Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Speakers

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

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As SVP, Product at WorkFusion, Mikhail leads Product Management at WorkFusion. He oversees research and development and strategic automation goals of charter customers and strategic partners. Prior to joining WorkFusion as an employee #1, spent many years in the consulting industry focusing on technology solutions in banking and financial informational services. Mikhail holds BS and MS degrees in Computer Science from Belarusian State University.

Presentations

Fighting Financial Crime with AI: Beyond Fraud Detection with AI-powered RPA 40-minute session

Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant Anti-Money Laundering (AML) and Know-Your-Customer (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. This session will walk-through a series of case studies that utilize AI-powered RPA that address AML and KYC.

Sarah Aerni is a Director of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications using autoML. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.

Presentations

Executive Briefing: Agile AI 40-minute session

How does Salesforce manage to make data science an agile partner to over 100,000 customers? We will share the nuts and bolts of the platform and our agile process. From our open-source autoML library (TransmogrifAI) and experimentation to deployment and monitoring, we will cover how the tools make it possible for our data scientist to rapidly iterate and adopt a truly agile methodology.

Vijay Srinivas Agneeswaran is a senior director of technology at Publicis Sapient. Vijay has spent the last 12 years creating intellectual property and building products in the big data area at Oracle, Cognizant, and Impetus, including building PMML support into Spark/Storm and implementing several machine learning algorithms, such as LDA and random forests, over Spark. He also led a team that build a big data governance product for role-based, fine-grained access control inside of Hadoop YARN and built the first distributed deep learning framework on Spark. Earlier in his career, Vijay was a postdoctoral research fellow at the LSIR Labs within the Swiss Federal Institute of Technology, Lausanne (EPFL). He is a senior member of the IEEE and a professional member of the ACM. He holds four full US patents and has published in leading journals and conferences, including _IEEE Transactions.

His research interests include distributed systems, cloud, grid, peer-to-peer computing, machine learning for big data, and other emerging technologies.

Vijay holds a bachelor’s degree in computer science and engineering from SVCE, Madras University, an MS (by research) from IIT Madras, and a PhD from IIT Madras.

Presentations

Industrialized Capsule Networks for Text Analytics 40-minute session

We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics. 2. We show an implementation of recurrent capsule networks. 3. We also benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.

David Arpin is a data scientist at Amazon Web Services.

Presentations

Building a recommender system with Amazon SageMaker Tutorial

Learn how to use the Amazon SageMaker platform to build a machine learning model to recommend products to customers based on their past preferences.

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

Presentations

Deep Learning with TensorFlow 2-Day Training

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Deep Learning with TensorFlow (Day 2) Training Day 2

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

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

Presentations

Introducing the AI Fairness 360 Toolkit Tutorial

Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias.

Till Bergmann is a Senior Data Scientist at Salesforce Einstein, building platforms to make it easier to integrate machine learning into Salesforce products, with a focus on automating many of the laborious steps in the machine learning pipeline. Before Salesforce, he obtained a PhD in Cognitive Science at the University of California, Merced, where he studied collaboration patterns of academics using NLP techniques.

Presentations

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

A problem in predictive modeling data is label leakage. At Enterprise companies such as Salesforce, this problem takes on monstrous proportions as the data is populated by diverse business processes, making it hard to distinguish cause from effect. We will describe how we tackled this problem at Salesforce, where we need to churn out thousands of customer-specific models for any given use case.

Aishani Bhalla is a software engineer on the Azure Machine Learning team. At Microsoft, she’s helping people operationalize models to build intelligence into every application, accelerated on FPGAs with Project Brainwave. She has a BS in Computer Science from the University of Buffalo.

Presentations

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

Deep neural networks (DNNs) have enabled breakthroughs in AI. Serving DNNs at scale has been challenging: fast and cheap? Won’t be accurate. Accurate and fast? Won’t be cheap. You’ll learn how Python and TensorFlow can be used to easily train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave, getting performance such as ResNet 50 in under 2 ms.

Tammy Bilitzky is responsible for managing DCL’s technology department, continuing its focus on resilient, high-quality, and innovative products, while helping to grow the organization. Tammy has extensive experience in leveraging technology to deliver client value, supporting business process transformation and managing complex, large-scale programs onshore and offshore. Tammy seeks to ensure that DCL uses the best talent and tools in the marketplace and to implement methodologies that will consistently provide clients with superior quality. Tammy joined DCL in 2013 following three decades with Marsh and McLennan Inc. subsidiary Guy Carpenter & Co., LLC, a global leader of reinsurance and capital management, where she successfully served in numerous senior technology management roles.

Presentations

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

A case study that details lights-out automation and how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units.

PRADIP BOSE is a Distinguished Research Staff Member and Manager of the Efficient & Resilient Systems Department at IBM T. J. Watson Research Center, Yorktown Heights, NY. He has been involved in the design and pre-silicon modeling of virtually all IBM POWER-series microprocessors, since the pioneering POWER1 (RS/6000) machine, which started as the Cheetah (and subsequently America) superscalar RISC project at IBM Research. From 1992-95, he was on assignment at IBM Austin, where he was the lead performance engineer in a high-end processor development project (POWER3). During 1989-90, Dr. Bose was on a sabbatical assignment as a Visiting Associate Professor at Indian Statistical Institute, India, where he worked on practical applications of knowledge-based (AI)systems. His current research interests are in high performance computers, artificial intelligence, power- and reliability-aware microprocessor architectures, accelerator architectures, pre-silicon modeling and validation. He is the author or co-author of over a hundred publications (including several book chapters) and he also serves as an Adjunct Professor ar Columbia University. He has received twenty five Invention Plateau Awards, several Research Accomplishment and Outstanding Innovation Awards from IBM. Dr. Bose served as the Editor-in-Chief of IEEE Micro from 2003-2006 and as the chair of ACM SIGMICRO from 2011-2017. He is an IEEE Fellow and a member of the IBM Academy of Technology.

Presentations

Towards Self-Aware Resilent Systems and Ethical Artifical Intelligence 40-minute session

We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed.

Fidan Boylu has 10+ years of technical experience on data mining and business intelligence. She holds a Ph.D in Decision Sciences and is a former professor conducting research and teaching courses on data mining and business intelligence at the University of Connecticut. She has a number of academic publications on machine learning and optimization and their business applications. She currently works as a senior data scientist at Microsoft responsible for successful delivery of end to end advanced analytic solutions. She has worked on a number of projects on predictive maintenance, fraud detection and computer vision.

Presentations

Deploying Deep Learning Models on GPU Enabled Kubernetes Clusters 40-minute session

Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? This session will help you by providing a step-by-step guide to go from a pre-trained deep learning model, package it in a Docker container and deploy as a webservice on Kubernetes cluster.

Chris Butler is the director of AI at Philosophie, where he leads the firm in human-centered AI engagements. Chris has over 19 years of product and business development experience at companies like Microsoft, KAYAK, and Waze. He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University and has worked on AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He has created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Presentations

Design Thinking for AI Tutorial

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

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

Presentations

Game Engines and Machine Learning 40-minute session

Learn how to use Unity to train, explore, and manipulate intelligent agents that learn. Train a quadruped to walk. Then train it to explore, fetch, and manipulate the world. Games are great places to explore AI. They’re wonderful contained problem spaces. Learn how to use them, even though you’re not a game developer.

Angelo A. Calvello is co-founder of Rosetta Analytics, Inc., a technology startup using proprietary deep learning techniques to create investment signals and scalable investment strategies.

He is also co-founder of Distributed Alpha, LLC, a proprietary trading firm that invests in mispriced application-driven, decentralized technologies, crypto currencies, and Blockchain tokenized assets, and founder of Impact Investment Partners, an investment consulting firm.

Additionally, Calvello is the “Dissident” columnist for Institutional Investor, where he had the most read opinion columns in 2017, and former columnist for Chief Investment Officer. His column, “The Doctor Is In,” won the American Business Media’s 2016 Jesse H. Neal Award for best commentary. Calvello has also published extensively in other publications, including Pensions & Investments, Healthcare Financial Management, American Indian Culture and Research Journal (UCLA).

As a serial innovator and author and speaker focusing on artificial intelligence, Calvello’s unique insights into the rise of disruptive technologies and how they are re-shaping investment management—new sources of return and new tools to measure and manage risk, realignment of the relationships between asset owners and asset managers, different composition and demographics of the workforce, and transformation of traditional business models—have revealed the future of beneficial investing.

Calvello has over 25 years of experience in the institutional investment industry and holds a PhD in Contemporary European Philosophy from DePaul University. He is the author of Environmental Alpha: Institutional Investors and Climate Change (Wiley 2009), Founder and Publisher of the Journal of Environmental Investing, and member of the Chicago Quantitative Alliance. Calvello is the Chairman of the Board of Outreach with Lacrosse and Schools, a sports-based non-profit that creates educational opportunities for at-risk youths on Chicago’s South and West sides.

Presentations

Executive Briefing: AI Changes Everything—Except in Investment Management 40-minute session

While there is no doubt that AI could be used to create better investment outcomes, successfully adopting AI requires asset managers to radically transform their existing business models and investment processes. Faced with such possible disruption, leadership will instead choose to maintain the status quo and introduce diluted forms of AI.

I am senior at Horace Mann School. I’ve been actively involved with Concerts in Motion since middle school, spending Sunday afternoons singing with seniors in nursing homes. I’ve also participated in seasonal events at the Turtle Bay Music School where we raised a music education fund for children from disadvantaged families. The friendships I’ve developed during these events have helped me to understand just how much music can mean to someone. Instead of just listening to someone singing once a week, everyone should be able to create their own music. Thus, I had the idea of combining my love for singing and recent technological advancements to help others compose their own pieces. I developed this research under the guidance of Professor David Gu.

We are among the first to apply a hybrid of HMM and convolutional neural network with LSTM to the music composing. The hybrid approach is further extended to allow users to specify magnitude of revision, duration of music segment to be revised, choice of genres, popularity of songs, and co-creation of songs in social settings. The mobile user interface we designed are intuitive, interactive, and flexible, suitable to the elderly and young children.

Presentations

Make Music Composing Easier for Amateurs: A Hybrid Machine Learning Approach 40-minute session

We propose a framework that unifies Hidden Markov Model and deep learn algorithm (RNN) with modeling components that consider long-term memory and semantics of music (LSTM and Convolution). It takes user original creation as input, modifies the raw scores, and generates musically appropriate melodies.

One of the most widely published machine learning researchers in the world, Lawrence Carin (he/him) has been a Professor of Electrical and Computer Engineering at Duke University for the past 22 years, and he has served as the Vice Provost for Research at Duke for over three years. Lawrence is an IEEE Fellow and has co-authored over 350 papers affecting fields as diverse as bomb detection, neuroscience, and voting behavior. Lawrence earned his BS, MS, and Ph.D. in electrical engineering from the University of Maryland. Outside of the office, Lawrence spends every possible minute with his family and works hard to keep his body as fit as his mind.

Presentations

Executive Briefing: From Cutting-Edge AI Research to Business Impact 40-minute session

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

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

Presentations

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

Using Topological Data Analysis, one can describe the functioning and learning of a neural network in a compact and understandable way. This understanding results in material speedups in performance (training time + accuracy) and allows for data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all data sets.

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

Presentations

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

In recent years, we have seen tremendous improvements in artificial intelligence. The major breakthroughs are due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. These issues will drastically impact the future of AI research.

Nandhini Chandramoorthy is a post-doctoral researcher at IBM T. J. Watson Research Center, Yorktown Heights, NY. She holds a Ph.D degree from Penn State University. Her research interests are in computer architecture, artificial intelligence, power-aware design and modeling methodologies.

Presentations

Towards Self-Aware Resilent Systems and Ethical Artifical Intelligence 40-minute session

We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed.

Data science expert and software system architect with expertise in machine-learning and big-data systems. Rich experiences of leading innovation projects and R&D activities to promote data science best practice within large organizations. Deep domain knowledge on various vertical use cases (Finance, Telco, Healthcare, etc.). Currently working pushing the cutting-edge application of AI at the intersection of high-performance database and IoT, focusing on unleashing the value of spatial-temporal data. I am also a frequent speaker at various technology conferences, including: O’Reilly Strata AI Conference, NVidia GPU Technology Conference, Hadoop Summit, DataWorks Summit, Amazon re:Invent, Global Big Data Conference, Global AI Conference, World IoT Expo, Intel Partner Summit, presenting keynote talks and sharing technology leadership thoughts.

Received my Ph.D. from the Department of Computer and Information Science (CIS), University of Pennsylvania, under the advisory of Professor Insup Lee (ACM Fellow, IEEE Fellow). Published and presented research paper and posters at many top-tier conferences and journals, including: ACM Computing Surveys, ACSAC, CEAS, EuroSec, FGCS, HiCoNS, HSCC, IEEE Systems Journal, MASHUPS, PST, SSS, TRUST, and WiVeC. Served as reviewers for many highly reputable international journals and conferences.

Presentations

Deep Learning and GPU Acceleration for Data Compression in Time Series Database 40-minute session

Time series database (TSDB) is of great use for data management in IoT, finance, etc. Performance is always a major optimization point for TSDB. Recently, we introduced neural networks and reinforcement learning to perform mode selection for compression algorithm. Experimental results show one can improve average compression ratio by 20%-120%, comparing with other well-known compression format.

Joanna graduated from Claremont McKenna College in May 2012 with a B.A. in Neuroscience and from California State University, Fullerton in May 2015 with a M.S. in Computational Statistics. Since graduate school, she has worked in management consulting as a data analyst with an emphasis on applying machine learning techniques and performing predictive analytics for clients in the health, education, and technology sectors.
Currently, Joanna is a Data Scientist at Booz Allen Hamilton. She is part of the Strategic Innovation Group and focuses on developing cognitive computing platforms/solutions for US federal agencies.

Presentations

Delivering Cognitive Computing Solutions in the US Federal Market 40-minute session

Cognitive Solutions, the application of intelligent technology and services to empower the user to draw insights from data using natural human interaction, is a disruptive force in the US Federal market and is changing the way citizens engage with data.

Presentations

Leveraging Data Science in Asset Management 40-minute session

Overview of data science applications within the asset management industry Specific use cases using ML to derive better investment insights and improve client engagement

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

Presentations

Opening Remarks Keynote

Opening Remarks

Opening Remarks Keynote

Opening Remarks

Data scientist with deep knowledge in large-scale machine learning algorithms. Partnered with several Fortune 500 companies and advise the leaderships on making data-driven strategic decisions. Provided software-based data analytics consulting service to 7 global firms across multiple industries, including financial services, automotive, telecommunications, and retail.

Presentations

Deep Learning and GPU Acceleration for Data Compression in Time Series Database 40-minute session

Time series database (TSDB) is of great use for data management in IoT, finance, etc. Performance is always a major optimization point for TSDB. Recently, we introduced neural networks and reinforcement learning to perform mode selection for compression algorithm. Experimental results show one can improve average compression ratio by 20%-120%, comparing with other well-known compression format.

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

Presentations

Applied machine learning in finance 40-minute session

In this talk we will see how machine learning and deep learning techniques can be applied in the field of quantitative finance. We will look at a few use-cases in detail and see how machine learning techniques can supplement and sometimes even improve upon already existing statistical models. We will also look at novel visualizations to help us better understand and interpret these models.

Andrew Y. Chin is the Chief Risk Officer and Head of Quantitative Research for AB. As the Chief Risk Officer, Chin oversees all aspects of risk management to ensure that the risks being taken are well understood and appropriately managed. In the Quantitative Research role, he is responsible for the firm’s data science strategy and for optimizing the quantitative research infrastructure, tools and resources across the firm’s investing platforms. He joined the firm in 1997 and held various quantitative research roles in New York and London. In 2004, Chin became a senior portfolio manager for Style Blend Equities. In 2005, he was named director of Quantitative Research for Value Equities. Prior to joining the firm, Chin was a project manager and business analyst in Global Investment Management at Bankers Trust from 1994 to 1997.

Chin teaches in the School of Operations Research and Information Engineering (Master of Financial Engineering Program) at Cornell University. He also leads teams of students on capstone projects utilizing quantitative and data science skills to address investment issues.

Chin earned a BA and an MBA from Cornell University.

Presentations

Leveraging Data Science in Asset Management 40-minute session

Overview of data science applications within the asset management industry Specific use cases using ML to derive better investment insights and improve client engagement

Scott is a co-founder and CEO of SigOpt, providing optimization tools as a service, helping experts optimally tune their machine learning models. Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.

Presentations

Best practices for scaling modeling platforms 40-minute session

Increasingly, companies building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. During this talk, we use a case study from a leading algorithmic trading firm to draw general best practices for building these types of platforms in any industry.

Speaker: MARIA YAP
Maria Yap is Vice President of the Digital Imaging Organization and leads product development for Photoshop and Lightroom, Adobe’s renowned imaging products. Over her 20 years at Adobe, Maria has applied her strong focus on the customer, helping to build products such as Acrobat, InDesign, Creative Suite, Photoshop and Lightroom. Before joining Adobe, Maria founded her own company of designers and production experts, where she brought her unique insight as an Adobe customer to her product strategies. She continues her passion for creativity through photography, crafting and gardening. @mariayap

Presentations

Pushing Creative Boundaries with AI 40-minute session

Creatives are always looking at ways to expand their toolbox by exploring new filters, effects and ways to organize their work. Can AI actually boost creativity?

Ira Cohen is a cofounder and chief data scientist at Anodot, where he is responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Presentations

Deep Learning for Time Series Data 40-minute session

In this talk we shall shares a novel two-step approach for building more reliable prediction models by integrating anomalies in them. Further, we shall walk the audience through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data.

As senior technical product manager @ Facebook, I am working on AI/ML Platform (FBLearner) that enables more personalized and smarter FB products.

I was senior software engineer manager @ Hewlett Packard Enterprise and Cisco System previously. I hold a Ph.D degree of Computer Engineering and MBA from University of California, Berkeley. My passion is to democratize AI across various industries thru building a performant, reliable, efficient, resilient and easy-to-use AI platform.

Presentations

Building a production-scale ML platform 40-minute session

An overview of why, what & how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions.

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

Presentations

Executive Briefing: Fear and Loathing in Explainability and Transparency - A Savage Journey to the Heart of AI 40-minute session

Explainability and transparency are discussed and debated as both required and unachievable goals for AI. This talk will focus on helping teams structure discussions about levels of explainability possible and needed for both user trust and regulatory requirements.

Neil Fendley is a computer vision researcher at JHU/APL. Neil works on machine perception and reasoning, focusing on deep learning. Over the last 2 years Neil has worked on automated retinal disease diagnosis and satellite imagery classification through JHU/APL’s support of IARPA’s Functional Map of the World effort. Recently, Neil has focused on adversarial machine learning and hosted a JHU/APL internal challenge to design and defend against adversarial perturbations.

Presentations

ImageNet for Satellite Imagery: Opportunities and Risks 40-minute session

While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. To address this, we released an ImageNet for satellite imagery called functional Map of the World (fMoW). We present our work building the dataset, running a public prize challenge, and investigating how one might attack or defend these deep learning models.

Jennifer Fernick is a computer science researcher, cybersecurity executive, technical advisor, and speaker. She currently serves as Director, Information Security at Scotiabank. She is also completing a PhD in Computer Science from the University of Waterloo, where her research involves quantum algorithms, computational complexity, and cryptography. She is a member of the Institute for Quantum Computing and the Centre for Applied Cryptographic Research, and was a part of the 2018 cohort of the Berkman Assembly at Harvard University and MIT Media Lab, focussing on Artificial Intelligence & its Governance. Her career has included designing and building satellite systems, working on bleeding edge cryptography research, and leading the development of global technology standards. Most recently, she served as Senior Cryptographic Security Architect for a major multinational bank. She holds a Master of Engineering degree in Systems Design Engineering from the University of Waterloo, and an Honours Bachelor of Science in Cognitive Science & Artificial Intelligence from the University of Toronto. A highly-regarded speaker, Jennifer has spoken at major technology conferences such as RSA, Blackhat, ECML, and DEF CON.

Presentations

Executive Briefing: Quantum Machine Learning 40-minute session

Quantum computers will enable us to efficiently compute things never thought possible - how will this impact artificial intelligence? In this talk, attendees will learn to filter signal from noise in discussions surrounding quantum machine learning by exploring how quantum computers work, what types of AI problems they may be good at, and which industries and use cases will (and will not) benefit.

Lise Getoor is a professor in the Computer Science Department at the University of California, Santa Cruz and director of the UCSC Data Science Research Center. Her research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data. She has over 250 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the board of the Computing Research Association (CRA), and was co-chair for ICML 2011. She is a recipient of an NSF Career Award and twelve best paper and best student paper awards. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013.

Presentations

The Unreasonable Effectiveness of Structure 40-minute session

Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. In this talk, I will describe AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. I will describe both the benefit of utilizing structure and the inherent risk of ignoring structure.

Bruno Gonçalves is currently a Vice President in Data Science and Finance at JPMorgan Chase. Previously, we was a Data Science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since completing his PhD in the Physics of Complex Systems in 2008 he has been pursuing the use of Data Science and Machine Learning to study Human Behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading.

Presentations

Recurrent neural networks for time series analysis Tutorial

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

Josh Gordon is a developer advocate for TensorFlow at Google. He’s passionate about machine learning and computer science education. In his free time, Josh loves biking, running, and exploring the great outdoors.

Presentations

TensorFlow 2.0: Machine Learning for You 40-minute session

Learn about the very latest in TensorFlow direct from Google. We will focus on TensorFlow 2.0 and its easy-to-use eager execution. We'll also cover how to use our revised high-level API, and pitfalls and tricks to get performance on accelerator hardware.

Sean Gourley is founder and CEO of Primer. Before Primer, Sean was co-founder & CTO of Quid, an augmented-intelligence company. He holds a PhD in physics from Oxford, where his research as a Rhodes Scholar focused on complex systems and the mathematical patterns underlying modern war. He sits on the Board of Directors at Anadarko (NYSE:APC) and is a TED Fellow.

Presentations

Keynote by Sean Gourley Keynote

Keynote by Sean Primer

Anna R. Gressel is a litigation associate whose practice focuses on complex civil litigation, corporate governance and intellectual property. Ms. Gressel is a member of the firm’s Technology, Media & Telecommunications Group, and she actively advises on issues related to emerging technologies. She also maintains a pro bono practice that includes federal civil rights litigation and indigent criminal defense.

Ms. Gressel joined the firm in 2014. She received a J.D. from Harvard Law School in 2014, where she twice served as teaching assistant for Harvard Law School’s flagship Negotiation Workshop course and Harvard Negotiation Institute’s executive education programs.

Ms. Gressel received a B.A. from Pomona College. Prior to law school, Ms. Gressel was the recipient of a Fulbright Research Fellowship to Morocco.

Ms. Gressel is the co-author of “Storm Clouds or Silver Linings? Assessing the Impact of the U.S. CLOUD Act on Cross-Border Criminal Investigations,” American Bar Association, Litigation Journal (Fall 2018, forthcoming) (with Frederick T. Davis), “Key Insights from the New York AI in Finance Summit,” Debevoise & Plimpton, TMT Insights Blog (October 2018, forthcoming), “Report of the Investigation,” American Bar Association, Section of Litigation, Internal Corporate Investigations, Brad D. Brian, Barry F. McNeil and Lisa J. Demsky, eds., 4th ed. 2017 (with Edwin G. Schallert) and “Do the Apps Have Ears? Cross-Device Tracking,” Bloomberg BNA, Privacy & Security Law Report, 15 PVLR 1421, (7 November, 2016) (with Jeremy Feigelson).

Ms. Gressel is a member of the Bar of New York. She is also admitted to practice before the U.S. Courts of Appeals for the Second and Third Circuits and the U.S. District Courts for the Southern and Eastern Districts of New York. Ms. Gressel is a member of the Harvard Law School Women’s Alliance, the Federal Bar Council and the Association of the Bar of the City of New York.

Presentations

Executive Briefing: The Regulatory Road Ahead - How to Navigate the Legal Trends Driving AI in 2019 40-minute session

This is a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and California Consumer Privacy Act. It will also explore key lawsuits challenging AI in U.S. courts - and unpack implications for companies going forward. By understanding these trends, companies can more effectively mitigate legal and regulatory risks and position their AI products for success.

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

Presentations

Bringing AI into the enterprise Tutorial

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

Dr. Behrooz Hashemian is senior machine learning scientist at the MGH & BWH Center for Clinical Data Science, where he is responsible for developing and implementing state-of-the-art machine learning models to address various clinical use cases by leveraging medical imaging data, clinical time-series data and electronic health records. Prior to CCDS, Dr. Hashemian worked at MIT, Senseable City Lab, as Chief Data Officer with focus on innovative implementation of big data analytics and artificial intelligence in smart cities.

Presentations

Interpretable Deep Learning in Healthcare 40-minute session

Artificial Intelligence has shown great potentials to revolutionize clinical medicine and health care delivery. However, incorporating these algorithms into clinical workflows faces a big challenge: convincing clinicians and regulators to trust a “black box” solution. In this talk, I present how we are making deep neural networks interpretable to provide evidences for clinical decisions.

Qais received a dual Ph.D. degrees in operation research and industrial engineering from Pennsylvania State University/University Park in August 2015. In his role as computer scientist/statistician at FDA, he conducts research in statistical/operational modeling and computer science at Center of Drug Evaluation and Research (CDER)/ Office of Translational Science (OTS)/ Office of Computational Science (OCS) in the U.S. Food and Drug Administration (FDA). Specifically, he applies advanced statistical modeling and scientific computing techniques to computationally intensive tasks that are encountered in regulatory and scientific applications. For this purpose, he utilizes various statistical and operations research methodologies such as machine learning and data mining algorithms, natural language processing (NLP) techniques, Neural Networks procedures, and text analytics to extract meaning, patterns and hidden structures in structured and unstructured data; identifying the most feasible approaches to software/networking system design and development problems; consulting reviewers, fellow scientists, and regulations to analyze problems and recommend technology based solutions. He also prepares reports and manuscripts based on research findings and will present at scientific meetings as necessary. Moreover, he is an active member in several working groups across FDA such as the Modeling and Simulation Workgroup, INFORMED and HIVE.

Presentations

Utilizing Rule-Based Text Extraction with Deep Learning Models for FDA Pharmacovigilance 40-minute session

Drug adverse event narratives contain a wealth of information that is laborious to assess using manual methods. To improve FDA Pharmacovigilance, we apply rule-based text extraction to generate training data for deep learning models. These models improve the identification of adverse events from narrative data, enhance time-to-value, and refine sources of medical terminology.

Vishal (‘Vish’) Hawa is Principal Data Scientist at Vanguard. Vish has over 15 years of experience in Retail and Financial services industry and works closely with Marketing Managers in designing attribution, propensity and attrition modeling.

Vish has executive management from Wharton school of business, post-graduation degrees in Information sciences, Statistics and computer engineering from Indian Statistical Institute.

Presentations

Regularization of RNN through Bayesian Networks 40-minute session

While Deep Learning has shown significant promise towards model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and over-fit . The presentation explains how a combination of RNNs and Bayesian Network (PGM) can improvise sequence-modeling behavior of RNNs.

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

Presentations

Using Deep Learning to Create Interactive Digital Actors 40-minute session

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

Michael Hind is a Distinguished Research Staff Member in the IBM Research AI organization in Yorktown Heights, New York. His current research passion is in the general of area of Trusted AI, focusing on the fairness, explainability, and reliability of the construction of AI systems.

Previously, he led departments of dozens of researchers focusing on programming languages, software engineering, cloud computing, and tools for cognitive systems. Michael’s team has successfully transferred technology to various parts of IBM and launched several successful open source projects. After receiving his Ph.D. from NYU in 1991, Michael spent 7 years as an assistant/associate professor of computer science at SUNY – New Paltz.

Michael is an ACM Distinguished Scientist, and a member of IBM’s Academy of Technology, a former Associate Editor of ACM TACO, has served on over 30 program committees, given talks at top universities and conferences, and co-authored over 40 publications. His 2000 paper on Adaptive Optimization was recognized as the OOPSLA’00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012.

Presentations

Introducing the AI Fairness 360 Toolkit Tutorial

Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias.

As Director, Market Enablement at WorkFusion, Kyle is responsible for partner enablement, sales enablement, and software demonstration, working within the Product team but closely tied to the Sales, Marketing, and Professional Services teams. With nearly 15 years of consulting and software experience, he is passionate about helping organizations efficiently collect and utilize the data, information, and knowledge of their organizations. Prior to WorkFusion, Kyle was a consultant with AT Kearney and Booz Allen Hamilton. He holds an MSc in Information Systems from the London School of Economics and a BS in Computer Science from the University of Nebraska at Omaha.

Presentations

Fighting Financial Crime with AI: Beyond Fraud Detection with AI-powered RPA 40-minute session

Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant Anti-Money Laundering (AML) and Know-Your-Customer (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. This session will walk-through a series of case studies that utilize AI-powered RPA that address AML and KYC.

Ana Hocevar obtained her PhD in Physics before becoming a postdoctoral fellow at the Rockefeller University where she worked on developing and implementing an underwater touchscreen for dolphins. She has over 10 years of experience in physics and neuroscience research and over 5 years of teaching experience. Now she combines her love for coding and teaching as a Data Scientist in Residence at The Data Incubator.

Presentations

Deep Learning with PyTorch 2-Day Training

PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy to use API and seamless use of GPUs make it a sought-after tool for deep learning. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets.

Deep Learning with PyTorch (Day 2) Training Day 2

PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy to use API and seamless use of GPUs make it a sought-after tool for deep learning. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets.

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

Presentations

Deep learning methods for natural language processing Tutorial

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

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

Presentations

Natural Language Code Search for GitHub Using Kubeflow 40-minute session

In this talk, we will use the example of a search engine for code using natural language to illustrate how Kubeflow and Kubernetes can be used to build and deploy ML products.

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

Presentations

Distributed TensorFlow with Distribution Strategies 40-minute session

Session description: This session covers how to use TensorFlow effectively in a distributed manner using best-practices. We will cover using TensorFlow's new DistributionStrategy to get easy high-performance training with Keras models (and custom models) on multi-GPU setups as well as multi-node training on clusters with accelerators.

Humayun Irshad is a lead scientist in Machine Learning & Computer Vision at Figure-Eight. He is developing machine learning, more specifically deep learning frameworks for various applications like object detection, segmentation and classification in fields ranging from medical, retail, self-driving car, etc. He has 3 years PostDoc experience at Harvard Medical School where he developed machine learning and deep learning frameworks include region of interest detection and classification, nuclei and gland detection, segmentation and classification in 2D and 3D medical images.

Presentations

An Active Learning Framework to Optimize Training of Deep Model with Human-in-the-loop 40-minute session

In this talk, an active learning framework with crowd sourcing approach is introduced to solve a real-world problem in transportation and autonomous driving discipline, parking sign recognition, for which a large amount of unlabeled data is available. It generates an accurate model in a cost-effective and fast way to solve the parking sign recognition problem in spite of the unevenness of the data

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

Presentations

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

Word embeddings such as word2vec have revolutionized language modelling. In this talk I will discuss exponential family embeddings, which apply probabilistic embedding models to other data types. I describe how we implemented a dynamic embedding model to understand how tech skill-sets have changed over 3 years. The key takeaway is that these models can enrich analysis of specialized datasets.

Swara Kantaria is a Senior Product Manager at BuzzFeed, a media company that publishes all the trending content people will want to share. Since joining the company, she’s worked on a range of products from launching buzzfeed.com in international market to building new internal tools. Most recently, Swara is the Product Lead for Distribution Tools, a team that team is responsible for building products that send all of our content to social media platforms (like Facebook, YouTube, Twitter and Instagram), OTT destinations (like Roku and Pluto) and to syndication partners. Prior to joining BuzzFeed, Swara was a technology consultant at Deloitte.

Presentations

Media Meets AI: How We Give Superpowers to BuzzFeed's Social Curators 40-minute session

As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. To this end, we first built publishing tools that let people work more efficiently. Now, we build artificial intelligence tools that let people work more intelligently. During this talk we plan to share this evolution with the audience.

Anoop Katti is a Data Scientist in the Deep Learning center at SAP. He did his bachelor studies at BIT, Bangalore. After a 1-year experience in building telecom software at Huawei, he pursued a research-based master’s in computer Vision at IIT Madras. During his time at SAP, he has extensively worked on documents with strong 2D structure where he has amalgamated his prior experience in Computer Vision with techniques from Natural Language Processing. Anoop has acquired multiple patents and publications in the field.

Presentations

Chargrid: Understanding 2D documents 40-minute session

We address understanding documents with 2D layout using machine learning. Examples of such documents are invoices, resumes, presentations etc. (in contrast to plain text documents like tweets, articles and reviews). We explore the shortcomings of the existing techniques and discuss a processing pipeline for 2D documents – the chargrid - pioneered by data scientists at SAP

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

Presentations

Deep Learning for Time Series Data 40-minute session

In this talk we shall shares a novel two-step approach for building more reliable prediction models by integrating anomalies in them. Further, we shall walk the audience through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data.

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

Presentations

Industrialized Capsule Networks for Text Analytics 40-minute session

We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics. 2. We show an implementation of recurrent capsule networks. 3. We also benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.

Marcel Kurovski is a Data Scientist at inovex, a German IT project house focusing on digital transformation. He earned a master’s degree in Industrial Engineering and Management from the Karlsruhe Institute of Technology (KIT) where he focused on computer science, machine learning and operations research.

Marcel works on novel methods to exploit deep learning for recommender systems in order to better personalize content and improve user experience. He works for clients in e-commerce and retail where he bridges the gap between proof-of-concept and scalable AI systems. His research spans recommender systems, deep learning as well as methods for approximate nearest neighbor search.

Presentations

Deep Learning for Recommender Systems and How to Compare Pears with Apples 40-minute session

Recommender Systems support decision making with personalized suggestions. They have proven useful in e-commerce, entertainment, or social networks. However, sparse data and linear models are a burden. Application of Deep Learning sets new boundaries and constitutes remarkable results. This talk shows its application on vehicle recommendations at Germany's biggest online vehicle market.

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

Presentations

Learning from Multi-Agent, Emergent Behaviors in a Simulated Environment 40-minute session

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

Francesca Lazzeri, PhD is AI & Machine Learning Scientist at Microsoft in the Cloud Developer Advocacy team. Francesca is passionate about innovations in big data technologies and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers a wide range of industries including energy, oil and gas, retail, aerospace, healthcare, and professional services.

Before joining Microsoft, she was Research Fellow in Business Economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit. At Harvard Business School, she worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.

Francesca holds a PhD in Economics & Management from Sant’Anna School of Advanced Studies and is currently Data Science Mentor for PhD and Postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding.

Social links:

Website: https://developer.microsoft.com/en-us/advocates/francesca-lazzeri

Presentations

Forecasting Financial Time Series with Deep Learning on Azure 2-Day Training

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

Forecasting Financial Time Series with Deep Learning on Azure (Day 2) Training Day 2

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

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

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you'll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters.

After I graduated Johns Hopkins University, double-majoring Applied Math and Statistics with Computer Science, I joined Financial Investment company in South Korea working as a Quant Data Analyst. Last year, I moved to here as a Data Scientist at LINE Corp. Our team, Company.AI was acquired by NAVER & LINE Corp in July 2017. We focus on improving performance of Dialogue Model and building better framework to lead customers to easily build and serve chatbot to their own business.

Presentations

AutoML in Chatbot Builder Framework 40-minute session

"Until when are you going to cluster queries by yourself to manage large data corpus?" "Until when are you going to tune model hyper parameters by yourself?" I would like to introduce how to implement self-trained dialogue model by using AutoML in Chatbot within our Chatbot Builder Framework.

Nicholas Leonard graduated from the Royal Military College of Canada in 2008. He obtained an MS in computer science from University of Montreal in 2014. Nicholas is a software engineer at Twitter Cortex. He was a core contributor to Lua Torch, and currently works with TensorFlow as part of the DeepBird team.

Presentations

Unifying Twitter Around a Single ML Platform 40-minute session

Twitter is a 4000+ employee company with many ML use cases. Historically, there are many different ways to productionize ML at Twitter. In this session, we describe the setup and benefits of a unified ML platform for production, and how Twitter Cortex team brings together users of various ML tools.

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

Presentations

Natural Language Code Search for GitHub Using Kubeflow 40-minute session

In this talk, we will use the example of a search engine for code using natural language to illustrate how Kubeflow and Kubernetes can be used to build and deploy ML products.

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

Presentations

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

There is a significant interest in applying deep learning based solutions to problems in medicine and healthcare. This talk will focus on identifying actionable medical problems, and then recasting them as tractable deep learning problems and the techniques to solve them.

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

Presentations

Opening Remarks Keynote

Opening Remarks

Opening Remarks Keynote

Opening Remarks

Tom Marlow is the CTO for Black Hills IP, where he works tirelessly to drive IP legal services into that age of AI and automation. Relying on data wherever it can be found Tom and the Black Hills team build products to power internal staff and legal professional customers. Tom comes from a background of technology and analytics. Being exposed to massive amounts of patent data very early in his career, he learned techniques to quickly build data sets and evaluate the results to drive strategy. This work led to a corporate leadership position driving global IP operation and strategy for the renowned Fairchild Semiconductor Corporation. In addition to maintaining a portfolio of several thousand patents, Tom managed patent development, litigation, licensing and acquisition across the US, Europe and Asia. Around this time, Tom co-authored a desk reference for patent attorneys which provided an indexed analysis of appeals decisions for use in prosecuting patent applications (now in its 7th edition).

Tom is a registered patent attorney and electrical engineer with a passion for IP systems and previously served as co-chair of the patent analytics and portfolio management department at the Minneapolis patent firm Schwegman, Lundberg & Woessner, P.A. Tom has advised companies from startups to household name multinationals on IP strategy, operations and policy.

Tom has spoken before a diverse audiences from patent attorneys, to C-suite executives to engineers to startup founders on patent management, analysis, and strategy over the years. He received his law degree from Franklin Pierce Law Center, and Bachelors of Science from the University of Notre Dame.

Presentations

Executive Briefing: The Hidden Data in AI IP 40-minute session

Three elements will control the AI market - Technology, Data and IP Rights. Leveraging rich patent data, we will uncover the companies with the top patent holdings across the world in groundbreaking research and implementation technologies, gaining insights into the sources and owners of AI technology as well as the hurdles and opportunities that lay in front of those entering the field today.

Alina Matyukhina is a cyber security researcher and 3rd-year PhD candidate at Canadian Institute for Cybersecurity (CIC). Her research work focuses on applying machine learning, computational intelligence, and data analysis techniques to design innovative security solutions. Before joining CIC, she worked as a research assistant at Swiss Federal Institute of Technology where she took part in cryptography and security research projects. Both her B.S. and M.S. was completed in Math and IT. Alina is a member of the Association for Computing Machinery, the IEEE Computer Society. Alina is presenting her research at several security and software engineering conferences including HackFest, IdentityNorth, ISACA Security & Risk, Droidcon SF, and PyCon Canada.

Presentations

Adversarial machine learning in digital forensics 40-minute session

Machine learning models are often susceptible to adversarial deception of their input at test time, which is leading to a poorer performance. In this session we will investigate the feasibility of deception in source code attribution techniques in real world environment. This session will present attack scenarios on users identity in open-source projects and discuss possible protection methods.

Fernando Maymí, Ph.D., CISSP, is Lead Scientist in the Cyber and Secure Autonomy Division of Soar Technology, Inc., an artificial intelligence research and development company where he leads multiple advanced research projects developing autonomous cyberspace agents for the Department of Defense. Dr. Maymi is a retired Army Officer with more than 25 years of service; he was the first Deputy Director of the Army Cyber Institute at West Point, an organization he helped grow and lead from its inception, and a former West Point faculty member. Dr. Maymí holds three patents and regularly consults on cybersecurity issues both in the U.S. and abroad. He is the author of numerous publications including the best-selling CISSP All-in-One Exam Guide.

Presentations

Building Synthetic Cyberspace Attackers 40-minute session

This talk describes the development of an autonomous cyberspace agent aimed at providing cost-effective, realistic penetration testing for the DoD. We will discuss current gaps as well as future threats and opportunities ranging from the need for scalable cyberspace mapping techniques to our work in modeling adversarial behaviors to the lessons learned in human-machine teaming.

Leah McGuire is a Principal Member of Technical Staff at Salesforce Einstein, building platforms to enable the integration of machine learning into Salesforce products. Before joining Salesforce, Leah was a Senior Data Scientist on the data products team at LinkedIn working on personalization, entity resolution, and relevance for a variety of LinkedIn data products. She completed a PhD and a Postdoctoral Fellowship in Computational Neuroscience at the University of California, San Francisco, and at University of California, Berkeley, where she studied the neural encoding and integration of sensory signals.

Presentations

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

A problem in predictive modeling data is label leakage. At Enterprise companies such as Salesforce, this problem takes on monstrous proportions as the data is populated by diverse business processes, making it hard to distinguish cause from effect. We will describe how we tackled this problem at Salesforce, where we need to churn out thousands of customer-specific models for any given use case.

Ming-Wei Chang is a research scientist in Google AI Language, Seattle. He enjoys developing interesting machine learning algorithms for practical problems, especially in the field of natural language processing. He has won an Outstanding Paper award at ACL 2015 for his work on question answering over knowledge bases. Over the years, he has published more than 35 papers on the top-tier conferences and won several international machine learning competitions including entity linking, power load forecast prediction and sequential data classification. Together with his colleagues in Google AI Language, his recent paper, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding“, has demonstrated the power of language model pre-training and obtain the new state-of-the-art over 11 natural language processing tasks.

Presentations

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 40-minute session

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.

Vinay Seth Mohta is a managing director at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to Manifold, Vinay was a product manager at KAYAK where he worked with both Hadoop and Hive to develop a robust view of customers. He also developed a predictive model for flight pricing. Before that, Vinay was an architect at Endeca Technologies, where he worked in the engineering team that developed new data structures and indexing technologies to enable search and faceted navigation. He is a co-inventor on several granted patents for search and faceted navigation. Vinay received his Bachelors of Science and Masters in Engineering from the Massachusetts Insitute of Technology (MIT).

Presentations

Executive Briefing: Five Key Questions to Kick Off Your AI Implementation 40-minute session

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

Cibele is a Machine Learning Engineer at Twitter Cortex, where she helps to build Twitter’s deep learning platform. Prior to working at Twitter, Cibele worked at Apple as a Data Scientist and Systems Design Engineer; and at Analog Devices as Product Applications Engineer . At Analog Devices, she worked on building machine learning algorithms that use smartphone sensors to understand a person’s behavior. Cibele obtained her B.S. from Stanford University in Electrical Engineering and Physics and her M.S. from the California Institute of Technology in Electrical Engineering with an emphasis in Computer Vision and Machine Learning.

Presentations

ML at Twitter: Deep dive into Twitter's Timeline 40-minute session

Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient.

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

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

Ryan Mukherjee is a senior research engineer at JHU/APL. Ryan has been involved in machine learning, computer vision, and remote sensing projects for over 9 years within JHU/APL’s Research and Exploratory Development Department. Most recently, Ryan led JHU/APL’s support of IARPA’s Functional Map of the World effort and is focused on providing easy and free access to associated tools and data.

Presentations

ImageNet for Satellite Imagery: Opportunities and Risks 40-minute session

While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. To address this, we released an ImageNet for satellite imagery called functional Map of the World (fMoW). We present our work building the dataset, running a public prize challenge, and investigating how one might attack or defend these deep learning models.

Karthikeyan Natesan Ramamurthy is a Research Staff Member in IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY. He received his PhD in Electrical Engineering from Arizona State University. His broad research interests are in understanding the geometry and topology of high-dimensional data and developing theory and methods for efficiently modeling the data. He has also been intrigued by the interplay between humans, machines, and data, and the societal implications of machine learning. His papers have won best paper awards at the 2015 IEEE International Conference on Data Science and Advanced Analytics and the 2015 SIAM International Conference on Data Mining. He is an associate editor of the Digital Signal Processing journal and a member of IEEE.

Presentations

Introducing the AI Fairness 360 Toolkit Tutorial

Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias.

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

Presentations

Executive Briefing: Overview of Data Governance 40-minute session

Data governance is an almost overwhelming topic. This talk surveys history, themes, plus a survey of tools, process, standards, etc. Mistakes imply data quality issues, lack of availability, and other risks that prevent leveraging data. OTOH, compliance issues aim to preventing risks of leveraging data inappropriately. Ultimately, risk management plays the "thin edge of the wedge" in enterprise.

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

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

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

Presentations

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

There is a significant interest in applying deep learning based solutions to problems in medicine and healthcare. This talk will focus on identifying actionable medical problems, and then recasting them as tractable deep learning problems and the techniques to solve them.

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

Presentations

Designing a Machine Learning Operating Platform 40-minute session

After investments in collecting & cleaning data, and building Machine Learning models Enterprises discover the operational challenges in deploying models to production and managing their portfolio of ML models. This talk will cover the strategic and technical hurdles each company must overcome and the best practices we've developed while deploying over 5,000 ML models for nearly 80,000 engineers.

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

Presentations

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

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

Jim Pastore is a litigation partner and a member of the firm’s Cybersecurity & Data Privacy practice and Intellectual Property Litigation Group. The Legal 500 recognizes Mr. Pastore for both his intellectual property and cybersecurity and data privacy work, describing him as a “skilled litigator” who is “highly accomplished.” Chambers USA 2018 recognizes Mr. Pastore as a leading lawyer for Privacy and Data Security, where sources explain “he knows the technical side of matters and is great at interfacing when there is federal involvement." Named as a Cybersecurity Trailblazer by The National Law Journal, Mr. Pastore has also twice been named to Cybersecurity Docket’s “Incident Response 30,” a collection of 30 of the “best and brightest” incident response attorneys in the country. Benchmark Litigation named Mr. Pastore to its Under 40 Hot List and Law 360 named him a “Rising Star” for his cybersecurity work.

Mr. Pastore has assisted a broad range of clients in cybersecurity and data privacy matters, including The Home Depot (in connection with its 2014 data breach); PayPal (in connection with a 2017 data security incident at its subsidiary, TIO Networks); American Express; KKR; and the NBA, among others.

From 2009 to 2014, he served as an Assistant United States Attorney in the Criminal Division of the Southern District of New York, where he was assigned to the Complex Frauds Unit and Computer Hacking and Intellectual Property Section. He successfully litigated eight jury trials to verdict and was the lead prosecutor in United States v. Monsegur, a/k/a “Sabu,” and Operation Cardshop, both of which were named to the FBI’s top 10 cases of 2012. Mr. Pastore also led Operation Dirty R.A.T., which targeted the creators and users of Blackshades ransom and malware, resulting in the largest ever worldwide law enforcement action against cybercriminals. In connection with the so-called “doomsday virus,” Mr. Pastore obtained a unique order to prevent catastrophic Internet outage.

Prior to 2009, Mr. Pastore was an associate at Debevoise, working on a variety of high-profile intellectual property matters, including the well-publicized Google books copyright litigation.

Mr. Pastore is routinely sought out as a speaker on cybersecurity and data privacy, having been invited to present to the Department of Justice’s National Cyber Security Division, the DOJ’s National Advocacy Center, Georgetown Law’s Cybersecurity Law Institute, the FBI-led International Conference on Cyber Security, the annual meeting of the Association of Life Insurance Counsel (ALIC), and the Fiduciary & Investment Risk Management Association (FIRMA)’s National Risk Management Training Conference, as well as to the boards of multiple public companies.
Mr. Pastore’s publications include “Cybersecurity: Evaluating Transactional Risk,” Transaction Advisors (July, 2015); “A Closer Look,” Best’s Review (June, 2015); “New York State Department Of Financial Services Expands Its Cyber Focus To Insurers,” FC&S Legal (April, 2015); and “Debevoise & Plimpton On Cybersecurity: Reducing Threats To Private Equity Firms And Their Portfolio Companies,” The Newsletter of the Private Equity Growth Capital Council (March, 2015).

Mr. Pastore earned his J.D., with distinction, from Stanford Law School in 2004. He served as Co-President of the Stanford Law & Technology Association and was a member of the Stanford Technology Law Review. He received his B.A., summa cum laude and Phi Beta Kappa, from the University of Notre Dame in 2001, where he was a Notre Dame Scholar, the recipient of the James E. Robinson Award for outstanding senior English major and one of 40 class members of the Honors Program of the College of Arts & Letters.

Presentations

Executive Briefing: The Regulatory Road Ahead - How to Navigate the Legal Trends Driving AI in 2019 40-minute session

This is a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and California Consumer Privacy Act. It will also explore key lawsuits challenging AI in U.S. courts - and unpack implications for companies going forward. By understanding these trends, companies can more effectively mitigate legal and regulatory risks and position their AI products for success.

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

Presentations

Getting started with PyTorch Tutorial

From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications.

Anwesa Paul is Chief Global Privacy Counsel at American Express and advises all parts of the business with legal questions relating to U.S. and Canadian financial privacy laws, marketing privacy laws, online and mobile privacy self-regulatory guidelines, big data governance and big data breaches. Before joining American Express, Ms. Paul served as in-house counsel at two online advertising technology startups, Kinetic Social and Epic Media Group. Previously, Ms. Paul worked in the privacy space at the New York State Attorney General’s Office Internet Bureau, handling consumer protection issues related to online marketing.

Presentations

Executive Briefing: The Regulatory Road Ahead - How to Navigate the Legal Trends Driving AI in 2019 40-minute session

This is a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and California Consumer Privacy Act. It will also explore key lawsuits challenging AI in U.S. courts - and unpack implications for companies going forward. By understanding these trends, companies can more effectively mitigate legal and regulatory risks and position their AI products for success.

Justina has a background in Econometrics and Data Analytics. Her curiosity for Data Science and human behaviour analytics has taken her to many places and industries – over the past three years she has been doing Data Science work across video gaming, fintech, insurance industries. Her interest in chatbots, natural language processing and open source has led her to Rasa, a Berlin-based conversational AI startup where she works as a Developer Advocate focusing on improving developer experience in using open source software for conversational AI.

Presentations

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

In this workshop, you will get hands-on experience in developing intelligent AI assistants based entirely on machine learning and using only open source tools - Rasa NLU and Rasa Core. You will learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data.

A data scientist from Silicon Valley with Ph.D. in Computer Science. Ex-data scientist at Microsoft.

Now Co-founder and CEO of Iterative AI startup in San Francisco. We create tools for machine learning and data versioning.

Presentations

Open source tools for machine learning models and data sets versioning 40-minute session

Many companies are using machine learning today, ML teams size is growing and complexity of ML project is increasing. Establishing a well define and manageable process become a central issue in this environment. ML models and data set versioning is an essential first step in the direction of establishing a good process. We will discuss open source tools and practices for ML models versioning.

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

Presentations

Design and Empirical Evaluation of Interactive and Interpretable Machine Learning 40-minute session

Human needs are constantly overlooked in AI, which leads to a gap between model developers and user expectations. I will demonstrate a system that bridges this gap by brings humans and models together for collaboration. I will also propose a template for examining the effects of model interpretability on human behavior to improve user experience and trust.

Anand Rao is a partner in PwC’s Advisory practice and is the Global AI Lead and the competency lead for the Analytics practice in US. He leads the design and deployment of artificial intelligence and other advanced analytical techniques and decision support systems for clients, including natural language processing, text mining, social listening, speech and video analytics, machine learning, deep learning, intelligent agents, and simulation. Anand is responsible for research and commercial relationships with academic institutions and startups.

Previously, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute; program director for the Center of Intelligent Decision Systems at the University of Melbourne, Australia; and a student fellow at IBM’s T.J. Watson Research Center. He has held a number of board positions at startups.

Anand has coedited four books and published over 50 papers in refereed journals and conferences. He was awarded the most influential paper award for the decade in 2007 from Autonomous Agents and Multi-Agent Systems (AAMAS) for his work on intelligent agents. He is a frequent speaker on AI, behavioral economics, autonomous cars and their impact, analytics, and technology topics in academic and trade forums.

Anand was recently selected as one of the Top 100 innovators of Data and Analytics and also in the Top 50 Data and Analytics professionals in US and Canada by Corinium. His recent paper on Strategist’s Guide to AI won the 2017 Azbee Award for Best Paper.

Anand holds an MSc in computer science from Birla Institute of Technology and Science in India, a PhD in artificial intelligence from the University of Sydney, where he was awarded the university postgraduate research award, and an MBA with distinction from Melbourne Business School.

Presentations

Executive Briefing: Responsible AI - Approach & Case Studies in building Fair, Interpretable, Safe AI 40-minute session

Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. This talk synthesizes the current research in FAT (Fairness, Accountability, Transparency) into a step-by-step methodology to address these issues. Case studies from financial services and healthcare are used to illustrate the approach.

Delip Rao is the founder of R7 Speech Science, a San Francisco-based company focused on building innovative products on spoken conversations. Previously, Delip was the founder of Joostware, which specialized in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning from O’Reilly.

Presentations

Natural language processing with deep learning 2-Day Training

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

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

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

Reardon received his PhD in Neuroscience from Columbia University, was CEO and Co-Founder of Avogadro, Inc., acquired by Openwave where he served as CTO, and creator of the Internet Explorer project at Microsoft. He’s a founding Board Member of W3C, has 7 patents, and was an MIT “Top 35 Young Innovator” in 2004.

Presentations

Sooner Than You Think: Neural Interfaces Are Finally Here 40-minute session

Following the launch of CTRL-labs’ developer kit, CTRL-kit (neural interface device), CEO Thomas Reardon will paint a picture of a world with neural interfaces and how this technology will change our lives. This presentation will outline a future where we will be looking up at the world instead of down at our phones and will feature a live demo.

Mishael is a Software Engineer at Twitter Timelines Quality team, focusing on relevance-based timelines ranking. Since 2011 Mishael has been utilizing Machine Learning to tackle real-world problems in various domains, such as hardware verification and chat agent allocation. He also has vast experience developing data science tools, pipelines and infrastructure. Mishael obtained his BSc in Mathematics and Computer Science and MSc in Theoretical Computer Science from the Hebrew University of Jerusalem.

Presentations

ML at Twitter: Deep dive into Twitter's Timeline 40-minute session

Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient.

Tom Sabo is a Principal Solutions Architect with SAS who, since 2005, has been immersed in text analytics and artificial intelligence applied to federal government challenges. He presents work internationally on diverse topics including modeling applied to government procurement, strategies to counter human trafficking, and using analytics to leverage and predict research trends. Sabo also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS/OHA on social media strategies. He has a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.

Presentations

Utilizing Rule-Based Text Extraction with Deep Learning Models for FDA Pharmacovigilance 40-minute session

Drug adverse event narratives contain a wealth of information that is laborious to assess using manual methods. To improve FDA Pharmacovigilance, we apply rule-based text extraction to generate training data for deep learning models. These models improve the identification of adverse events from narrative data, enhance time-to-value, and refine sources of medical terminology.

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

Presentations

Deploying Deep Learning Models on GPU Enabled Kubernetes Clusters 40-minute session

Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? This session will help you by providing a step-by-step guide to go from a pre-trained deep learning model, package it in a Docker container and deploy as a webservice on Kubernetes cluster.

Tim Schwuchow is a Data Scientist in Residence at The Data Incubator. Prior to joining The Data Incubator, he earned degrees in economics from Harvard and Duke. Professionally, he has designed and instructed several undergraduate- and graduate-level courses at Duke, worked as an analyst at D. E. Shaw & Co., a quantitative hedge fund, and worked as an economist for the Postal Regulatory Commission.

Presentations

AI for Managers 2-Day Training

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

AI for Managers (Day 2) Training Day 2

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Kabir Seth is currently the Director of Operations for the Wall Street Journal Product, Design and Engineering lead and the co-lead of the AI Center of Excellence at Dow Jones. He has worked in a variety of industries including Apparel, Travel and Children’s media. In his free time, he enjoys spending time with his family watching movies, playing with legos and writing fiction.

Presentations

Leveraging AI in a Large Organization Tutorial

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

MS in CS. Former team lead for open-source project sedna.org. Co-founded a company The Tweeted Times that was acquired by Yandex in 2011. Recently have been working on tools for data scientists at Iterative.ai as a CTO.

Presentations

Open source tools for machine learning models and data sets versioning 40-minute session

Many companies are using machine learning today, ML teams size is growing and complexity of ML project is increasing. Establishing a well define and manageable process become a central issue in this environment. ML models and data set versioning is an essential first step in the direction of establishing a good process. We will discuss open source tools and practices for ML models versioning.

While his focus today is Artificial Intelligence, Alex’s expertise is in successfully managing projects, products and people. In studying and perfecting the constants – those skills and techniques necessary to design, develop and deploy revenue-driving technologies across place and time. He currently serves as an AI Technical Program Manager at Dow Jones and co-lead of the Dow Jones AI Center of Excellence.

Presentations

Leveraging AI in a Large Organization Tutorial

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

Evan Sparks is cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from UC Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

Presentations

Risk-free Deep Learning without Sacrificing Performance 40-minute session

Building deep learning applications is hard. Building them repeatably is harder. Maintaining high computational performance during a repeatable deep learning development process is borderline impossible. We describe the key pitfalls associated with fast, repeatable, model development, and what practitioners can do to avoid these and maintain a super-charged AI development workflow.

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

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

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

Executive Briefing: What you must know to build AI systems that understand natural language 40-minute session

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.

Ameet Talwalkar is co-founder and chief scientist at Determined AI and an assistant professor in the Machine Learning Department at Carnegie Mellon University. His primary interests are in the field of statistical machine learning, including problems at the intersection of systems and learning. He helped to create the SysML conference, led the initial development of the MLlib project in Apache Spark, is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press), and teaches an award-winning MOOC called Distributed Machine Learning with Apache Spark (edX).

Presentations

Massively Parallel Hyperparameter Tuning 40-minute session

Hyperparameter tuning is a crucial, yet expensive, component of the ML development lifecycle. Given the growing costs of model training, we would like to leverage parallelism to tune models in roughly the same wall-clock time needed to train a single model. We propose an elegant solution to this problem, and present extensive experimental results supporting the effectiveness of our approach.

Jeff Thompson is an artist, programmer, and educator based in the NYC area. His work explores collaboration with, empathy for, and the poetics of computers and technological systems. Through code, sculpture, sound, and performance, Thompson’s work uses conceptual processes like remix, translation, and visualization to physicalize and give materiality to otherwise invisible processes. He is Assistant Professor and Program Director of Visual Art & Technology at Stevens Institute of Technology, where he is also the coordinator of the Arts & Music research cluster at the Institute for Artificial Intelligence. This fall, Thompson is a Visiting Fellow at King’s College and artist-in-residence at the Computer Laboratory, both at University of Cambridge.

Presentations

Artists and Supercomputers: Creative Collaborations in AI 40-minute session

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

Wee Hyong Tok is a principal data science manager with the AI CTO office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.

Presentations

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

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you'll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters.

Pamela Vagata is the AI tech-lead at Stripe, focusing on building deep-learning models. Before joining Stripe, she was a member of Facebook AI Research, developed FBLearner Flow, Facebook’s production ML infrastructure and spent time building data infrastructure.

Presentations

Fraud Detection without Feature Engineering 40-minute session

Explore how Stripe applies deep-learning to user-behavior for fraud detection. This deep-dive will include data-preparation, modeling methods and performance comparisons.

Deepashri Varadharajan received her undergraduate degree in electronics & communications engineering at Vellore Institute of Technology in India. She later studied journalism, and received a Master’s degree from the Columbia University Graduate School of Journalism.

Deepashri has worked for organizations including Al Jazeera America, Deccan Herald, and interned at Siemens India. At CB Insights, she analyzes AI and its intersection with different industries.

Presentations

Executive Briefing: New Business Models In the Age Of Artificial Intelligence 40-minute session

At CB Insights, we track over 3,000 AI startups across 25+ verticals. While every vertical has benefited from deep learning and better hardware processing, the bottlenecks and opportunities are unique to each sector. We will explore what is driving AI applications in different verticals like healthcare, retail, and security, and analyze emerging business models.

Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.

Dr. Varshney is a research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the Learning and Decision Making group. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences.

Presentations

Introducing the AI Fairness 360 Toolkit Tutorial

Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias.

Augusto Vega is a Research Staff Member at IBM T. J. Watson Research Center. He holds a Ph.D in Computer Architecture from UPC Barcelona in Spain. He is a lead contributor to the swarm-AI project at IBM Research – with specific interests in the self-driving car application space.

Presentations

Towards Self-Aware Resilent Systems and Ethical Artifical Intelligence 40-minute session

We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed.

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

Presentations

Building Intelligent Conversational Agents with Transfer Learning and Cognitive Automation 40-minute session

The session describes the innovative application of Deep Learning to power Cognitive Conversational Agents. By leveraging Transfer Learning and deep pre-trained models for NLP, we show how Chatbots can overcome limitations of limited training datasets. In addition, we will demonstrate how Machine Learning can advances Robotic Process Automation (RPA) from “robotic” to “cognitive” automation

Maja Vukovic is a Research Manager and a Research Staff Member at IBM T.J. Watson Research Center. Maja’s research expertise is in IT service innovation, AI planning, crowdsourcing technologies, API ecosystems innovation and social media applications for disaster management. Maja leads Cognitive Service Management team, with focus on AI driven insights and automation in hybrid cloud systems.

Maja has received numerous IBM Outstanding Technical Achievement Awards and IBM Research awards for her technical leadership. Maja is an IBM Master Inventor, with over 160 patents filed and 50 granted. Maja is an author of over 90 papers in top international conferences and journals. Maja is a co-founder of a number of workshops: Enterprise Crowdsourcing, Ubiquitous Crowdsourcing and Social Web for Disaster Management, collocated with leading international conferences.

Maja received her PhD from University of Cambridge, UK, for her work on context aware service composition using AI planning. Maja received her MSc from International University in Germany, and her BSc from University of Auckland, New Zealand. Prior to IBM, Maja was a Research Scientist at MercedesBenz Research and Technology Center in Palo Alto, working in the field of telematic services.

Maja is a Member of IBM Academy of Technology.

Maja is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE).

Maja is a IEEE TCSVC Award Winner: Women in Services Computing 2018.

Presentations

Towards Automated AI Planning in Enterprise: Opportunities and Challenges. 40-minute session

Existing AI driven automation in enterprises employ ML, NLP and chatbots. There is additional opportunity for AI Planning to drive reasoning about action trajectories to help build automation. I will demo application of AI planning for migration of legacy infrastructure to Cloud, based on real client data, and discusses challenges in adopting AI planning solutions in the enterprise.

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

Presentations

Media Meets AI: How We Give Superpowers to BuzzFeed's Social Curators 40-minute session

As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. To this end, we first built publishing tools that let people work more efficiently. Now, we build artificial intelligence tools that let people work more intelligently. During this talk we plan to share this evolution with the audience.

Ted Way is a senior program manager on the Microsoft Azure Machine Learning engineering team. He’s passionate about telling the story of how AI will empower people and organizations to achieve more. He currently works on bringing machine learning to the edge and hardware acceleration of AI.

He received BS degrees in electrical engineering and computer engineering, MS degrees in electrical engineering and biomedical engineering, and a PhD in biomedical engineering from the University of Michigan – Ann Arbor. His PhD dissertation was on “spell check for radiologists,” a computer-aided diagnosis (CAD) system that uses image processing and machine learning to predict lung cancer malignancy on chest CT scans.

He has been invited as a keynote speaker for two Microsoft partner conferences, and has twice received the Microsoft Executive Briefing Center’s Distinguished Speaker Award, awarded to only 5 out of over 1,000 speakers.

Presentations

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

Deep neural networks (DNNs) have enabled breakthroughs in AI. Serving DNNs at scale has been challenging: fast and cheap? Won’t be accurate. Accurate and fast? Won’t be cheap. You’ll learn how Python and TensorFlow can be used to easily train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave, getting performance such as ResNet 50 in under 2 ms.

Aric Whitewood is co-founder of WilmotML, a machine learning and macroeconomics focused investment and advisory firm. He is also an Honorary Senior Lecturer in the Computer Science Department of University College London (UCL), for which he runs several research programs with UCL students on machine learning topics.

He focuses on the combination of neuroscience, artificial intelligence (A.I.), and investing, with a particular emphasis on developing investment systems that are transparent (enabling trust in investment decisions) and that operate on longer timescales than has historically been the case with algorithmic systems (typically months).

Previous to his current position, he was Head of Data Science in Credit Suisse Zurich, where he ran A.I. projects across a number of businesses and geographic locations. He also served as the Banks subject matter expert in machine learning, regularly presenting to both the Banks management as well as its major clients.

He holds a PhD in Electronic Engineering from UCL (2006).

Presentations

GAIA: The Global AI Allocator 40-minute session

Our firm focuses on the application of AI to investment management. Topics covered in this presentation include the application of AI to the problem of asset selection, dealing with low signal-to-noise ratios in financial time series data, the development of real-time macroeconomic indicators from social media data, and the use of heterogeneous compute architectures, specifically GPUs and FPGAs.

Andrew Zaldivar is a Developer Advocate in Google’s AI group, helping to bring the benefits of AI to everyone. Andrew develops, evaluates and promotes tools and techniques that can help the larger communities build responsible AI systems.

Before joining Google AI, Andrew was a Senior Strategist in Google’s Trust & Safety group and worked on protecting the integrity of some of Google’s key products by utilizing machine learning to scale, optimize and automate abuse-fighting efforts.

Prior to joining Google, Andrew completed his Ph.D. in Cognitive Neuroscience from the University of California, Irvine and was an Insight Data Science fellow.

Presentations

Responsible AI Practices: A Technical Demonstration 40-minute session

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

Matt Zeiler, Founder and CEO of Clarifai, is a machine learning Ph.D. and thought leader pioneering the field of applied artificial intelligence (AI). Matt’s groundbreaking research in computer vision alongside renowned machine learning experts Geoff Hinton and Yann LeCun has propelled the image recognition industry from theory to real-world application. Since starting Clarifai in 2013, Matt has evolved his award-winning research into developer-friendly products that allow enterprises to quickly and seamlessly integrate AI into their workflows and customer experiences. Today, Clarifai is the leading independent AI company and “widely seen as one of the most promising [startups] in the crowded, buzzy field of machine learning.” (Forbes) Reach him @MattZeiler.

Presentations

Closing the Loop on AI: How to Maintain Quality Long-Term AI Results 40-minute session

At the core of today's problems with image classification and deep learning lies one fundamental truth: most AI systems operate by choosing the path of least resistance – not the path of highest long-term quality. Matt Zeiler, founder and CEO of Clarifai, will discuss the company's approach to Closing the Loop on AI and employing techniques to counter the AI quality regression phenomenon.

Yi Zhuang is a software engineer at Twitter, where he tech leads a group of people to build platform and infrastructure for working with ML models. Currently, he works on unifying Twitter around a single ML framework & infrastructure, bringing together users of Lua Torch, PyTorch, Scikit Learn, XGBoost, VW, and other in house ML solutions. Previously, Yi led a group of people to develop a trillion-document scale distributed search engine at Twitter. Yi holds an MS in computer science from Carnegie Mellon University. He loves cats and enjoys pondering over all things technical and logical.

Presentations

Unifying Twitter Around a Single ML Platform 40-minute session

Twitter is a 4000+ employee company with many ML use cases. Historically, there are many different ways to productionize ML at Twitter. In this session, we describe the setup and benefits of a unified ML platform for production, and how Twitter Cortex team brings together users of various ML tools.

Yulia Zvyagelskaya is a data scientist at Dow Jones, where she is responsible for development and implementation of machine learning applications. Yulia developed several AI-driven projects in the fields of Computer Vision and Natural Language Processing. She holds Master’s degrees in NLP (Computational Linguistics, Artificial Intelligence), as well as in Big Data Management and Analytics. Yulia has won several international Artificial Intelligence and Big Data competitions.

Presentations

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

Companies have a strong need for complying with anti-money laundering, anti-bribery, corruption and economic sanctions regulation in mitigating third party risk. Dow Jones Risk & Compliance deliver research tools and services for vetting and investigation to evaluate these risks with more confidence. The presentation highlights how DJ uses deep learning and NLP for efficient compliance solutions.