, which he grew from $0 to $100M in 2.5 years, and president of Doculabs, a research and advisory firm co-owned by Forrester Research that is focused on collaboration and content management across a range of industries, including financial services, insurance, energy, manufacturing, and life sciences. Jeetu holds a BS in information decision sciences from the University of Illinois Chicago.

Presentations

AI and the future of work Session

AI will completely change and fundamentally power the way the world works together, so what does the future of AI in the enterprise look like? Jeetu Patel 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.

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, as well as 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.

Neejole Patel is a sophomore at Virginia Tech, where she is pursuing a BS in computer science with a focus on machine learning, data science, and artificial intelligence. In her free time, Neejole completes independent big data projects, including one that tests the Broken Windows theory using DC crime data. She recently completed an internship at a major home improvement retailer.

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.

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

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

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 cyber threat detection Session

Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Michael Balint 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.

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 search 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 a number of enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially in new product or market entry—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.

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 it touches.

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 it touches.

Dima is a Senior Distinguished Engineer with Optum Tech, a division of United Healthcare Group. He works on technology strategy with an emphasis on Deep Learning.
Previously, Dima spent many years with IBM, where he was Distinguished Engineer. He was involved in a wide variety of innovation and Deep Learning projects related to Analytics, Cloud and Edge.

Presentations

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

Julie Zhu shares 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 leads the Chatbase bot analytics team within Area 120, an incubator for early-stage products operated by Google. Previously, he served as 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

How to save time optimizing chatbots 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 is a Partner in McKinsey’s New York office and a leader in both Digital McKinsey and McKinsey’s Risk practice. 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. He has accomplished this 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. Kayvaun convenes leading forums on risk data and technology, for example, GDPR and two biannual roundtables on BCBS 239 including all GSIBs and most D-SIBs. He also leads much of our 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: Achieving sustainability with GDPR Session

The session will explore the extent to which firms have addressed the GDPR regulation (the deadline being imminent) and how they might build further sustainability into their capabilities, especially through use of AI and other innovative technologies.

Mike Ruberry is a senior associate of data science at ZestFinance, where his research interests include explainability and generative models. Mike has worked on several machine learning models and tools, including deploying automated models that process terabytes of data daily. Before specializing in machine learning, he worked on Windows as a program manager at Microsoft. Mike holds four degrees in computer science, including a PhD from Harvard University.

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. Mike Ruberry shares a framework for evaluating, explaining, and managing these more complex methods.

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

Keynote by Olga Russakovsky Keynote

Keynote by Olga Russakovsky

Session by Olga Russakovsky Session

Session by 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.

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, 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 for bringing 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 in 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 (wunder.ai), a cognitive AI startup that is helping consumers to 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 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 finding 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 different types of machine learning problems such as recommendation systems, classification/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 HRTech. 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.

Madhu Tadikonda is the chief underwriting officer for general insurance at AIG, where he is responsible for overseeing AIG’s global commercial product CUOs, defining global underwriting standards, developing robust pricing frameworks, and deploying innovative underwriting tools. Previously, he was a senior vice president on AIG’S science team; was a partner at Oliver Wyman and led the company’s Corporate Finance and Advisory practice in North America, focusing on strategy, product optimization, distribution tactics, and M&A for a number of high-growth financial services institutions; and spent 10 years in the venture capital industry, most recently as a general partner at Accretive LLC, where he led and managed investments in a range of technology-enabled companies that utilized sophisticated analytics in areas such as consumer finance, supply chain management, and price optimization. Madhu holds a BA from Princeton and an MBA from Stanford.

Presentations

Data, AI, and innovation in the enterprise Session

What are the latest initiatives and use cases around data and AI? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, and Len Usvyat—to answer these questions.

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 ways to put computer vision to work today 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.

Yulia Tell is a Technical Program Manager in Big Data Technologies team within Software and Services Group at Intel, where she is working on several open source projects and partner engagements in the big data domain. Her work is focused specifically on Apache Hadoop and Apache Spark, including big data analytics applications that use machine learning and deep learning. She has worked in several groups at Intel over the past 10 years, including work on Intel’s HPC software tools and services.

Yulia has received her MSc degree in Computer Science from Moscow Power Engineering Technical University. She has also completed executive education program on Market Driving Strategies at London Business School.

Presentations

Classifying images in Spark Session

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

Wee Hyong Tok is a principal data science manager at Microsoft, where he works with teams to cocreate new value and turn each of the challenges facing organizations into compelling data stories that can be concretely realized using proven enterprise architecture. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his range of experience has given him unique superpowers to nurture and grow high-performing innovation teams that enable organizations to embark on their data-driven digital transformations using artificial intelligence. He strongly believes in story-driven innovation and has a passion for leading artificial intelligence-driven innovations and working with teams to envision how these innovations can create new competitive advantage and value for their business. He coauthored one of the first books on Azure Machine Learning, Predictive Analytics Using Azure Machine Learning, and authored another demonstrating how database professionals can do AI with databases, Doing Data Science with SQL Server.

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) 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.

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

Yufeng Guo 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 apply 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? How are data and AI reshaping industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of four experts in different industries—Madhu Tadikonda, Glenn Hofmann, Saar Golde, 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

Keynote by Manuela Veloso Keynote

Keynote by Manuela Veloso

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).

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, 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.

Meihong Wang joined Facebook in 2012 and is currently an Engineering Director on News Feed team, where he leads the team to deliver most relevant content to billions of users in theirs news feeds. Meihong has been building the large scale machine learning system with the team to improve news feed personalization in the last few years.

Presentations

Keynote by Meihong Wang Keynote

Keynote by Meihong Wang

Session by Meihong Wang Session

Session by Meihong Wang

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.

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 open source integration data science platform 3Blades.io. Greg has built information technology businesses his entire career. Previously, he cofounded Certsuperior, currently one of the largest web security companies by sales in LATAM, 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.

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 Tech.

Presentations

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

Julie Zhu shares 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 super-resolution models more efficiently using the cloud Session

Super-resolution 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 super-resolution 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 scientist 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 work to establish best practices.

Liran Zvibel is cofounder and CTO at WekaIO, where he guides the company’s long-range technical strategies. Previously, Liran ran engineering at social startup and Fortune 100 organizations including Fusic, where he managed product definition, design, and development for a portfolio of rich social media applications and was responsible for the principal architecture of the hardware platform, clustering infrastructure, and overall systems integration for XIV Storage System (acquired by IBM in 2007). He holds a BSc in mathematics and computer science from Tel Aviv University.

Presentations

Making machine learning compute bound again (sponsored by WekaIO) Session

Modern analytics platforms need to process large datasets to deliver the highest levels of accuracy to the training and analytics systems. Liran Zvibel explains how WekaIO’s parallel and distributed Matrix filesystem can easily saturate a GPU node and how the integrated cloud tiering scales to exabyte of capacity in a single namespace.