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 is an entrepreneur who is passionate about building radical products. She co-founded cloud configuration management startup qwikLABS. qwikLABS was acquired by Google and still remains the exclusive platform used by AWS customers and partners worldwide to create and deploy on-demand lab environments on the cloud. Nidhi most recently led Product, Strategy, Marketing and Finance at data integration company Tamr. Before founding qwikLABS, Nidhi worked at McKinsey & Company, working on Big Data and Cloud Strategy. Nidhi holds a Ph.D. in Computer Science from the University of Wisconsin- Madison and holds 6 US patents. Follow Nidhi on the Medium publication, Radical Product and on Twitter @aggarwalnidhi.

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. In this talk, we introduce a practical framework for building customer-centered AI products. You will learn how to craft and communicate a far-reaching vision and strategy centered around customer needs, and balance that vision with the day-to-day needs of your company.

Alasdair Allan is a scientist and researcher who has authored over eighty peer reviewed papers, eight books, and has been involved with several standards bodies. Originally an astrophysicist he 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 U.S. 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. We walkthrough how to setup and build the kits, and how to use the kits Python SDK to use machine learning both in the cloud, and locally on the Raspberry Pi.

Robbie is the CEO of InfiniaML. Check out his blog for more: http://unsupervisedmethods.com

Presentations

Best Practices for Machine Learning in the Enterprise Session

Learn from an entrepreneur behind two successful AI companies who implemented machine learning and NLP solutions in over a hundred organizations. Find out the factors common to successful machine learning implementations and which factors predict failure. Finally, you will learn 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

We’ve developed 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 a deep learning anomaly detection platform Session

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

Ian Beaver is the Lead Research Engineer at Next IT, the provider of conversational A.I. systems for enterprise businesses. Ian has been publishing and presenting discoveries in the field of AI since 2005 on topics surrounding Human-Computer interactions such as gesture recognition, user preference learning, and detecting and preventing miscommunication with multi-modal automated assistants. Ian holds a BS and MS in computer science and will complete his doctorate at the University of New Mexico in 2017. 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

As conversation emerges as the next great human-machine interface, we discuss the challenges faced by the AI industry to relate to humans in the way they relate to each other. Highlighting findings from a recent study we demonstrate relational strategies used by humans in conversation and how Virtual Assistants must evolve to communicate effectively.

Chris Benson is Chief Scientist, Artificial Intelligence & Machine Learning for one of the four global strategic business groups at Honeywell – Safety & Productivity Solutions – where he is responsible for all AI initiatives across all product lines.

He is an AI strategist, solution architect, and public speaker / evangelist, who specializes in deep learning – the machine learning discipline that is driving the artificial intelligence revolution.

Chris frequently speaks about various AI topics at conferences, and is the Founder & 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 AI that works today - the driving force behind the current AI revolution. Deep learning will impact every industry on the planet, and there will be countless opportunities to take advantage of it. Success requires an AI strategy. We will address strategy for delivering deep learning into production, and explore 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 analytic techniques 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. This talk will examine the confluence of two development revolutions: (1) Almost every exciting new application today is intelligent, and (2) developers are increasingly deploying their work on container application platforms. Learn how these two revolutions benefit one another!

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

AI innovation in Deep Learning has moved from labs to large-scale deployments at Google. Hear techniques and lessons from Google, Spotify, Netflix, Evernote and eBay. Learn how to apply AI for personalized recommendations, intelligent bot support and enhancing the digital experience. Hear how to overcome common pitfalls in dealing with data, automation, experimentation.

Chris Butler is senior product strategist at Philosophie in NYC. He has over 17 years of experience working in product management and business development for large to small companies (including founding one of his own). He has worked with Microsoft, Waze, Horizon Ventures, KAYAK, and more. He was recently honored with The Best Product Person of 2016. At Philosophie he is responsible for strategy and execution of top client initiatives and relationships across field service operations, education, retail, fashion, and pharmaceuticals. He has developed workshop programs to elevate client success, including Strategy Kernel Canvas, Design Thinking for AI.

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. Stealing from the principals of Design Thinking we have created exercises that lead to more impactful solutions and better team alignment. Attendees get first-hand experience running them during the session so they can take them back to their teams later.

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 Deep Learning Land Session

Just predicting the target label for computer vision machine learning problem is not enough. It is also important to understand the “why”, “what” and “how” about the categorization process. In this talk we explore different ways to faithfully interpret and evaluate Deep Neural Network models - CNN Image models to understand the impact of salient features in driving categorization.

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. This talk shows how one can instead exploit large amounts of surrogate data to learn advanced word representations that are custom-tailored for sentiment, and presents a special deep neural architecture to use them.

Danielle Dean is a senior data scientist lead at Microsoft in the Algorithms and Data Science group within Cloud and Enterprise, where she leads a team of data scientists and engineers on end-to-end analytics projects using Microsoft’s Cortana Intelligence Suite—from automating the ingestion of data to analysis and implementation of algorithms, creating web services of these implementations, and using those to integrate into customer solutions or build end-user dashboards and visualizations. 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. Containers are allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean, as they share with you how to use the Cognitive Toolkit (CNTK) with Kubernetes clusters.

Radhika is a product executive who has participated in 4 exits, two of which were companies she founded. She is currently writing a book, Radical Product, about vision-driven product management. The first startup Radhika co-founded was Lobby7, a venture-backed company that created an early version of Siri back in 2000 and was acquired by Scansoft/Nuance. She later worked at Avid, growing their broadcast business by building a product suite to address pain points of broadcasters worldwide as they were moving from tape to digital media. She then led strategy at the telecom startup, Starent Networks, later acquired by Cisco for $2.9B. She left Cisco to start Likelii to offer consumers “Pandora for wine”. Likelii was later acquired by Drync. Recently she led Product Management at Allant to build a SaaS product for TV advertising. Allant’s TV division was subsequently acquired by Acxiom. Too long ago to admit, Radhika graduated from MIT with an SB and M.Eng in Electrical Engineering, and speaks 9 languages. You can follow her on the Medium publication, Radical Product and on Twitter @radhikadutt.

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. In this talk, we introduce a practical framework for building customer-centered AI products. You will learn how to craft and communicate a far-reaching vision and strategy centered around customer needs, and balance that vision with the day-to-day needs of your company.

As Chief Executive Officer of Darktrace, Nicole Eagan has positioned the company as an international leader in cyber defense. Nicole was named ‘Woman of the Year’ at the 2016 Cyber Security Awards and “AI Leader of the Year” at the 2017 Tech Leader’s Awards for successfully introducing disruptive machine learning technology to the global market. 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 helped Darktrace secure $75 million in Series D funding from Insight Venture Partners, KKR, and Summit Partners and led the company to $230 million in contract value. Under her leadership, Darktrace’’s innovative approach to cyber security has won over 80 awards, including World Economic Forum Technology Pioneer. The company is headquartered in San Francisco, CA and Cambridge, England and now has more than 600 employees working across 24 countries.

Presentations

Lessons Learned Through 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 them, and easily accessible for those with limited AI expertise. With over 4 years’ experience and 4,000 deployments, Darktrace has unique insights into how to develop and deploy both practical and successful AI applications.

Jana Eggers is a tech exec focused on products and the messages surrounding them. Jana has started and grown companies and led large organizations within enterprises. She supports, subscribes to, and contributes to customer-inspired innovation, systems thinking, Lean approaches, and autonomy, mastery, and purpose-style leadership. Jana’s experience includes positions at Intuit, Lycos, American Airlines, Blackbaud, Los Alamos National Laboratory and startups that you’ve never heard of. Jana is a frequent speaker, writer, and CxO educator on AI, innovation, and technology leadership. She’s proudly a mathematician.

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. It also gets points for requiring you to get your data house in order. I think AI's hat trick is how it can transform your company into a learning organization. I'll review the benefits of a learning org, as well as the key aspects, then show you 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

We’ve developed 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

This session includes cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots,

Cynthia Freeman is a research and software engineer at Next IT corporation, a developer of conversational AI systems. She is a computer science graduate student at the University of New Mexico and previously obtained her MS in Applied Mathematics at 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

As conversation emerges as the next great human-machine interface, we discuss the challenges faced by the AI industry to relate to humans in the way they relate to each other. Highlighting findings from a recent study we demonstrate relational strategies used by humans in conversation and how Virtual Assistants must evolve to communicate effectively.

Zoubin Ghahramani is a Professor at the University of Cambridge leading the Machine Learning group, and the Chief Scientist at Uber. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence, was a founding Cambridge Director of the Alan Turing Institute. His research focuses on probabilistic approaches to machine learning and AI. In 2015 he was elected a Fellow of the Royal Society.

Presentations

Keynote by Zoubin Ghahramani Keynote

Keynote by Zoubin Ghahramani

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 in Electrical Engineering at Stanford and founder/researcher at Subtle Medical.

His research focus is on applying machine learning, deep learning and optimization for medical imaging reconstruction and processing. Specifically, he is working on fast Magnetic Resonance Imaging (MRI) algorithms, multi-contrast neuroimaging applications (MRI, PET/MR). He is advised by Professor John Pauly in Electrical Engineering and Professor Greg Zaharchuk in Radiology at Stanford.

Recently he is working to bridge deep learning methods with MRI reconstruction, such as enhancing image quality with Deep Learning and multi-contrast information, solving quantitative imaging (water-fat separation, QSM, parameter mapping) using Deep Learning framework as well as using Generative Adversarial Network (GAN) for Compressed Sensing MRI. At Subtle Medical, he is pushing the performance of deep learning methods to boost the efficiency and value for medical imaging.

Presentations

Deep Learning and AI is changing clinical neuroimaging: faster, safer and smarter Session

What is the impact of AI and deep learning to clinical workflow? Going beyond simple classification tasks, in this talk, we will introduce AI/deep learning technologies invented at Stanford and applied in clinical neuroimaging workflow at Stanford Hospital, providing faster, safer, cheaper and smarter medical imaging and treatment decision making.

Funda researches and implements new data mining and machine learning approaches for SAS. 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 has 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. In this presentation, I will show how combining well-proven methods from classical statistics can enhance modern deep learning methods in terms of both predictive performance and interpretability.

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

Presentations

Getting up and running with TensorFlow Tutorial

In this session, you will learn how to train a machine-learning system using TensorFlow, a popular open source ML library. Starting from conceptual overviews, we will build all the way up to complex classifiers. You’ll gain insight into deep learning and how it can apply to complex problems in science and industry.

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

This workshop provides a practical framework for understanding their role in problem-solving and decision-making, focusing on how they can be used, the requirements for doing so and the expectations for their effectiveness.

Mark Hammond is co-founder 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. 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. He 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 and served as an adjunct professor for both Loyola University and University of Maryland. He holds a BS in Electrical Engineering, an MBA, and a PhD in Information Systems. (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. Deep learning methods can track dynamic, complex operations to create a normal operating envelop for dynamic, data-rich environments. Self-organizing maps dynamically group activities while recurrent neural networks predict their likelihood for residency in an identified group.

Jeanine Gubler Heck is Executive Director of Product Management in the Technology and Product organization of Comcast Cable. In this role, Jeanine is leading the company’s efforts to bring artificial intelligence into XFINITY products. She was the founding product manager for the X1 voice remote, launched in 2015 and currently used in more than 10 million Comcast customers’ homes with over 4 billion total voice commands since launch. The voice remote allows customers to discover TV content through intuitive voice commands.

Since joining Comcast in 2007, Jeanine has used her entrepreneurial approach to product management to build algorithms that enhance user experiences. In addition to managing the creation of the voice remote, Jeanine has led the launch of a cloud-based TV search engine as well as 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 also is a frequently featured 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 received her bachelor’s degree in Computer Science & Engineering from the University of Pennsylvania and an MBA from Columbia University. She lives in the Philadelphia area with her husband and 4 children.

Presentations

How Comcast uses AI to Reinvent the Customer Experience Session

We will describe how we use deep learning to build virtual assistants that allow our customers to contact us with questions or concerns, and how we use contextual information about our customers and systems in a reinforcement learning framework to identify the best actions that answer our customer's questions or resolve their concerns.

Kathryn Hume is Vice President Product & 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. As the former Director of Sales and Marketing at Fast Forward Labs (Cloudera), Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was 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

While AI research is making breathtaking advances, large enterprises still struggle to apply deep learning & other machine learning technologies successfully because they lack the mindset, processes or culture for an AI-first world. AI requires a radical shift. This talk surveys common failure models that hinder enterprise success & provides a framework for building an AI-First Enterprise Culture.

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

Presentations

Fooling Neural Networks in the Physical World Session

We’ve developed an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.

Mustafa is an Operations Research expert who is working at the interface of machine learning and optimization. He works as a Data Scientist in Analytic Server Division of SAS R&D and 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 CRM and IoT space. During his PhD he worked on game theory models of supply chains selling to strategic customers. Earlier in his career at SAS he developed distributed large scale integer optimization algorithms for Marketing Optimization problems. As an optimization enthusiast he always looks into ways to improve the algorithms. Nowadays his favorites are the Distributed Stochastic Gradient and Online Learning methods.

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. 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? Can you learn small patterns from the data that generate the big picture? This session will provide 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.

I am currently leading the NLP & Data Science practice at Episource, a US healthcare company. My daily work revolves around working on semantic technologies and computational linguistics (NLP), building algorithms and machine learning models, researching data science journals and architecting secure product backends in the cloud.

Techstack that my team and I typically work on includes;

Language: Python
Testing Frameworks: unittest, pytest
Automation & Configuration Management: Ansible, Docker, Vagrant
CI: Travis CI
Cloud Services: AWS, Google Cloud, MS Azure
APIs: Bottle, CherryPy, Flask
Databases: MySQL, SQLite, MSSQL, RDF stores, Neo4J, ElasticSearch, MongoDB, Redis
Editor: Sublime, Pycharm

I have architected multiple commercial NLP solutions in the area of healthcare, foods & beverages, finance and retail. I am deeply involved in functionally architecting large scale business process automation & deep insights from structured & unstructured data using Natural Language Processing & Machine Learning. I have contributed to multiple NLP libraries like Gensim and Conceptnet5. I blog regularly on NLP on multiple forums like Data Science Central, LinkedIn and my blog Unlock Text.

I love teaching and mentoring students. I speak regularly on NLP and text analytics at conferences and meetups like Pycon India and PyData. I have also taught multiple hands-on session at IIM Lucknow and MDI Gurgaon. I have mentored students from schools like ISB Hyderabad, BITS Pilani, Madras School of Economics. When bored – I like to fall back on Asimov to lead me into an alternate reality.

Presentations

Building a Healthcare Decision Support System for ICD10/HCC Coding through Deep Learning Session

At Episource, we work on building Deep Learning frameworks and architectures to help summarize a medical chart, extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. This not only required building a wide variety of deep learning algorithms to account for natural language variations but also fairly complex in-house training data creation exercises

Murali Kaundinya is a senior strategist with technology and architecture with extensive leadership and management consulting experience. He has served in a leadership role conceiving, executing and delivering transformational programs with Fortune 100 enterprises including financial services, health and life sciences, insurance and advanced technology. 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. He has held leadership roles at Sun Microsystems, Inc. (now under Oracle) where he provided strategy consulting services to CXOs of Sun’s top clients across the world. Murali started his career at NASA/Goddard Space Flight Center, Greenbelt, Maryland and has published and presented extensively on many technology topics. He holds several patents in RFID and in the field of Telemedicine.

Presentations

An Open Extensible AI Platform implementing 4 Use Cases for the Enterprise Session

An innersource model to curate and operationalize Machine Learning and Deep Learning algorithms with a common workflow and engaging user experience. With a focus on patterns and practices, this talk shares experiences realizing 4 enterprise scale use cases namely optical character recognition, release engineering, virtual customer assistants and data unification,

Geordie is a digital product design leader who has designed 15 SaaS products across verticals including healthcare, IT, education, and finance. After receiving his BA from Yale in Political Science, he did his obligatory tour of duty in management consulting before joining Boston-area UI/UX studio Fresh Tilled Soil in 2012. He is now a partner at Heroic, a design leadership coaching firm that helps growing companies scale their digital product capabilities. Follow Geordie on the Medium publication, Radical Product and on Twitter @didgeoridoo.

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. In this talk, we introduce a practical framework for building customer-centered AI products. You will learn how to craft and communicate a far-reaching vision and strategy centered around customer needs, and balance that vision with the day-to-day needs of your company.

Speaker Bio

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.

She’s the founder of Seattle PyLadies and is the co-organizer of 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.

Speaker Experience:
I’ve had the honor of speaking at three PyData’s:
Seattle PyData 2015
Seattle PyData 2017
DC PyData 2016
And will be speaking at the upcoming PyCascades.

She has also spoken at 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

This talk will cover the basics of facial recognition and the importance of having diverse datasets when building out a model. We’ll explore racial bias in datasets using real world examples and cover a use case for developing an OpenFace model for a celebrity look-a-like app.

Emma Kinnucan is a Lead Associate responsible in Booz Allen’s Strategic Innovation Group, where she is responsible for developing the firm’s Machine Intelligence (MI) strategy. In this role, she works with public, private, and social sector leaders to understand the opportunities and challenges in using machine learning, high performance computing, and other machine intelligence (MI) technologies to improve business and society. In previous roles, Emma was a counter-terrorism analyst at the Department of Defense, a strategic cyber security researcher for Fortune 500 executives, and a developer new products and business strategy for a leading private sector firm. She has been published in The Atlantic and Information Weekly. Emma graduated summa cum laude from Northeastern University with a B.S. in criminal justice, political science, and international affairs. She lives in Washington, D.C.

Presentations

Executive Briefing: Beyond Killer Robots - A Framework for the Ethical Implementation of AI Session

Executives responsible for leading AI are dedicating too much effort dispelling myths about implications of the technology (e.g., “killer robots” and mass unemployment). More realistic ethical issues concerning AI’s impact on human privacy, equity, dignity, and justice are ignored.We’ll provide explicit, real-world examples that show ignoring AI’s immediate ethical implications is a business risk.

David Kiron is the executive editor of MIT Sloan Management Review where he directs “The Big Ideas Initiative”, a content platform examining macro-trends that are transforming the practice of management.

Dr. Kiron 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. 

Dr. Kiron currently serves as an expert panelist on the Fraunhofer Institute’s Future of Operating Procedures project. He has a Ph.D. in philosophy from the University of Rochester and a B.A. from Oberlin College.

Presentations

Executive Briefing: The Adoption of Artificial Intelligence in Business - Why Leaders Forge Ahead and Laggards Fall Behind Session

While expectations for AI are sky-high across industry and geography, few organizations have mastered integrating the technology into their business processes and offerings — and many who want to don’t fully understand the work that lies ahead.

Max Kleiman-Weiner is a cofounder and chief scientist of Diffeo and a PhD student in computational cognitive science at MIT funded by the NSF and 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, earned an MSc in Statistics as a Marshall Scholar in Oxford, and did his undergraduate work at Stanford as a Goldwater Scholar.

Presentations

Collaborative machine intelligence: Accelerating human knowledge Session

Recent advances have made machines more autonomous. However, much remains to be done in order for AI collaborate with people. Drawing inspiration from the way humans accumulate knowledge and naturally work together, we will share new insights 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 co-founder and CEO of Cape Analytics and former Principal at Khosla Ventures. Cape Analytics is 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. The company is a leader in building valuable AI solutions and counts some of the nation’s largest insurers as customers. 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

Artificial Intelligence is transforming traditional industries from property insurance to agriculture – here’s how. Session

There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries like insurance, finance or agriculture. This session dives into the lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture.

Tim Kraska is an Associate Professor 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 spend the majority of 2017 at Google Research, where he invented the concept of learned index structures with the MLX and Brain team. 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

I will present how fundamental data structures can be enhanced using machine learning with wide reaching implications even beyond indexes. To quote Steven Sinofsky, board partner at A16Z and former president at Microsoft: "This paper [about learned indexes] blew my mind....ML meets 1960's data structures and crushes them."

Business Development of System Simulation Products for IoT, Autonomous Car, Cloud and Memory sub-systems. Joined Intel in 2008.

Presentations

End to end video analytics solution to surveillance and secure high value assets Session

Using AI and Computer vision for security surveillance in the energy industry

Dr. 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.

Dr. Kurtoglu 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, Dr. Kurtoglu 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 by 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 Ames Research Center, and a mechanical design engineer at Dell Corporation.

Dr. Kurtoglu’s own research was focused 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 received his Ph.D. from the University of Texas at Austin and M.S. from Carnegie Mellon University, both in Mechanical Engineering. He holds a bachelor’s degree in the same field from Orta Dogu Technical University (ODTU).

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.

Presentations

Executive Briefing: Making Reliable and Trustworthy AI Systems a Reality Session

Tolga will walk through the advanced technology that is needed to bring cyberphysical systems to reality today: the right hardware to sense the right data, explainable AI, designing security for trustworthy operations. He will walk the audience through a few case studies and advance tech deployments to illustrate his points.

Dr. 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.

Before joining Uber, Danny was General Manager of Amazon Machine Learning providing internal teams with access to machine intelligence. He also launched an AWS product that offers machine learning as a cloud service to the public. Prior to Amazon, he was Principal Development Manager at Microsoft where he led a product team focused on large-scale machine learning for big data. Danny spent eight years on speech recognition systems, first as CTO of General Magic, Inc., and then as founder of his own company, Vocomo Software. During this time he was working 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 holds MS and PhD degrees in computer science from the Technical University of Denmark. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit.

Presentations

Democratizing Deep Reinforcement Learning Session

The speaker will address how the crossroads between machine learning and gaming, offering innovations that are applicable in other fields of technology such as robotics, automotive, and any industry where developers must solve difficult problems.

I’ve been shipping products in technology and AI for over 10 years, with experience spanning large companies (Netflix, Microsoft) to early stage startups (Lumiata, Gliimpse, OpenTalent), to companies making the transition from smaller company to large organization (Powerset, Shutterstock). My 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 $1MM per year on a nearly 10X cost reduction; and the massive distributed document summarization engine that generates all the text you see on Bing.

I hold an a M.S. in Computational and Mathematical Engineering from Stanford University, and a 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 more challenging than building the AI models themselves; and getting it wrong is very expensive. In this talk we cover common pitfalls in defining AI products, and organizing teams to solve them; and talk through emerging best practices.

Andre Luckow is a project manager and researcher at the BMW IT Research Center in Greenville, South Carolina, USA. 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. A particular interest of Andre are deep learning applications and system-level challenges related to deep learning, streaming and edge computing. Previously, Andre held several functions at BMW Group IT in Munich, Germany. He possesses a PhD in the field of distributed computing from the University of Potsdam, Germany.

Presentations

AI Applications, Experiences and Best Practices in the Automotive Domain Session

AI delivers value to many facets of the automotive value chain, e.g. in the domain of smart manufacturing, supply chain management and customer engagement. Learn about how to assess AI technologies, validate use cases and foster the fast adoption. This talk will cover our experiences and best practices from developing computer vision and natural language understanding applications.

Zhenxiao Luo is an Engineering Manager at Uber, running 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. The talk is about how Uber tackles data caching in large scale machine learning. We will talk about Uber machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speedup AI jobs

David has been a pioneer and early adopter in the Web (US 5838906), Cloud (9195507), e-Commerce (6338066, 6278966), data sciences (6230153, 6100901) and is currently investigating the full-stack and full-life-cycle of cognitive agents using the scenario of eldercare assistance (ibm.biz/ageathome).

Presentations

Cognitive IoT and Eldercare Session

An investigation of cognitive function in conjunction with edge computing and IoT sensors and actuators for eldercare scenarios; specifically identification of individuals, daily activity monitoring, and aberration detection performed on-premises using open source software 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. We will introduce the TensorFlow graph, using its Python API, and demonstrate its use. Starting with simple machine-learning algorithms, we will move on to implementing neural networks. Several real-world deep-learning applications will be discussed, including machine vision, text processing, and generative networks.

Taniya Mishra is the Lead Speech Scientist at Affectiva. Taniya’s 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 completed her PhD in Computer Science from the OGI School of Science & Engineering at OHSU, Portland, Oregon in 2008. Taniya has been a co-author on more than 25 technical publications and has been awarded more than 12 patents related to speech technology. Taniya is passionate about STEM education and mentoring.

Presentations

Humanizing Technology: Emotion Detection from Face and Voice Session

Affectiva is building multi-modal Emotion AI that can detect human emotions from face and voice. In this presentation Dr. Taniya Mishra will talk about how to build multi-modal emotion detection using various deep learning approaches, how to mitigate the challenges of data collection and annotation, and how to avoid bias in model training. She will also cover use cases.

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.

Calum Murray is the chief data architect in the Small Business group at Intuit. Calum has 20 years’ experience in software development, primarily in the finance and small business spaces. Over his career, he has worked with various languages, technologies, and topologies to deliver everything from real-time payments platforms to business intelligence platforms.

Presentations

Executive Briefing: Implementing GDPR across a large, complex, distributed enterprise Session

In large multi-national companies, you inevitably have to deal with large, complex distributed systems and data. You may have offerings that are marketed in the EU (customer data) as well as work-forces based in European countries (employee data). In complex systems, meeting the GDPR regulations may require the creation of a new distributed architecture to handle GDPR requests.

Dr. Yacin Nadji is an expert in computer security. He received his Ph.D. in computer science at Georgia Institute of Technology, is an author of 16 academic publications with over 600 citations, and has served as a reviewer for academic security conferences and journals. In addition to being a recognized expert in the academic community, he has worked at numerous companies building and improving machine learning-based fraud and abuse detection systems at scale, and given talks at several industry conferences and symposia.

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? In this talk, we evaluate, break and fix 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 is a Senior Engineer at Microsoft where she builds foundational algorithms and scalable machine learning systems focused on solutions for Natural Language Question Answering. She has contributed on Entity Linking, Temporal Fact Extraction and Photosynth projects in her time at Microsoft.

Presentations

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

Knowledge acquisition techniques for users in a world of information overload and information manipulation are expected to provide instant, precise and succinct answers. Question Answering Systems are faced with the challenges of serving answers with high accuracy and backed by strong verification techniques. This talk offers an overview of challenges & approaches of such large scale QnA systems.

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

Presentations

Scaling Your Data Science Experiments - from Jupyter notebooks to 6,000 GPUs Session

Data Scientists and Machine Learning professionals today face a quandary of choices when trying to figure out how to scale their data science experiments. This presentation will give attendees the information they need to better understand the landscape of options available to them and how to make best use of the free and open source tools available.

Appointed Principal Adviser of the European Commission on strategic justice issues on 12. April 2017, previously for 6 years Director for Human Righs and Citizenship, leading reform of privacy law in Europe, lead negotiator of EU-US Privacy Shield and of Code of conduct against hate speech and incitement to violence on the internet

Presentations

Democracy, Human Rights and Rule of Law by design for Artificial Intelligence Session

The advent of Artifical Intelligence requires a new innovation model. With the learning, judging and deciding machine becoming ever more pervasive, it is necessary to insert by design and default the basic rules of democracy, human rights and the rule of law into the innovation process and the programmes of Articial Intelligence. The presentation gives examples on how this can be done.

Jan Neumann leads the Comcast Applied Artificial Intelligence Research group with team members in Washington, DC, Philadelphia, Chicago, Denver and Silicon Valley. His team 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.  
Before Comcast, he 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 Ph.D. in Computer Science from the University of Maryland, College Park.

Presentations

How Comcast uses AI to Reinvent the Customer Experience Session

We will describe how we use deep learning to build virtual assistants that allow our customers to contact us with questions or concerns, and how we use contextual information about our customers and systems in a reinforcement learning framework to identify the best actions that answer our customer's questions or resolve their concerns.

Dave joined Intel in 2016 and is currently Technical Program Manager of BigDL. He helped found BigDataCamp and CloudCamp

Presentations

Classify Images in Spark Session

BigDL enables Deep Learning frameworks natively for Apache Spark. Using BigDL, we created a new app to demonstrates how you can use image recognition. To demonstrated Deep Learning in Apache Spark, we developed an app called VegNonVeg. The app uses BigDL framework to classify images of food as vegitarian or non-vegitarian.

Engineer at Intel with PhD from Purdue in Electrical and Computer Engineering. Joined Intel in 2010

Presentations

HIGH throughput Single-shot multi-box object detection (ssd) on edge devices using fpga Session

Using Single-shot multi-box detection (SSD) for object detection, specifically using SSD for security 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

Jeetu Patel is Chief Product Officer at Box. He leads the company’s overall product and platform strategy, driving Box’s long-term roadmap and vision for cloud content management in the enterprise. Previously, as Chief Strategy Officer and SVP of Platform at Box, 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. Prior to EMC, Jeetu was president of Doculabs, a research and advisory firm co-owned by Forrester Research focused on collaboration and content management across a range of industries, including financial services, insurance, energy, manufacturing and life sciences.

Jeetu holds a B.S. in Information Decision Sciences from the University of Illinois Chicago.

Presentations

AI and the Future of Work Session

Artificial Intelligence is gearing up to be the most important technological advancement since the internet, yet to many people, it still feels like companies are years away real technological breakthroughs, especially for the enterprise. How are companies applying the technology today and what does the future of AI in the enterprise look like?

Mo Patel is an independent Deep Learning consultant, focusing on independently advising on strategic and technical AI topics to individuals, startups and enterprise. Previously, as 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. 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, Mo was a management consultant and a software engineer. 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 renaissance of Artificial Intelligence, pushing it forward is a flexible framework for training models called PyTorch. In this tutorial we will understand computer vision fundamentals and walk 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 renaissance of Artificial Intelligence, pushing it forward is a flexible framework for training models called PyTorch. In this tutorial we will understand computer vision fundamentals and walk 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 a deep learning anomaly detection platform Session

Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Michael Balint will 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. She has a long-standing interest in how people perceive and digest complex information. Emily co-founded 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. However, much remains to be done in order for AI collaborate with people. Drawing inspiration from the way humans accumulate knowledge and naturally work together, we will share new insights that enable machines and people to work and learn as a team, discovering new knowledge in unstructured natural language content together.

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. During Brian’s 16 years with Wells Fargo, he and his teams have led large, multi-channel efforts including Mobile Remote Deposit, Apple Pay, P2P payments, Transfers, Bill Pay and the launch of an online brokerage platform. Brian was the head of the mobile banking function for Wells Fargo from 2011-2014, a period of explosive growth and innovation. Prior to Wells Fargo, he worked for both industry leaders such as First Data Corporation and Anderson Consulting, as well as an Internet startup.

Brian is based in San Francisco and lives in the East Bay with his wife and 3 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, with banks across the world utilizing them for everything from basic customer service to assisting internal IT support. But chatbots only skim the AI landscape. AI can help us 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 at IBM working 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. In this talk I explore the newest 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

DataKind Founder Jake Porway will help shed light on AI’s true potential to impact the world in a positive way. As the head of an organization applying AI for social good, Jake will share best practices, discuss the importance of using human-centered design principles, and address ethical concerns and challenges one may face in using AI to tackle complex humanitarian issues.

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. Prior to joining Shutterstock, Mike held a variety of software developer roles at New Century Software, Boulder Imaging and most recently, AlchemyAPI, which was acquired by IBM Watson. Mike first became enamored with ray tracers, and then soon after machine vision while studying at Colorado State University. He started working as an intern programmer in his first year of college and has been a professional since. During his time at AlchemyAPI, Mike spearheaded a natural scene optical character recognition (OCR) project that provided an API to extract text from images. He was also a member of the larger machine vision group at AlchemyAPI that launched the industry’s first commercial image tagging and similarity API. In 2015, Mike joined Shutterstock, which he considers a veritable candy store for training data thanks to its massive image collection. Mike is passionate about cycling, and spends most of his free time training for races as a new domestic pro. Mike graduated from Colorado State University with a BS in Computer Science.

Presentations

The Search for a New Visual Search, Beyond Language Session

Mike Ranzinger of Shutterstock will detail his research on composition aware search. He will demonstrate how the research led to the launch of AI technology allowing users to more precisely find the image they need within Shutterstock’s collection of more than 150 million images. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI.

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.

Naveen Rao is the vice president and general manager of Intel’s artificial intelligence products group. Naveen’s fascination with computation in synthetic and neural systems began around age nine when he began learning about circuits that store information and encountered the AI themes prevalent in sci-fi at the time. He went on to study electrical engineering and computer science at Duke, but continued to stay in touch with biology by modeling neuromorphic circuits as a senior project. After studying computer architecture at Stanford, Naveen spent the next 10 years designing novel processors at Sun Microsystems and Teragen, specialized chips for wireless DSP at Caly Networks, video content delivery at Kealia, Inc., and video compression at W&W Comms. After a stint in finance doing algorithmic trading optimization at ITG, Naveen was part of the Qualcomm’s neuromorphic research group leading the effort on motor control and doing business development. Naveen was the founder and CEO of Nervana (acquired by Intel), which brings together engineering disciplines and neural computational paradigms to evolve the state of the art and make machines smarter. Naveen holds a PhD in neuroscience from Brown, where he studied neural computation and how it relates to neural prosthetics in the lab of John Donoghue.

Presentations

Keynote by Naveen Rao Keynote

Keynote by Naveen Rao

Ofer leads the Chatbase bot analytics team within Area 120. (Area 120 is an incubator for early-stage products operated by Google). Previously he served as CEO of Pulse.io, an app performance monitoring service, which sold to Google. In addition he was CEO of Sendori, an ad network, which sold to IAC. He is a startup mentor at Stanford and an angel investor in Lyft, Palantir, and Klout. Ofer 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. Certain issues in particular make building bots that do not frustrate users difficult. This presentation will delve into such issues and suggest ways, including machine learning, for developers to save time addressing them.

Mike Ruberry is a Senior Associate of Data Science at ZestFinance, where his research interests include explainability and generative models. He has four degrees in Computer Science, including a PhD from Harvard University. During and since his his doctorate, 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.

Presentations

Explaining Machine Learning for Consumer Loans Session

This talk will describe how to evaluate and manage the complexity of machine learning to meet regulatory requirements for consumer loans. This will be an interesting and generally applicable subject to anyone working in a regulated industry or high-risk field, as it relates a seemingly abstract machine learning concept (explainability) directly to a critical business requirement.

Dr. Olga Russakovsky is an Assistant Professor in the Computer Science Department at Princeton University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. She completed her PhD at Stanford University and her postdoctoral fellowship at Carnegie Mellon University. She was awarded the PAMI Everingham Prize as one of the leaders of the ImageNet Large Scale Visual Recognition Challenge, the NSF Graduate Fellowship and the MIT Technology Review 35-under-35 Innovator award. In addition to her research, she co-founded the Stanford AI Laboratory’s outreach camp SAILORS to educate high school girls about AI. She then co-founded and continues to serve as a board member of the AI4ALL foundation dedicated to educating a diverse group of future AI leaders.

Presentations

Keynote by Olga Russakovsky Keynote

Keynote by Olga Russakovsky

I have a background of Geography, Environmental Science, Remote Sensing and Machine Learning and I have been working within Research and Innovation over the past 15 years . I have a wide experience within private and public industry and currently working for the Research and Innovation team within Customer Solutions at NATS in the UK.

Presentations

Revolutionising Aviation with AI Session

The Aviation industry is awakening to new technologies that can revolutionise the way it operates and evolve. Airports in particular are perfect candidates for AI and Machine Learning concepts. NATS is collaborating with several companies and institutes to change the way that data is captured and processed to transform Airport operations.

Nabeel Sarwar

Machine Learning Engineer, Comcast NBCUniversal

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

Presentations

Machine Learning meets DevOps – Paying down the High Interest Credit Card Session

We propose processes and systems 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 Google’s Cloud Platform team, where he focuses on machine learning and data analytics products, such as TensorFlow, Cloud ML, and BigQuery. Kaz has also led and supported developer communities for Google Cloud for over eight years. He has been an invited speaker at events including Google Cloud Next ’17 SF, Google I/O 2016 and 2017, the 2017 Strata Data Conference in London, the 2016 Strata + Hadoop World in San Jose and NYC, the 2016 Hadoop Summit, and ODSC East 2016 and 2017. Kaz is also interested in hardware and the IoT and has been hosting FPGA meetups since 2013.

Presentations

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

TensorFlow Lite is TensorFlow’s lightweight solution for Android, iOS and embedded devices. It enables on-device machine learning inference with low latency and a small binary size. In this session, we will discuss how developers can use TensorFlow Lite to overcome the challenges for bringing the latest AI technology to production mobile apps and embeded systems.

Jorge Silva is Principal Machine Learning Developer at SAS. Previously, he was Adjunct Professor at Instituto Superior de Engenharia de Lisboa (ISEL), and Sr. Research Scientist at Duke University. He received his PhD in Electrical and Computer Engineering from Instituto Superior Técnico (IST), Lisbon.
His research interests include statistical models applied to large-scale problems, e.g., manifold learning, computer vision and recommender systems. He holds multiple U.S. patents and has authored numerous scholarly papers.

Presentations

Online and Active Learning for Recommender Systems Session

Recommender systems suffer from concept drift and scarcity of informative ratings - we use a Bayesian approach to tackle both problems by making the learning process online and active. Online learning deals with concept drift, i.e., changing user preferences. Active learning prioritizes the most informative users and items by quantifying uncertainty in a principled, probabilistic framework.

Kaarthik is a Principal Software Engineer in the AI Infrastructure and Tools group at Microsoft. In his current role, he is building a platform for scale-out deep learning to unlock the full potential of GPU Cloud, Data and the 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 Mobius project (https://github.com/Microsoft/Mobius) and used it to implement Spark Streaming workloads for timely, high-fidelity processing of Bing logs at scale. Prior to joining Microsoft, Kaarthik was a Senior Software Engineer in a semantic technology start-up 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

In this session we will share our 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, Ph.D. is the Senior Vice President and Chief Data Officer at Intuit. 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. He is hiring hundreds of people in machine learning, AI, and related areas at all levels.

Previously, he was Vice President of Big Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. His global team focuses on building new revenue-generating products and services powered by big data and artificial intelligence. He is an Adjunct Professor at Stanford in the Electrical Engineering Department 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 extensive experience in research, development, and implementation of machine learning and optimization techniques on massive data sets. He has built successful research and development teams in these areas and serves as an advisor to numerous companies such as Trident Capital and MyBuys in the area of big data analytics and strategic investments.

Ashok has a range of business experience including serving as Senior Director at Blue Martini Software and Senior Consultant at IBM. In these roles, he led engagements with numerous Fortune Global 500 companies including Bank of America, Saks 5th Avenue, Chevron, and LG Semiconductor.

He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a Ph.D. in Electrical Engineering from the University of Colorado at Boulder.

For more information see: ashoksrivastava.com

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 their customers’ biggest financial pains. Join this session with Intuit Chief Data Officer Ashok Srivastava to learn more about technology’s role in solving economic problems, and how Intuit is using their financial data set 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 way to real-world AI is a long and winding road. All what we heard from reputable experts turned out to be true: The need for better data, a new UX, new ways of learning and many more. This session highlights the lessons we have learnt while implementing cognitive AI applications to help consumers finding the products they love. With evidence what to expect in case you build AI too lean.

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. He works on a variety of machine learning problems in the areas of algorithmic trading, risk management, operations and compliance and wealth/investment management. He has extensive experience in different types of machine learning problems such as sales recommendations, classification/regression, clustering, anomaly detection, NLP/NLU and optimal control. He helps the firm set its AI strategy and and leads innovation initiatives. His area of interest is in applying newer AI techniques such as deep generative models, deep reinforcement learning, recurrent neural networks on use cases in finance. He is a strong proponent of unsupervised/semisupervised learning methods. Ambika’s background is in signal processing and information theory and he has a Masers in Telecommunications 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. In this talk we will discuss how recent advancements in AI can help simplify financial model building by carefully replacing complex mathematics with a data driven incremental learning approach.

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. I present my research addressing two core scalability bottlenecks related to (i) tuning the knobs of deep learning models (i.e., hyperparameter optimization), and (ii) studying the landscape of 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 rst and largest In-Image advertising platform, revolutionizing the industry.

Ophir’s was named one of Adweek’s “Young Inuentials,” was featured on the cover of Entrepreneur Magazine and received the Siemer Summit Innovation in Advertising Award.

Prior to launching GumGum in 2007, Ophir was the CEO and co-founder of Mojungle.com, a mobile media-sharing platform that was sold to Shozu.com in 2007. Before this, Ophir co-founded and sold Fluidesign, an award-winning interactive and branding agency.

Ophir holds a B.S. and a M.S. from Carnegie Mellon University and currently lives in Los Angeles, CA.

Presentations

3 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. The benefits of applied AI in computer vision are only beginning to emerge. This session will explain the tools and image technology utilizing AI that you can apply to your business today.

Wee Hyong Tok is a principal data science manager for the cloud AI team 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/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique super powers to nurture and grow high-performing innovation teams that enable organizations to embark on their data-driven digital transformations using artificial intelligence. He 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 and strongly believes in story-driven innovation. 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

In this session we will share our 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. Containers are allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean, as they share with you how to use the Cognitive Toolkit (CNTK) with Kubernetes clusters.

Carmen joined Intel in 2015 and current is a Artificial Intelligence & Analytics Training Specialist. She is part of the AIPG group at Intel.

Presentations

Hunt For Lunar Ice: AI Lunar Crater Detector Session

Repurpose NASA FDL Lunar Crater Identification solution as a NIPs showcase of using AI in space resource exploration and highlighting our collaboration with NASA FDL team. We will also provide do it yourself instructions and guides so that people can join in on the excitement and recreate their very own lunar crater detector at home

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

Embedded Systems Architect
Robotics, IoT and automotive electronics

I am a results oriented hands-on engineering leader, an entrepreneur, and an innovator with extensive experience in bringing embedded products to market. My crazy-passion towards deploying products and sustaining them has led me through a journey of being hands-on at every stage of product development life cycle. I am currently focused on the front and rear end of the cycle. i.e. Requirements gathering & analysis, prototyping, deployment, training & sales support, and maintenance & technical support.

Specializations:
• Product management, applications engineering & sales support
• Ecosystem development
• Robotics & IoT architecture design
• Product design & deployment
• Embedded hardware & firmware design
• Automotive ECU software design
• Reverse Engineering

Presentations

Accelerate deep Neural Networks at the Edge with the Intel Movidius Neural Compute Stick Tutorial

Hands on workshop using is 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 (Machine Learning) at Comcast NBCUniversal working on operationalizing Machine Learning models to enable rapid turnaround times from model development to model deployment. In the process he oversees data ingestion from data-lakes, streaming data-transformations and model deployment in hybrid environments ranging from on-premise, cloud as well as edge devices. Previously, he has 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. He has implemented Natural Language Processing (NLP) and Computer Vision based systems for various public/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

We propose processes and systems 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.

2015 – present: Machine learning specialist at the Commonwealth Scientific and Industrial Research Orgainsation (CSIRO).

2010 – present: PhDc at Monash University in Computational Quantum Physics

2017 – Technical assistant at the Creative Destruction Lab Quantum Machine Learning incubator program https://www.creativedestructionlab.com/locations/toronto/quantum/ .

2017 – Lecturer in parallel computing at Monash University

2016 – Researcher at the inaugural Frontier Development Lab http://frontierdevelopmentlab.org/#/ .

Presentations

Reliable and Robust Classification Pipeline for Protein Crystallisation Imaging Session

The achievement of human level accuracy in image classification through the use of modern AI algorithms has renewed interest its application to automated protein crystallisation imaging. This session aims to discuss the development of the deep tech pipeline required for the robust operation of an on-line classification system in CSIRO's GPU cluster and the lessons learned along the way.

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. Prior to SessionM, Scott served as Vice President of Product & Technology for Scientific Games (SGMS). While at Scientific Games, Scott oversaw the development and integration of interactive technologies into MDI’s products and services. Earlier in his career, Scott served as Vice President of Product and Technology at GameLogic (acquired by Scientific Games in 2010), Co-Founder and GM of SnapYap.com, Principle Software Engineer at Terra / Lycos Inc., and Senior Software engineer at Gamesville.com (acquired by Lycos in 1999). At Lycos, Scott spent several years innovating data and advertising platform technologies. At the age of 16, he joined a team of eight motivated geeks to help build country’s first Internet service provider, later acquired by Conversant Communications. Scott graduated from the University Of Rhode Island with a BS in Computer Science.

Presentations

The Vital Role of Failure in Machine Learning Session

In video games, players learn by failing. They might “die” 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 that we can play with, yet we expect immediate success. Is this realistic? Technologist Scott Weller explores the role of failure in machine learning using real-world examples.

Greg Werner is Founder and CEO of 3Blades.io, an open source integration data science platform. Greg has built Information Technology businesses his entire career including; co-founding 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 & Gas, Insurance, Telco and Financial Verticals. In 2015 3Blades was formed to build a more dynamic data science platform which allows data scientists and business analysts to deploy artificial intelligence models based on experience from these industries.

Greg is a co-organizer of the PyData Meetup group in Atlanta. Greg frequently contributes to open source projects that help the scientific community, particularly those within the Python ecosystem. Greg earned a bachelor of arts in economics from Emory University, an MBA in international management from Thunderbird and a masters 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

This workshop will focus on how to use 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. The workshop will also touch on how to monitor and iterate upon trained models for continued success using standard development and operations tools.

Megan has a BA and MS in Statistics from the University of Virginia. With experience in data migration and data science, she is now a member of the Center for Machine Learning at Capital One. Her research areas include threshold testing, feature selection, and linkage analysis. She has production experience with Natural Language Processing and various Neural Networks.

Presentations

Using NLP, Neural Networks, and Reporting Metrics in Production for Continuous Improvement in Text Classifications Session

The combination of a custom tokenized vocabulary set, embedding, CNNs, and Bi-LSTMs classify tags ranging from sentiment to low level content on raw text. Monte Carlo simulations and non-parametric tests determine what classifications to report on. Additional human validated classifications are continuously added to the model to increase model accuracy as well as add a check on model performance.

Greg Zaharchuk, MD/PHD is an associate professor in Radiology at Stanford University and a neuroradiologist at Stanford Hospital.

Dr. Zaharchuk’s 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
Resting-state fMRI for perfusion imaging and stroke

Presentations

Deep Learning and AI is changing clinical neuroimaging: faster, safer and smarter Session

What is the impact of AI and deep learning to clinical workflow? Going beyond simple classification tasks, in this talk, we will introduce AI/deep learning technologies invented at Stanford and applied in clinical neuroimaging workflow at Stanford Hospital, providing faster, safer, cheaper and smarter medical imaging and treatment decision making.

Julie is a Data Scientist with Optum Tech

Presentations

Imputing medical conditions based on patient's medical history with Deep Learning Session

We cover a general application of Deep Learning to a time series problem for a patient's history. The goal was to impute a medical condition based on a multi-year history of prescriptions filled by an individual. We benchmarked Deep Learning against traditional Machine Learning methods and found it more accurate with no feature engineering. We describe a Keras implementation.

Xiaoyong Zhu is a Microsoft Program Manager focusing on distributed machine learning and it’s applications.

Presentations

Scaling up Deep Learning Based Super Resolution Models more efficiently using Cloud Session

In this talk, we will demonstrate the latest academic progress in Super Resolution field using Deep Learning, especially GANs, and how it can be applied in various industries, such as Medical area; we will also showcase how the training could be done in a distributed fashion on the Cloud, which gives more torch light to researchers and data scientists who are in this empirical field.

Dr. Scott Zoldi is Chief Analytics Officer at FICO responsible for the analytic development of FICO’s product and technology solutions, including the FICO™ Falcon® Fraud Manager product which protects about two thirds of the world’s payment card transactions from fraud. While at FICO, Scott has been responsible for authoring 80 analytic patents with 40 patents granted and 40 in process. Scott 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. Scott is most recently focused on the applications of streaming self-learning analytics for real-time detection of Cyber Security attack and Money Laundering. Scott serves on two boards of directors including Tech San Diego and Cyber Center of Excellence. Scott received his Ph.D. in theoretical physics from Duke University.

Presentations

Innovations in Explainable AI in the context of real world business applications Session

Reason Reporter explains the workings of neural network models used to detect fraudulent payment card transactions in real time. Comparative study with Local Interpretable Model-agnostic Explanations (LIME) exposes why former is better at providing explanations. A novel patent pending architecture called LENNS would be discussed that exposes more of what’s driving the score.

Lindsey Zuloaga holds a Ph.D. in Physics and is the Director of Data Science at HireVue. She is very interested in how AI can help humans make better decisions.

Presentations

Avoiding Biased Algorithms: Lessons from the Hiring Space Session

We are all familiar with the highly-publicized stories of algorithms displaying overtly biased behavior towards certain groups. What is actually happening behind the scenes and how can these situations be avoided? In this session, Lindsey Zuloaga (HireVue) will share experiences and lessons learned in the hiring space to help others avoid unfair modeling and work to establish best practices.