Speakers

Santi Adavani is a cofounder at RocketML, where he and his team are building a superfast engine for building machine learning models. Previously, Santi was a product manager and software development lead in the Technology and Manufacturing Group at Intel. He holds a PhD in computational sciences from the University of Pennsylvania. His areas of expertise include high-performance computing, nonlinear optimization, partial differential equations, machine learning, and big data.

Sarah Aerni is a director of data science at Salesforce Einstein, where she leads teams building AI-powered applications using autoML. Previously, she led teams in healthcare and life sciences at Pivotal, building models for customers, and she cofounded a company offering expert services in informatics to both academia and industry. Sarah holds a PhD in biomedical informatics from Stanford University, where she performed research at the interface of biomedicine and machine learning.

Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001, a PhD from IIT Madras (2008) and a post-doctoral research fellowship in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL). He currently heads data sciences R&D at Walmart Labs, India. He has spent the last eighteen years creating intellectual property and building data-based products in Industry and academia. In his current role, he heads machine learning platform development and data science foundation teams, which provide platform/intelligent services for Walmart businesses across the world. In the past, he has led the team that delivered real-time hyper-personalization for a global auto-major as well as other work for various clients across domains such as retail, banking/finance, telecom, automotive etc. He has built PMML support into Spark/Storm and realized several machine learning algorithms such as LDA, Random Forests over Spark. He led a team that designed and implemented a big data governance product for a role-based fine-grained access control inside of Hadoop YARN. He and his team have also built the first distributed deep learning framework on Spark. He is a professional member of the ACM and the IEEE (Senior) for the last 10+ years. He has five full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems, artificial intelligence as well as Big-Data and other emerging technologies.

Brigitte Alexander is the managing director of artificial intelligence (AI) partner programs for Intel, where she’s responsible for creating a scalable and vibrant global AI partner ecosystem on Intel AI technology by attracting, recruiting, and maintaining relationships with best-of-breed enterprise independent software vendors, system integrators, and original equipment manufacturers. Previously, Brigitte led ecosystem and global marketing for Vuforia, an augmented reality platform owned by Qualcomm and then sold to PTC, and held a variety of positions, including director of partnerships, partner marketing, and product management, at such companies as Yahoo and Infospace. Brigitte holds an MBA from the Thunderbird School of Global Management and a BA from the University of California, Santa Barbara.

Krishna Anumalasetty is a principal program manager for Azure Machine Learning at Microsoft. He’s worked as a program and product manager at Azure cloud services and AI org for the last eight years, enabling enterprise customers with on-premises and cloud hybrid scenarios, helping them scale up and out in the cloud and implement security protections and easy to deploy ML models in the cloud. Krishna is a founding member of Microsoft’s AutoML team. He holds a master’s degree in computer science from Arizona State University.

Responsible for sales for AI and other engineering services and for joint go-to-market with leading technology companies

Satanjeev ‘Bano’ Banerjee is a machine learning engineer at Twitter working on Tweet relevance ranking. Previously he worked as a data scientist on Twitter’s Advanced Analytics team, and as a backend engineer within the consumer engineering team. He holds a PhD from CMU; his dissertation was on active learning in the context of meeting understanding and summarization. Bano’s interests outside of work include volunteering his data analysis skills at local non-profits, and training towards a marathon.

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 the Howard Hughes Medical Institute (HHMI). Dylan studied physics and math at the University of Maryland and holds a PhD in physics from New York University.

Rachel Bellamy is a principal research scientist and manages the Human-AI Collaboration Group at the IBM T. J. Watson Research Center, 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.

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. He holds a PhD in cognitive science from the University of California, Merced, where he studied the collaboration patterns of academics using NLP techniques.

Anna Bethke is a deep learning data scientist within the Artificial Intelligence Products Group at Intel, where she is the head of AI for Social Good. Her responsibilities include researching and designing fair, transparent, and accessible AI systems and establishing partnerships with social good organizations to enable their missions with Intel’s technologies and AI expertise. She’s an active member of the Intel AI Lab, developing deep learning NLP algorithms as part of the NLP Architect open source repository. Previously, she was a geospatial data scientist at MIT Lincoln Labs and Argonne National Labs and a senior data scientist at Lab41. Anna holds an MS and BS in aerospace engineering from MIT.

Aishani Bhalla is a software engineer on the Azure Machine Learning team at Microsoft, where she’s helping people operationalize models to build intelligence into every application, accelerated on FPGAs with Project Brainwave. She holds a BS in computer science from the University at Buffalo.

Tammy Bilitzky is CIO at DCL, where she’s responsible for managing the company’s technology department and 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 tp implement methodologies that will consistently provide clients with superior quality. Previously, she spent three decades at 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.

Sarah Bird is a principal program manager at Microsoft where she leads research and emerging technology strategy for Azure AI. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations research with the best of open source and product expertise to create new tools and technologies. She leads the development of responsible AI tools in Azure Machine Learning. She’s also an active member of the Microsoft Aether committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Previously, Sarah was one of the founding researchers in the Microsoft FATE research group and worked on AI fairness in Facebook. She’s an active contributor to the open source ecosystem; she cofounded ONNX, an open source standard for machine learning models and was a leader in the PyTorch 1.0 project. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She cofounded the SysML research conference and the Learning Systems workshops. She holds a PhD in computer science from the University of California, Berkeley, advised by Dave Patterson, Krste Asanovic, and Burton Smith.

Pradip Bose is a distinguished research staff member and manager of the Efficient and Resilient Systems Department at the IBM T. J. Watson Research Center as well as an adjunct professor at Columbia University. Pradip has been involved in the design and presilicon 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. Previously, he was the lead performance engineer for POWER3, a high-end processor development project, at IBM Austin and served as a visiting associate professor at the Indian Statistical Institute, 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, presilicon modeling, and validation. Pradip is the author or coauthor of over 100 publications (including several book chapters). He’s an IEEE fellow and a member of the IBM Academy of Technology and was the editor-in-chief of IEEE Micro from 2003 to 2006 and the chair of ACM SIGMICRO from 2011 to 2017. He’s received 25 Invention Plateau Awards and several Research Accomplishment and Outstanding Innovation Awards from IBM.

Fidan Boylu Uz is a senior data scientist at Microsoft, where she’s responsible for the successful delivery of end-to-end advanced analytic solutions. She’s also worked on a number of projects on predictive maintenance and fraud detection. Fidan has 10+ years of technical experience on data mining and business intelligence. Previously, she was a 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 and holds a PhD in decision sciences.

Joanna J. Bryson is a transdisciplinary researcher on the structure and dynamics of human- and animal-like intelligence and a Reader (associate professor) at the University of Bath. Her research covers topics from artificial intelligence to autonomy and robot ethics to human cooperation and has appeared in venues ranging from Reddit to Science. She holds degrees in psychology from Chicago and Edinburgh and artificial intelligence from Edinburgh and MIT. She has additional professional research experience from Princeton, Oxford, Harvard, and Lego as well as technical experience in Chicago’s financial industry and international management consultancy.

Chris Butler is the chief product architect at IPsoft. Previously, Chris worked at Microsoft, KAYAK, and Waze, and he was involved in 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 was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University. He’s created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Paris Buttfield-Addison is a 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 a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly; he recently finished writing Practical AI with Swift and is currently working on Head First Swift. He holds a degree in medieval history and a PhD in computing. Paris loves to bring machine learning into the world of practical and useful. You can find him on Twitter as @parisba.

Angelo A. Calvello is cofounder of Rosetta Analytics, a technology startup using proprietary deep learning techniques to create investment signals and scalable investment strategies. He’s also cofounder of Distributed Alpha LLC, a proprietary trading firm that invests in mispriced application-driven, decentralized technologies, cryptocurrencies, and blockchain tokenized assets, and founder of Impact Investment Partners, an investment consulting firm. A serial innovator and author and speaker focusing on artificial intelligence, Angelo’s unique insights into the rise of disruptive technologies and how they are reshaping 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. Angelo is the “dissident” columnist for Institutional Investor, where he had the most read opinion columns in 2017, and a 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. He has also published extensively in other publications, including Pensions & Investments, Healthcare Financial Management, and the American Indian Culture and Research Journal (UCLA). He’s the author of Environmental Alpha: Institutional Investors and Climate Change (Wiley 2009), founder and publisher of the Journal of Environmental Investing, and a member of the Chicago Quantitative Alliance. He’s also the chairman of the board of outreach with Lacrosse and Schools, a sports-based nonprofit that creates educational opportunities for at-risk youths on Chicago’s South and West Sides. He has over 25 years of experience in the institutional investment industry and holds a PhD in contemporary European philosophy from DePaul University.

Lawrence Carin 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. An IEEE fellow, Lawrence is one of the most widely published machine learning researchers in the world: he’s coauthored over 350 papers affecting fields as diverse as bomb detection, neuroscience, and voting behavior. He holds a BS, MS, and PhD 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.

Gunnar Carlsson is a professor of mathematics (emeritus) at Stanford University and 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 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.

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 is 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.

Jian Chang is a senior algorithm expert at the Alibaba Group, where he is working on cutting-edge applications of AI at the intersection of high-performance databases and the IoT, focusing on unleashing the value of spatiotemporal data. A data science expert and software system architect with expertise in machine learning and big data systems and deep domain knowledge on various vertical use cases (finance, telco, healthcare, etc.), Jian has led innovation projects and R&D activities to promote data science best practices within large organizations. He’s a frequent speaker at technology conferences, such as the O’Reilly Strata and AI Conferences, NVIDIA’s GPU Technology Conference, Hadoop Summit, DataWorks Summit, Amazon re:Invent, Global Big Data Conference, Global AI Conference, World IoT Expo, and Intel Partner Summit, and has published and presented research papers 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. He’s also served as a reviewer for many highly reputable international journals and conferences. Jian holds a PhD from the Department of Computer and Information Science (CIS) at University of Pennsylvania, under Insup Lee.

Celia joined AB in April 2017 as a data scientist. She has been working with multiple teams on building machine learning models, applying natural language processing techniques and leveraging other modern data science techniques to gain business insights and integrate alternative datasets to make better and faster investment decisions.

After completing her MA in Quantitative Methods with a data science focus at Columbia University, she joined the Data Incubator, a data science fellowship program, to train on cutting-edge data science techniques and technology. She is currently pursuing a MS in Computer Science specializing in machine learning from Georgia Institute of Technology.

Roger Chen is cofounder and CEO of Computable 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 realm 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.

Sanjian Chen is a Senior Algorithm Expert at the Alibaba Group. He has deep knowledge of large-scale machine learning algorithms. Over his career, he’s partnered with and advised leaders at several Fortune 500 companies on making data-driven strategic decisions and provided software-based data analytics consulting service to seven global firms across multiple industries, including financial services, automotive, telecommunications, and retail.

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 mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.

Adam Cheyer is a vice president of R&D at Samsung. Previously, he was cofounder and vice president of engineering at artificial intelligence company Viv (acquired by Samsung in 2016); was cofounder and vice president of engineering at Siri (acquired by Apple in 2010); cofounded Sentient Technologies, which applies distributed machine learning algorithms to discover novel solutions to complex problems; served as vice president of engineering at Verticalnet (enterprise software) and Dejima (mobile software); and was chief architect of CALO, the largest AI project in US history, at SRI International. Adam is a founding member and first developer at Change.org, the world’s largest petition platform, with a quarter billion members. He has authored more than 60 publications and 25 patents. Adam graduated with highest honors from Brandeis University and received the Outstanding Masters Student award from UCLA’s School of Engineering.

Andrew Y. Chin is the chief risk officer and head of quantitative research for AllianceBernstein, where he oversees all aspects of risk management to ensure that the risks being taken are well understood and appropriately managed and 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’s held a number of quantitative research roles at the firm, in both New York and London, including senior portfolio manager for style blend equities and director of quantitative research for value equities. Prior to joining the firm, he was a project manager and business analyst in global investment management at Bankers Trust. Andrew teaches in the School of Operations Research and Information Engineering at Cornell University and leads teams of students on capstone projects utilizing quantitative and data science skills to address investment issues. He holds a BA and an MBA from Cornell University.

Scott Clark is a cofounder and CEO of SigOpt, providing optimization tools as a service that help 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. Previously, he 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 was chosen as one of Forbes’s 2016 “30 under 30.” He holds a PhD in applied mathematics, an MS in computer science from Cornell University, and BS degrees in mathematics, physics, and computational physics from Oregon State University.

Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s 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.

Brendan Collins is a Solutions Engineer working to implement machine learning infrastructure for Algorithmia’s largest enterprise customers. Previously, he held a similar position at Synology. He has worked in financial enterprise infrastructure for more than 10 years, with groups ranging in size from the largest financial institutions in the world to community banks. Brendan has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems.

Simon Crosby is CTO at SWIM.AI, an edge intelligence software vendor that focuses on edge-based learning for fast data. He cofounded Bromium in 2010 and now serves as a strategic advisor. Previously, he was the CTO of the Data Center and Cloud Division at Citrix Systems; founder, CTO, and vice president of strategy and corporate development at XenSource; and a principal engineer at Intel, as well as a faculty member at Cambridge University, where he led the research on network performance and control and multimedia operating systems. Simon is an equity partner at DCVC, serves on the board of Cambridge in America, and is an investor in and advisor to numerous startups. He’s the author of 35 research papers and patents on a number of data center and networking topics, including security, network and server virtualization, and resource optimization and performance. He holds a PhD in computer science from the University of Cambridge, an MSc from the University of Stellenbosch, South Africa, and a BSc (with honors) in computer science and mathematics from the University of Cape Town, South Africa.

Alexis Crowell Helzer is senior director of artificial intelligence product marketing at Intel, where she and her team are responsible for technical positioning and messaging as well as outbound content and campaigns for Intel AI products. Alexis and her team partner with AI adopters across the industry from small device implementations to HPC clusters to launch products, showcase innovative use cases, and help other companies find their own AI path. She has an unyielding passion to deliver technology solutions that help businesses thrive. Over her rich career, she has run a cloud software engineering team focused on distributed computing and microservices integration, led the open source marketing efforts from Intel, and worked with many of the Fortune 100 companies to help incubate service offerings and deliver innovative products.

Nick Curcuru is vice president of enterprise information management at Mastercard, where he’s responsible for leading a team that works with organizations to generate revenue through smart data, architect next-generation technology platforms, and protect data assets from cyberattacks by leveraging Mastercard’s information technology and information security resources and creating peer-to-peer collaboration with their clients. Nick brings over 20 years of global experience successfully delivering large-scale advanced analytics initiatives for such companies as the Walt Disney Company, Capital One, Home Depot, Burlington Northern Railroad, Merrill Lynch, Nordea Bank, and GE. He frequently speaks on big data trends and data security strategy at conferences and symposiums, has published several articles on security, revenue management, and data security, and has contributed to several books on the topic of data and analytics.

Morten Dahl is cofounder and research scientist at Dropout Labs, a startup building a platform for secure, privacy-preserving machine learning to manage the sensitive, competitive, and regulatory nature of data. He is also lead developer on TF Encrypted, an open source project for integrating and experimenting with privacy-preserving machine learning directly in TensorFlow. With a background in cryptography and privacy, Morten has spent recent years applying and adapting techniques from these fields to machine learning, focusing on practical tools and concrete applications in hope of making the field more accessible to practitioners.

Jack Dashwood is a product marketing manager at Intel, which he joined as part of the company’s acquisition of Movidius in 2016; he continues to work in the Movidius Division of Intel’s Internet of Things Group. Previously, Jack worked in the field of augmented reality and commercial finance. He holds a BS, where he studied the complexities of the human vision system and cognitive processes, as well as a degree in finance and a master’s degree in global business from the University of Victoria.

Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.

Fabrizio Del Maffeo is managing director of AAEON Technology Europe, a leading company in industrial AI on the edge, the IoT, and machine vision. He’s the founder of UP Bridge the Gap, an Intel reference platform for machine vision and IoT use cases.

Zongjie, aka ”Z”, is currently a Director of Product Strategy at Cisco’s Data Center Compute product group (DCC). DCC provides world leading compute solutions to enterprise and SMB customers. DCC’s leading products include Unified Compute Systems (UCS), Cisco Converged Infrastructure (FlexPod, FlashStack, VBlock, etc.) and Cisco’s own HyperConverged solution – HyperFlex.
Z is known to be the “Intrapreneur”, who helps drive Cisco compute core product strategy, as well as identifying adjacent and new market opportunities to drive business growth. She also leads the Product Management team for Cisco’s AI/ML infrastructure products, aiming at helping Cisco’s customers deploy AI/ML in both on-prem and multi-cloud environment.
Prior to Cisco, Z worked at McKinsey & Company as an Engagement Manager. She was appointed to be the first Engagement Manager at McKinsey’s Fast Growth Tech Practice, specialized in serving start-ups. Prior to McKinsey, she spent 6 years in Singapore as a Product Engineer at Avago and a Supply Chain consultant at iCognitive.

With over twenty years in the IT industry, Anthony Dina serves as the North America Director of Data Analytics at Dell EMC. He leads a team of solutions architects that synthesize the tsunami of new data
types (machine, application, person-driven) with traditional systems of record. Their expertise in big data, data warehouse modernization and analytic modeling, artificial intelligence helps customers
succeed in the era of Digital Transformation. This work not only involves intellectual property from Dell EMC but also from partners like Cloudera, HortonWorks, Splunk, Intel, Nvidia and SAP. Prior to this, he
served as executive director of strategy and director of solutions marketing. He has earned a Masters of Business Administration from the University of St. Thomas and a Masters of Fine Art from Cranbrook
Academy of Art. His technical certifications include ITIL v3 Foundation and ITIL Services Strategy.

Yu Dong is a senior technical product manager at Facebook, where he works on the company’s AI/ML platform, FBLearner, which enables more personalized and smarter products. Previously, he was senior software engineer manager at HPE and Cisco. He holds a PhD in computer engineering and an MBA from the University of California, Berkeley. His passion is to democratize AI across various industries by building a performant, reliable, efficient, resilient, and easy-to-use AI platform.

Michael Eagan has 18 years of Project Management experience providing leadership for consulting clients on developmental and organizational issues.

As Vice President Product Development at Korn Ferry, the preeminent global people and organizational advisory firm, Eagan is responsible for enabling new and existing suites of products to operate in a cloud-based SaaS environment and driving excellence in the company’s fastest growing business segment. Prior to his work at Korn Ferry, he served in multiple roles at Aon Hewitt, most recently as Senior Director HR Services Workday.

Eagan is based in Charlotte, North Carolina.

Rob Earhart is a deep learning software engineer in the Artificial Intelligence Products Group at Intel, where he works on PlaidML, an open source polyhedral tensor compiler that makes it pretty easy to run neural networks with good performance on a wide variety of hardware. Prior to Intel (and prior to diving into machine learning systems implementation), Rob worked on the NT kernel, was one of the original Hyper-V hypervisor engineers, and cofounded the virtual machine monitor that grew up to power Google Compute Engine.

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’s taken her from a three-person business to fifty-thousand-plus-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.

Yoav Einav is vice president of product at GigaSpaces, where he drives product management, technology vision, and go-to-market activities. Yoav has more than 12 years of industry experience in product management and software engineering at high-growth software companies. Previously, Yoav held product management roles at Iguazio and Qwilt, mapping the product strategy and roadmap while providing technical leadership regarding architecture and implementation. An entrepreneur at heart, Yoav drives innovation and product excellence and successfully incorporates it with the market trends and business needs. He holds a BSC (magna cum laude) in computer science and business from Tel Aviv University and an MBA in finance from the Leon Recanati School in Tel Aviv University.

Miro Enev is a senior solutions architect at NVIDIA, specializing in advancing data science and machine intelligence while respecting human values. He supports the Pacific Northwest teams engaged with cloud, industrial, and retail clients while participating in research in deep reinforcement learning and edge-to-cloud AI. Miro holds a PhD from the University of Washington’s computer science and engineering department, where his thesis was on machine learning applications for information privacy in emerging sensor contexts. He studied cognitive science and computer science as an undergraduate at the University of California, Berkeley.

Mohamed Fawzy is senior manager and tech lead at Facebook. In his six years at the company, he’s worked on its distributed storage system and was part of the team that developed cold storage, Facebook’s exabyte archiver storage system that keeps your memories safe. More recently, he started the Distributed Training Group to build large-scale distributed training infrastructure for deep learning.

Neil Fendley is a computer vision researcher at JHU/APL, where he works on machine perception and reasoning, focusing on deep learning. Over the last two 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.

Sheldon Fernandez is CEO of DarwinAI. A seasoned executive and respected thought leader in the technical and enterprise communities, Sheldon has coupled his entrepreneurial endeavors with nontechnical pursuits throughout his career, resulting in an interdisciplinary approach that is critical to the intelligent application of AI. Sheldon is also an accomplished author and speaker at spaces including Singularity University, the prestigious think tank in the Bay Area, and he has written technical books and articles on many topics including both artificial intelligence and computational creativity. He holds a master’s degree in theology from the University of Toronto, with thesis work in neuroscience and metaethics, and also pursued creative writing at Oxford University.

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Marina Rose Geldard (Mars) is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. She compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS) as well as the AUC. She’s writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.

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. Previously, she was a professor in the Computer Science Department at the University of Maryland, College Park. Lise 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 and an elected board member of the International Machine Learning Society; she serves on the board of the Computing Research Association (CRA) and was cochair for ICML 2011. Lise is a recipient of an NSF Career Award and 12 best paper and best student paper awards. She holds a PhD from Stanford University, an MS from UC Berkeley, and a BS from UC Santa Barbara.

Bruno Gonçalves is a chief data scientist at Data For Science, working at the intersection of data science and finance. Previously, he 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’s 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. In 2015, he was awarded the Complex Systems Society’s 2015 Junior Scientific Award for “outstanding contributions in complex systems science” and in 2018 was named a science fellow of the Institute for Scientific Interchange in Turin, Italy.

Enhao Gong is the founder and CEO at Subtle Medical, an AI and radiology startup from Stanford and the winner of 2018 NVIDIA Inception Award at AI+Healthcare. He’s a serial entrepreneur and PhD in electrical engineering at Stanford, with a research focus on applying AI and deep learning to improve reconstruction, analysis, and quantification in medical imaging. His work applies AI to accelerate and reduce doses for MRI and PET and has been featured in numbers of academic journals and clinical conferences. Enhao has won several awards, including being recognized by Forbes China as one of 2018’s “30 under 30.”

Josh Gordon is a developer advocate at Google AI and teaches applied deep learning at Columbia University and machine learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter as @random_forests.

Desirée Gosby is vice president of identity and profile at Intuit, where she leads a team of data and software engineers to architect and develop all aspects of identity for 50M Intuit customers. Desi began her 10-year career at Intuit as a senior engineer and has continued to take on bigger roles and responsibilities—including as a founding member of the company’s Innovation and Advanced Technology Group. Prior to her current role, she served as director of product development for Intuit’s Emerging Services Group, where her team built shared infrastructure to deliver personalized mobile, image data extraction (OCR), and search experiences used throughout Intuit’s mobile and online products, including TurboTax, QuickBooks, and Mint. She holds six patents. Desi received Intuit’s Scott Cook Innovation award in 2009 for Intuit’s first mobile application GoPayment.

Sean Gourley is founder and CEO of Primer. Previously, he was cofounder and CTO of augmented intelligence company Quid. He sits on the board of directors at Anadarko and is a TED fellow. Sean 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.

Pankaj Goyal is the vice president of artificial intelligence strategy at HPE, where he’s focused on helping enterprises accelerate their digital transformation journeys through the power of AI. He’s passionate about the potential of AI to improve human lives. A computer science engineer by training, Pankaj built his first NLP algorithms in his undergrad days. Previously, he was a technology entrepreneur and a technology consultant at McKinsey & Company. He holds a bachelor’s degree in computer science from IIT Kanpur, India, and an MBA from IIM Bangalore, India.

Matt Greenwood is the chief innovation officer at Two Sigma Investments, where he has led company-wide efforts across both engineering and modeling teams. Matt oversees development of BeakerX, which extends the Jupyter Notebook to support to six languages, additional widgets, and one-click publication. Matt is also a board member and Venture Partner at Two Sigma Ventures and works closely with portfolio companies in both board membership and advisory capacities. Matt began his career at Bell Labs and later moved to IBM Research, where he was responsible for early efforts in tablet computing and distributed computing. Matt was also lead developer and manager at Entrisphere, where he helped create a product providing access equipment for broadband service providers. Matt holds a PhD in mathematics from Columbia University, where he taught for many years, as well as a BA and MA in math from Oxford University and an MA in theoretical physics from the Weizmann Institute of Science in Israel.

Anna Gressel is a litigation associate at Debevoise & Plimpton LLP and a member of the firm’s Commercial Litigation Group and Technology, Media & Telecommunications practice. Her practice focuses on complex civil litigation in federal and state courts, and she advises on legal and regulatory issues around artificial intelligence and other emerging technologies. She’s the coauthor of publications including “German Report May Be Road Map for Future AI Regulation,” “Storm Clouds or Silver Linings? Assessing the Impact of the U.S. CLOUD Act on Cross-Border Criminal Investigations,” and “Do the Apps Have Ears? Cross-Device Tracking." She sits on the board of directors of Ms. JD, a nonprofit organization dedicated to the success of women in law school and the legal profession. She a member of the Law Committee of the IEEE Global Initiative on the Ethics of Autonomous and Intelligent Systems.

Kristian Hammond is a chief scientist at Narrative 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 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.

Kevin Han is a business consultant and service planner at Naver/LINE, a Korean company known for the biggest domestic web portal (Naver), mobile messenger (LINE), and AI-related solutions (Clova). His team provides an end-to-end AI service for clients, from improving dialogue models to consulting clients to create maximum value out of the company’s chatbot service. Previously, Kevin was a cognitive consultant at IBM. He holds a bachelor’s degree in psychology and business from New York University.

Behrooz Hashemian is Vice President of Artificial Intelligence at VideaHealth. Previously, he was lead machine learning scientist at the MGH & BWH Center for Clinical Data Science, where he was 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, and chief data officer at the MIT Senseable City Lab, where he focused on innovative implementation of big data analytics and artificial intelligence in smart cities.

Vishal “Vish” Hawa is a principal data scientist at Vanguard, where he works closely with marketing managers to design attribution, propensity, and attrition modeling. Vish has over 15 years of experience in the retail and financial services industries. He has training in executive management from the Wharton School and holds postgraduate degrees in information sciences, statistics, and computer engineering from the Indian Statistical Institute.

Kim Hazelwood is a senior engineering manager leading the AI Infrastructure Foundation and AI Infrastructure Research efforts at Facebook, where the focus is designing and optimizing efficiency hardware and software systems for Facebook’s many applied machine learning-based products and services. Previously, Kim was a tenured associate professor at the University of Virginia, a software engineer at Google, and director of systems research at Yahoo Labs. She’s been recognized with an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, and the ACM SIGPLAN 10-Year Test of Time Award. She serves on the board of directors of CRA and has authored over 50 conference papers and one book. She holds a PhD in computer science from Harvard University.

Kevin He is the founder of DeepMotion. Kevin has spent his career pushing the boundaries of gaming and engineering. Previously, Kevin was the CTO of Disney’s midcore mobile game studio, technical director of ROBLOX, senior developer of World of Warcraft at Blizzard, and 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.

Martial Hebert is the director of Carnegie Mellon University’s Robotics Institute, a department in the School of Computer Science. A leading researcher in computer vision and robotics, Martial joined the faculty of the Robotics Institute in 1984, just five years after its founding. The Robotics Institute has since grown into the world’s largest robotics academic robotics research center, with an annual expenditures of over $85 million. Martial played a role in such high-profile projects as a pioneering program for self-driving vehicles. His research interests include computer vision, especially recognition in images and video data; model building and object recognition from 3D data; and perception for mobile robots and intelligent vehicles. In the area of machine perception for robotics, his group has developed techniques for people detection, tracking and prediction, and for understanding the environment of ground vehicles from sensor data. Martial has served on the editorial boards of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the International Journal of Computer Vision, for which he serves as editor-in-chief. He holds a PhD in computer science from the University of Paris.

Thomas Henson is a data engineering advocate and senior systems engineer for the unstructured data solutions team at Dell EMC. Thomas has been involved in many different big data, analytics, and artificial intelligence projects throughout his career, with a focus on distributed systems. He’s a proud alumnus of the University of North Alabama, where he earned his undergraduate and graduate degree in computer information systems. Thomas is an accomplished speaker in the artificial intelligence and big data ecosystem at various conferences.

Michael Hind is a distinguished research staff member in the IBM Research AI organization. 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. Previously, Michael spent seven years as an assistant/associate professor of computer science at SUNY New Paltz. Michael is an ACM Distinguished Scientist, a member of IBM’s Academy of Technology, and a former associate editor of ACM TACO. He has served on over 30 program committees, given talks at top universities and conferences, and coauthored 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. He holds a PhD from NYU.

Kyle Hoback is director of market enablement at WorkFusion, where he’s 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 as well. Kyle has nearly 15 years of consulting and software experience. Previously, he was a consultant with AT Kearney and Booz Allen Hamilton. He’s passionate about helping organizations efficiently collect and utilize the data, information, and knowledge of their organizations. 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.

Ana Hocevar is a data scientist in residence at the Data Incubator, where she combines her love for coding and teaching. Ana has more than a decade of experience in physics and neuroscience research and over five years of teaching experience. Previously, she was a postdoctoral fellow at the Rockefeller University, where she worked on developing and implementing an underwater touch screen for dolphins. She holds a PhD in physics.

Garrett Hoffman is a 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.

Carlos Humberto Morales is the senior director of deep learning systems at Intel, where he obsesses over making AI development easier and more available. Previously, Carlos was Nervana’s platform architect and an architect for Cisco Systems, where he focused on topics such as cross-platform infrastructure security and fog computing. Over the span of his career, Carlos has been fortunate enough to be involved with the development of software and hardware at just about every level, from microcode to data center-scale orchestration suites.

Hamel Husain is a senior data scientist at GitHub, where he’s 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. Previously, 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.

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.

Humayun Irshad is a lead scientist in machine learning and computer vision at Figure Eight, where he’s developing deep learning frameworks for various applications like object detection, segmentation, and classification for medical, retail, and self-driving car use cases. Previously, he was a postdoc at Harvard Medical School, where he developed machine learning and deep learning frameworks, including region of interest detection and classification, nuclei and gland detection, and segmentation and classification in 2D and 3D medical images.

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.

Tony Jebara is director of machine learning at Netflix and professor on leave from Columbia University. He has published over 100 peer-reviewed papers in leading conferences and journals across machine learning, computer vision, social networks, and recommendation and is the author of the book Machine Learning: Discriminative and Generative. His work has been recognized with best paper awards from the International Conference on Machine Learning and from the Pattern Recognition Society, the Career award from the National Science Foundation, and faculty awards from Google, Yahoo, and IBM. He has cofounded and advised multiple startup companies in the domain of artificial intelligence and served as general chair and program chair for the International Conference on Machine Learning. He holds a PhD from MIT.

Patrick Kaifosh is the cofounder and chief science officer at CTRL-labs, a venture-backed startup developing noninvasive neural interfaces. Patrick completed his graduate studies under the supervision of Larry Abbott and Attila Losonczy at Columbia University, where he studied circuit-level mechanisms of learning and memory in hippocampal and cerebellar memory systems and led the development of a widely used open source software package for analysis of calcium imaging data.

Swara Kantaria is a senior product manager at BuzzFeed, where she’s worked on a range of products from launching Buzzfeed.com in international markets to building new internal tools. Swara is the product lead for distribution tools, a team responsible for building products that send all of the company’s content to social media platforms (like Facebook, YouTube, Twitter, and Instagram), OTT destinations (like Roku and Pluto), and syndication partners. Previously, Swara was a technology consultant at Deloitte.

Anoop Katti is a data scientist in the Deep Learning Center at SAP, where he combines computer vision with techniques from natural language processing to work on documents with strong 2D structure. Previously, he built telecom software at Huawei. Anoop holds multiple patents and publications in the field. He did his bachelor studies at BIT, Bangalore, and pursued a research-based master’s in computer vision at IIT Madras.

Arun Kejariwal is an independent lead engineer. Previously, he was he 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, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. 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.

Chief Data Officer

Sanjay Krishnan is an assistant professor of computer science at the University of Chicago. His research focuses on applications of machine learning and control theory to computer and cyberphysical systems problems. His work has received a number of awards including the 2016 SIGMOD Best Demonstration award, 2015 IEEE GHTC Best Paper award, and Sage Scholar award. Sanjay holds a PhD and master’s degree in computer science from UC Berkeley.

Abhishek Kumar is a senior manager of data science in Publicis Sapient’s India 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 the 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’s 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. Abhishek has spoken at past O’Reilly conferences, including Strata 2019, Strata 2018, and AI 2019.

Marcel Kurovski is a data scientist at inovex, a German IT project house focusing on digital transformation, where he works on novel methods to exploit deep learning for recommender systems in order to better personalize content and improve user experience for clients in ecommerce and retail. His work bridges the gap between proof of concept and scalable AI systems, and his research spans recommender systems, deep learning, and methods for approximate nearest neighbor search. He holds 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.

Marcelo Labre is executive director at Morgan Stanley, where he works on model risk management. Marcelo has extensive experience both as a practitioner and in academia in traditional quantitative finance, machine learning, and artificial intelligence. Previously, he was head of model risk at OnDeck Capital; head of quant analytics and market data for OCBC Bank; adjunct associate professor at the National University of Singapore’s Business School; managing director and head of quantitative analytics at Standard Bank; director and head of quantitative analytics at ING Bank; and a member of the faculty at London Business School. He holds a PhD in mathematical finance from Imperial College London and a master’s degree in finance from London Business School.

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

James is WorkFusion’s Director of Strategic Markets. He focuses on those areas where customers have achieved greatest impact with AI, and where WorkFusion sees greatest opportunity, for example, Anti-Money Laundering.

James is a highly commended business adviser, supporting executives to transform their operations. He helped accelerate WorkFusion’s rapid growth across EMEA, and now has a global role. His recent Intelligent Automation work has involved two of Europe’s top 5 Banks, a top 3 US bank, a top 3 European Teleco, a leading Scandinavian retailer, and one of the world’s leading entertainment streaming companies.

He previously worked in Strategy & Operations at Deloitte, where he was identified as one of the UK’s leading consultants by the Management Consultancies Association. He focused on corporate strategy, innovation initiatives, operating model transformation and digital programmes.

His clients have included a range of global financial institutions and FTSE 100 companies, as well as the UK government, Police and the City of London. James is a fellow of the Adam Smith Institute and read Philosophy, Politics and Economics at the University of Oxford

Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a 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, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.

Jaewon Lee is a data scientist working on NLP at Naver and LINE in South Korea. His team focuses on developing the Clova Chatbot Builder Framework, enabling customers to easily build and serve chatbots to their own business, and undertakes NLP research to improve performance of their dialogue model. He joined Naver/LINE after his company, Company.AI, was acquired in 2017. Previously, Jaewon was a quantitative data analyst at Hana Financial Investment, where he used machine learning algorithms to predict financial markets. He holds a BS in applied math and statistics with computer science from Johns Hopkins University.

Nicholas Leonard 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. He graduated from the Royal Military College of Canada and holds an MS in computer science from the University of Montreal.

Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—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.

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, JPMorgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, TechCrunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, VentureBeat, TechTarget, and O’Reilly. Michael was a postdoc at Cornell, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Eric Liang is a second-year PhD student working in the University of California, Berkeley, RISELab with Ion Stoica. He works on frameworks and applications for machine learning and reinforcement learning. Previously, he spent several years working on systems in industry at Databricks and Google.

Joleen Liang is a partner at Squirrel AI Learning, mainly in charge of branding and media. Joleen is a successful serial entrepreneur and experienced brand planner who has successively built a number of fast-moving consumer goods, young fashion catering, and AI internet education brands. After becoming the partner at Yixue Squirrel (Songshu) AI Learning, she created the Squirrel AI brand. She has been invited to give keynote speeches at AI conferences such as IJCAI in Sweden, KDD in London, TechCrunch in Berlin, World Summit AI in Amsterdam, Bloomberg in Shanghai, and UBS headquarter in Zurich. Joleen also founded 360D, a branding theory that helped Squirrel AI Learning expand its influence and brand reputation internationally, which also resulted in a 7x compound annual growth rate.

Katie Link 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. A member of the Mount Sinai Health System AI Consortium (AISINAI), she is passionate about applying her skills in machine learning to solving problems in healthcare, and her research has focused on developing a novel semisupervised learning approach for accelerating the training of deep convolutional neural networks. She is a graduate of Johns Hopkins University (Phi Beta Kappa) with degrees in neuroscience and computer science and a FlexMed member of the Icahn School of Medicine’s class of 2023.

Victor Llorente is a technology program manager at Dow Jones, where he’s responsible for data strategy applications in the professional information business. Victor has worked in several workflow automation and AI-driven projects using BPM engines, big data, and data science technologies. He holds master’s degrees in computer engineering from Polytechnic University of Catalonia, Barcelona, and computer science from the Royal Institute of Technology, Stockholm, as well as an MBA from Instituto de Empresa, Madrid.

Ben Lorica is the chief data scientist at O’Reilly. 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.

Siwei Lyu is a tenured associate professor in the Department of Computer Science within the College of Engineering and Applied Sciences at the University at Albany, State University of New York, where he’s the director of the Computer Vision and Machine Learning Lab (CVML). His research interests include digital image forensics, computer vision, computational neuroscience, and machine learning. Siwei has published over 110 refereed journal and conference papers. He’s the recipient of the 2011 IEEE Signal Processing Society Best Paper Award, the 2010 National Science Foundation CAREER Award, SUNY Albany’s Presidential Award for Excellence in Research and Creative Activities, and the SUNY Chancellor’s Award for Excellence in Research and Creative Activities. He’s a senior member of the International Society of Electric and Electronic Engineers (IEEE) and a member of Omicron Delta Kappa. He holds a PhD in computer science from Dartmouth College.

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 has a background in technology and analytics. Being exposed to massive amounts of patent data very early in his career, he learned techniques to quickly build datasets 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 coauthored a desk reference for patent attorneys that provided an indexed analysis of appeals decisions for use in prosecuting patent applications (which is now in its 7th edition). Tom is a registered patent attorney and electrical engineer with a passion for IP systems. Previously, he was cochair of the Patent Analytics and Portfolio Management Department at the Minneapolis patent firm Schwegman, Lundberg & Woessner, PA. Tom has advised companies from startups to household name multinationals on IP strategy, operations, and policy and has spoken before a diverse audiences from patent attorneys to C-suite executives to engineers to startup founders on patent management, analysis, and strategy. He holds a law degree from Franklin Pierce Law Center and a BS from the University of Notre Dame.

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Adam Marek is a software architect at Intel.

Russell Martin is a data scientist in residence at the Data Incubator, where he instructs fellows, teaches online courses, and leads training courses with corporate partners. Russ lived and worked in the UK for 17 years, including at Warwick University and the University of Liverpool, where he taught in the Department of Computer Science. He holds a PhD in applied mathematics from the Georgia Institute of Technology.

Hilary Mason is the general manager for machine learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company (acquired by Cloudera in 2017). Hilary is the data scientist in residence at Accel Partners and serves on the board of the Anita Borg Institute. Formerly, she cofounded HackNY.org, a nonprofit that helps engineering students find opportunities in New York’s creative technical economy, served on Mayor Bloomberg’s Technology Advisory Council, and was the chief scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.

Alina Matyukhina is a cybersecurity researcher and PhD candidate at the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick. Her research focuses on applying machine learning, computational intelligence, and data analysis techniques to design innovative security solutions. Previously, she was a research assistant at the Swiss Federal Institute of Technology, where she took part in cryptography and security research projects. Alina is a member of the Association for Computing Machinery and the IEEE Computer Society. She has spoken at several security and software engineering conferences, including HackFest, IdentityNorth, ISACA Security & Risk, Droidcon SF, and PyCon Canada.

Leah McGuire is a principal member of the technical staff at Salesforce Einstein, where she builds platforms to enable the integration of machine learning into Salesforce products. Previously, 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 and completed a postdoctoral fellowship at the University of California, Berkeley. She holds a PhD in computational neuroscience from the University of California, San Francisco, where she studied the neural encoding and integration of sensory signals.

Brian McMahan is a data scientist at Wells Fargo, working on projects that apply natural language processing (NLP) to solve real world needs. Recently, he published a book with Delip Rao on PyTorch and NLP. Previously, he was a research engineer at Joostware, a San Francisco-based company specializing in consulting and building intellectual property in NLP and Deep Learning. Brian is wrapping up his PhD in computer science from Rutgers University, where his research focuses on Bayesian and deep learning models for grounding perceptual language in the visual domain. Brian has also conducted research in reinforcement learning and various aspects of dialogue systems.

Ming-Wei Chang is a research scientist at Google AI Language. He enjoys developing interesting machine learning algorithms for practical problems, especially in the field of natural language processing. He has published more than 35 papers at top-tier conferences and won an outstanding paper award at ACL 2015 for his work on question answering over knowledge bases. He also won several international machine learning competitions on topics like entity linking, power load forecast prediction, and sequential data classification. His recent paper, “BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding“—cowritten with his colleagues in Google AI Language—demonstrates the power of language model pretraining and details the new state of the art for over 11 natural language processing tasks.

Vinay Seth Mohta is CEO at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Previously, Vinay was a product manager at KAYAK, where he worked with both Hadoop and Hive to develop a robust view of customers and developed a predictive model for flight pricing, and an architect at Endeca Technologies, where he worked on the engineering team that developed new data structures and indexing technologies to enable search and faceted navigation. He’s a coinventor on several granted patents for search and faceted navigation. Vinay holds a BS and a master’s degree in engineering from MIT.

Philipp Moritz is a PhD candidate in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He’s a member of the Statistical AI Lab and the RISELab.

Kurt Muehmel is the vice president of solutions engineering at Dataiku, where he’s built analytics and AI solutions for Fortune 100 companies worldwide and is building its solutions engineering capability worldwide. Having worked with dozens of clients of all sizes and across a multitude of sectors, Kurt has developed a deep understanding of the challenges and opportunities for companies looking to increase the value they’re deriving from their data and increase the capabilities of the growing teams of data scientists, engineers, and analysts. In a career that’s spanned several international moves, he’s worked for the United Nations, a Big Four consultancy, and a struggling high school in the Paris suburbs

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 nine 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.

Banu Nagasundaram is a product marketing manager with the Artificial Intelligence Products Group at Intel, where she drives overall Intel AI products positioning and AI benchmarking strategy and acts as the technical marketer for AI products including Intel Xeon and Intel Nervana Neural Network Processors. Previously, Banu was a product marketing engineer with the Data Center Group at Intel, where she supported performance marketing for Xeon Phi, Intel FPGA, and Xeon for AI; was a design engineer on the exascale supercomputing research team with Intel Federal; and worked at Qualcomm doing design verification of mobile processors. Banu holds an MS in electrical and computer engineering from the University of Florida and is working toward an MBA at UC Berkeley’s Haas School of Business.

Karthikeyan Natesan Ramamurthy is a research staff member in IBM Research AI at the T. J. Watson Research Center. His broad research interests include 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’s an associate editor of Digital Signal Processing and a member of the IEEE. He holds a PhD in electrical engineering from Arizona State University.

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. His recent roles include director of the Learning Group at O’Reilly and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.

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

Jack Norris is the senior vice president of data and applications at MapR Technologies, where he works with leading customers and partners worldwide to drive the understanding and adoption of new applications enabled by data and analytics. With over 25 years of enterprise software experience, he has demonstrated success from identifying new markets to defining new products to launching companies. Jack’s background includes senior executive positions with establishing analytic, virtualization, and storage companies. Jack was an early employee of MapR Technologies and held senior executive roles with EMC, Brio Technology, and Bain and Company.

Will Nowak is a data scientist a Dataiku, where he helps Fortune 500 companies improve data science operations. Previously, he engineered machine learning models for several Y Combinator startups, learning the pitfalls and challenges to productionalizing machine learning. Will holds a bachelor’s in math and economics from Northwestern University and a master’s in organizational leadership from Columbia University. In addition to the aforementioned topics, Will enjoys biking, coffee, and donuts and dislikes buzzwords, pretension, and meanness.

Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up. He’s writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll. (A quoll is an Australian animal.)

Eric Karl Oermann is an instructor of neurological surgery in the Mount Sinai Health System and the director of AISINAI, Mount Sinai’s artificial intelligence research group. Prior to attending medical school, Eric spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino. He 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 50 manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, he was selected as one of Forbes’s “30 under 30” for his work in applying machine learning to develop prognostic models for cancer patients. He’s 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. He holds an MD and studied mathematics at Georgetown University with a focus on differential geometry; he completed a postdoctoral fellowship at Google (Google Health/Verily Life Sciences).

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.

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), the Department of Veterans Affairs (VA), the US 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 US 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’s passionate about mentoring junior talent and promoting education for the firm’s Women in Data Science group. Previously, Catherine worked for the CDC, the Defense Advanced Research Projects Agency (DARPA), and the US intelligence community. She holds a BS in applied biology from Georgia Tech, an MPH in environmental and occupational health from Emory University, and an MBA from George Washington University. She’s also a Booz Allen NVIDIA-certified Deep Learning Instructor.

Jim Pastore is a litigation partner at Debevoise & Plimpton LLP and a member of the firm’s Cybersecurity and Data Privacy Practice and Intellectual Property Litigation Group. His practice focuses on privacy and cybersecurity issues. He’s recognized by Chambers USA and the Legal 500 US (2015–2019) for his cybersecurity work and was included in Benchmark Litigation’s Under 40 Hot List, which recognizes attorneys under 40 with outstanding career accomplishments. Named as a cybersecurity trailblazer by the National Law Journal, he’s twice been named to Cybersecurity Docket’s “Incident Response 30,” a list of the best and brightest data breach–response attorneys. Previously Jim served for five years as an Assistant United States Attorney in the Southern District of New York, where he spent most of his time as a prosecutor with the Complex Frauds Unit and Computer Hacking and Intellectual Property Section.

Maharshi Patel is a Software Engineer at Microsoft working on FPGA accelerated Azure Machine Learning Platform for Cloud and Edge.

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.

Anwesa Paul is chief global privacy counsel at American Express, where she advises all parts of the business with legal questions relating to US and Canadian financial privacy laws, marketing privacy laws, online and mobile privacy self-regulatory guidelines, big data governance, and big data breaches. Previously, Anwesa was in-house counsel at online advertising technology startups Kinetic Social and Epic Media Group and worked in the privacy space at the New York State Attorney General’s Office Internet Bureau, handling consumer protection issues related to online marketing.

Justina Petraityte is a developer advocate at Berlin-based startup Rasa, where she helps improve the developer experience in using open source software for conversational AI. Justina has a background in econometrics and data analytics, and her interests include chatbots, natural language processing, and open source. Her curiosity for data science and human-behavior analytics has taken her to many places and industries; over the past three years, she’s worked in the video gaming, fintech, and insurance industries.

Dmitry Petrov is a creator of the open-source tool Data Version Control (DVC.org), a building block for MLOps infrastructure.

Dmitry is a former data scientist at Microsoft with a Ph.D. in Computer Science. Today, he is based in San Francisco working on tools for machine learning and data versioning as a Co-Founder and CEO of Iterative.AI.

Forough Poursabzi-Sangdeh is a postdoctoral researcher at Microsoft Research New York City. She works in the interdisciplinary area of interpretable and interactive machine learning, collaborating 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’s 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.

Rajendra Prasad (RP) is the Global Automation and Artificial Intelligence Lead for Accenture Technology Services. In this leadership role, RP focuses on driving efficiency across the IT application lifecycle for Fortune 500 companies. With his more than 25 years of industry experience, he partners with clients on their journeys to stay competitive and achieve business strategy in our post-digital world. RP holds 13 patents, including in AI, and over 120 patents-pending, and has had 30 papers published in international journals. He leads the team that created and currently deploys Accenture myWizard, an end-to-end intelligent automation and delivery excellence platform with Artificial Intelligence at its core.

Adam Procter is a deep learning software engineer in the Artificial Intelligence Products Group at Intel, where he works on the core design of the Intel nGraph deep learning compiler. He holds a PhD in computer science from the University of Missouri, where his research focused on programming language semantics, high-assurance computing, and techniques for compiling functional programming languages to reconfigurable hardware.

Ruchir Puri is the chief scientist of IBM Research and an IBM Fellow. Previously, he led IBM Watson as its CTO and chief architect and has held various technical, research, and engineering leadership roles across IBM’s AI and Research businesses and served as an adjunct professor at Columbia University and a visiting scientist at Stanford University. Ruchir is a fellow of the IEEE, has been recognized as an ACM Distinguished Speaker and an IEEE Distinguished Lecturer, and was awarded 2014 Asian American Engineer of the Year. Ruchir was also honored with the John Von-Neumann Chair at the Institute of Discrete Mathematics at Bonn University, Germany. Ruchir is an inventor who holds over 50 United States patents; he has authored over 100 scientific publications on software, hardware automation methods, and optimization algorithms.

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’s 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 the US and Canada by Corinium. His recent paper, “A Strategist’s Guide to Artificial Intelligence,” won the 2017 Azbee Award for Best Paper. Anand holds an MSc in computer science from the Birla Institute of Technology and Science, 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.

Delip Rao is the vice president of research at the AI Foundation, where he leads speech, language, and vision research efforts for generating and detecting artificial content. Previously, he founded the AI research consulting company Joostware and the Fake News Challenge, an initiative to bring AI researchers across the world together to work on fact checking-related problems, and he was at Google and Twitter. Delip is the author of a recent book on deep learning and natural language processing. His attitude toward production NLP research is shaped by the time he spent at Joostware working for enterprise clients, as the first machine learning researcher on the Twitter antispam team, and as an early researcher at Amazon Alexa.

Vinay Rao is the cofounder and CEO of RocketML, a machine learning platform on a mission to lead and enable transformation of the world toward artificial intelligence. RocketML implements bleeding-edge learning algorithms to perform at scale, delivering “near-real-time” training performance on any data size.

Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University. He’s also affiliated with the Statistical Machine Learning Group, the Pervasive Parallelism Lab, and the Stanford AI Lab. His work’s goal is to enable users and developers to build applications that more deeply understand and exploit data. His contributions span theory, systems, and machine learning, and his work has won best papers at the premiere venue in each area. Work from his group has been incorporated into major scientific and humanitarian efforts, including MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies. He cofounded a company, based on his research, that was acquired by Apple in 2017. He also recently cofounded a company, SambaNova, based on his research, that is currently building accelerated platforms for AI.

Matthew Reyes is a consultant and an independent researcher developing a reinforcement learning-based approach to influence the maximization on social networks. Previously, he spent more than four years at MIT’s Lincoln Laboratory. He holds a PhD and MS in electrical engineering systems from the University of Michigan, Ann Arbor, and an MS and BS in math from Wichita State University.

Matthew Reyes is a contractor at Technergetics working on deep learning and FPGAs. He earned his B.S. and M.S. in mathematics at Wichita State University in Wichita, KS. He then earned his M.S. and Ph.D. in EE:Systems at the University of Michigan in Ann Arbor, MI doing a thesis on compression of Markov random fields. He worked for four years at MIT Lincoln Laboratory doing work on sensor calibration. From early 2015 to early 2019, Matt conducted independent research on compression, belief propagation, and interpolation of Markov fields, and is currently developing a model of social decision-making based on random utility.

Bill Roberts is managing director of Deloitte Consulting, where he specializes in data science, algorithms, and advanced analytics. He has over 20 years’ experience applying machine learning and stochastic modeling to real-world problems in industry, government, and academia, including algorithmic solutions that enable analytics to be applied at scale to open and closed source data.

Thomas Roehm is senior director of global marketing and messaging at SAS, where he’s responsible for providing global direction for messaging, sales enablement, and industry sales support. He leads positioning and market-facing activities in artificial intelligence and oversees several operational teams, including corporate messaging, product and industry marketing, customer validation, global industry practices, and marketing and sales enablement. Thomas has more than 20 years’ experience working within the manufacturing industry and 32 years’ experience in information technology, spent helping clients leverage analytics for new business values.

Tom Sabo is a principal solutions architect at SAS. He’s been immersed in the field of text analytics as it applies to federal government challenges since 2005. Tom presents work internationally on diverse topics including modeling applied to government procurement, best practices in social media analysis, and using analytics to leverage and predict research trends. He also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS and OHA on social media strategies. He holds a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.

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

Shioulin Sam is a research engineer at Cloudera Fast Forward Labs, where she bridges academic research in machine learning with industrial applications. Previously, she managed a portfolio of early stage ventures focusing on women-led startups and public market investments and worked in the investment management industry designing quantitative trading strategies. She holds a PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.

Tony Sandoval has been working in the AI and Machine Learning Space for the past few years, having assisted in deploying AI solutions into companies such as Liberty Mutual Life Insurance and Nationwide. Starting as a developer during the early dot-com years, he advanced through various technology-based organizations in the US, Europe and Asia, with his last international posting being the Director of Technology for the multinational HR, Payroll and Billing company, Adecco in North East Asia. He has also worked for the Advertising Agencies Publicis, Leo Burnett and J Walter Thompson. In addition, Tony has served as CTO for MyRegistry.com as well as having been a founding member in startups, most notably, the Tackable and Eventster apps.

Tony holds a Bachelors of Arts in International Relations from New York University as well as a Juris Doctor from Brooklyn Law School. He also is a retired member of the New York and New Jersey Bar, as well as the Federal Bar.

Tony has also worked on Digital Advertising Campaigns for such brands as: Thule, General Mills products, Grey Goose, Bacardi, Yellow Tail Wines, Sisley and Plantronics.

Sumit Sanyal is founder and chief revenue officer at Minds.ai.

Tim Schwuchow is a data scientist in residence at the Data Incubator. Previously, he designed and instructed several undergraduate- and graduate-level courses at Duke, was an analyst at quantitative hedge fund D. E. Shaw & Co., and was an economist for the Postal Regulatory Commission. He holds degrees in economics from Harvard and Duke.

Kabir Seth is the director of operations for the Wall Street Journal product, design and engineering lead, and the colead of the AI Center of Excellence at Dow Jones. He has worked in a variety of industries including apparel, travel, and children’s media. He spends his free time with his family, watching movies, playing with Legos, and writing fiction.

Ivan Shcheklein is cofounder and CTO at Iterative AI, where he’s working on tools for data scientists. Previously, he was team lead for open source project Sedna.org and cofounded the Tweeted Times (acquired by Yandex in 2011). He holds an MS in CS.

Alex Siegman is an AI technical program manager at Dow Jones and colead of the Dow Jones AI Center of Excellence. Alex’s expertise includes successfully managing projects, products, and people as well as studying and perfecting the constants—those skills and techniques necessary to design, develop, and deploy revenue-driving technologies across place and time.

Gadi Singer is vice president of the Artificial Intelligence Products Group and general manager of architecture at Intel, where he’s responsible for the planning and architecture of future products and technologies in the AI space, including dedicated deep learning ASICs such as the Intel Nervana Neural Network Processor. He previously oversaw the architecture of future IPs and leadership technologies for integration in Intel products and foundry such as CPU cores, graphics, imaging, security and audio, among others and drove solutions for emerging workloads and usages in artificial intelligence and machine learning.

Evan Sparks is a 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 the University of California, 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.

Matt Speck is a data engineer and senior consultant in the Strategic Innovation Group (SIG) at Booz Allen Hamilton, where he works on cognitive solutions projects, building intelligent search and chat applications. Previously, he taught data science and Python at General Assembly, a coding boot camp with locations across the globe.

Vladimir Starostenkov is a machine learning solution architect at Altoros with extensive experience in big data.

Ion Stoica is a professor in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley, where he researches cloud computing and networked computer systems. Previously, he worked on dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He’s 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).

Adam Straw is a Deep Learning Software Engineer in the Artificial Intelligence Products Group at Intel Corporation. Adam received a B.S. in Computer Engineering from Iowa State University and is currently working on a M.S. in Computer Science with a specialty in machine learning at Georgia Institute of Technology. Adam work on Intel® nGraph™ deep learning compiler with special focus on core design including current responsibilities for the nGraph quantization scheme.

Baohong Sun is the Dean’s Distinguished Chair Professor of Marketing at Cheung Kong Graduate School of Business. Before joining CKGSB, she was the Carnegie Bosch Chair Professor of Marketing at Carnegie Mellon University. She holds a Ph.D. in economics from University of Southern California.
She develops dynamic structural models to investigate consumer response to cross-selling campaigns, loyalty programs, service allocation in service centers, new service channels, optimal design of subscription pricing, dynamic and proactive customer information management. Her recent research interest focuses on modeling dynamic and inter-dependent consumer decisions on e-commerce and social media platforms.
Her papers have been nominated for John Little Best Paper Award and Long-Term Impact Award by Informs and won CART Research Frontier Award for Innovative Research at CMU. She won All Star Teaching Awards and was selected Master of MBA teaching. She also won George Leland Bach Teaching Award at CMU. She serves on the editorial boards of Journal of Marketing Research, Marketing Science, Journal of Marketing etc.

Her work and speech have been cited by medias such as the Economist, Wall Street Journal, New York Times, Bloomberg and BBC. She speaks at global conferences such as Summer and Winter Davos.

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 led business operations for Bing Shopping in the US and Europe with Microsoft’s Bing Group and built and ran distributed teams that helped scale Amazon’s financial systems with Amazon in both Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

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

Katherine Taylor is a senior data scientist in the Global Technology Practice for Artificial Intelligence and Machine Learning at SAS. She researches and develops AI applications in industry that are practical and understandable and deliver tangible benefits; in this capacity, she works closely with SAS research and development and customers to connect the technology with the market and the market with technical understanding. Her background is in quantitative analysis, primarily in energy and banking.

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, Jeff’s work uses conceptual processes like remix, translation, and visualization to physicalize and give materiality to otherwise invisible processes. He’s an assistant professor and program director of visual art and technology at Stevens Institute of Technology, where he’s also the coordinator of the Arts and Music research cluster at the Institute for Artificial Intelligence. This fall, he’s a visiting fellow at King’s College and artist in residence at the Computer Laboratory, both at University of Cambridge.

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.

Richard Tong is the Chief Architect of Squirrel AI Learning by Yixue Education Group. He is an experienced ed-tech technologist, executive and entrepreneur. He was the Head of Implementation, Greater China Region for Knewton, and Director of Solution Architecture for Amplify Education. He also served as CTO of Phoenix New Media (NYSE:FENG). He has been heavily involved in education technology standardization in the last 8 years. He is a current member of the IEEE AIS (Adaptive Instructional Systems) Standard working group and chair for the 2247.2 Interoperability Subgroup, a member of the IEEE ICICLE (IC Industry Consortium on Learning Engineering) and IEEE FML (Federated Machine Learning) working group. He was a member of the School Interoperability Framework Association (SIFA) Technology Board, and co-chair of the Assessment Group and IDM Group. He also served as a member of the Assessment Interoperability Framework working group for Common Education Data Standard (CEDS), and IMS Global Caliper standard workgroup and Computer Adaptive Testing workgroup, etc.

Olga Troyanskaya is a professor at the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University and deputy director for genomics at the Flatiron Institute of the Simons Foundation. Her group is focused on developing machine learning methods to address cutting edge problems in genomics and precision medicine. She’s a fellow of the International Society for Computational Biology and a recipient of the Sloan Research Fellowship, the NSF CAREER award, the Howard Wentz faculty award, and the Blavatnik Finalist Award. She has also been honored as one of the top young technology innovators by MIT Technology Review and is the 2011 recipient of the Overton Prize from the International Society for Computational Biology and the 2014 Ira Herskowitz Award from the Genetic Society of America. She holds a PhD from Stanford University.

Suresh Vadakath is an NYC-based data scientist at DataRobot, where he helps customers and prospects understand and leverage automated machine learning. Suresh has eight years of experience in analytics and data science. Previously, he was a senior solutions engineer at Alteryx, where he guided high-touch enterprise customers in their data prep and predictive analytics initiatives. He holds an MBA in finance from the University of Rochester in Upstate NY.

Pamela Vagata is the AI tech lead at Stripe, where she focuses on building deep learning models. Previously, she was a member of Facebook AI Research, developed FBLearner Flow (Facebook’s production ML infrastructure), and spent time building data infrastructure.

Sundar Varadarajan is a consulting partner on AI and ML at Wipro and plays an advisory role on edge AI and ML solutions. He’s an industry expert in the field of analytics, machine learning and AI, having ideated, architected and implemented innovative AI solutions across multiple industry verticals. Sundar can be reached at sundar.varadarajan@wipro.com.

Deepashri Varadharajan is a lead analyst at CB Insights, researching the intersection of AI and different industries. She’s passionate about studying new business models and opportunities that AI is helping shape and how traditional corporations and governments are adapting to a rapidly changing landscape. Previously, she worked for media houses including Al Jazeera America. Deepashri holds an undergraduate degree in electronics and communications engineering from VIT University in India and an MS from the Columbia University Graduate School of Journalism.

Kush R. Varshney is a research staff member and manager at IBM Research AI at the T. J. Watson Research Center, where he leads the Learning and Decision Making Group. He’s the founding codirector of the IBM Science for Social Good initiative. His research 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 also 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. He holds a PhD and SM in electrical engineering and computer science from MIT, where he was a National Science Foundation Graduate Research Fellow, and a BS (magna cum laude) in electrical and computer engineering with honors from Cornell University.

Benjamin Vigoda is the CEO of Gamalon. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor, a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing, and a cofounder of Design That Matters, a not-for-profit that, for the past decade, has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. Lyric Semiconductor was named one of the “50 most innovative companies” by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Lyric was successfully acquired by Analog Devices, and Lyric’s products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. Ben has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation and National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. He’s serving on the DARPA Information Science and Technology (ISAT) steering committee. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.

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.

Nanda Vijaydev is the lead data scientist and head of solutions at BlueData (now HPE), where she leverages technologies like TensorFlow, H2O, and Spark to build solutions for enterprise machine learning and deep learning use cases. Nanda has more than 10 years of experience in data science and data management. Previously, she worked on data science projects in multiple industries as a principal solutions architect at Silicon Valley Data Science and served as director of solutions engineering at Karmasphere.

Maja Vukovic is a research manager and a research staff member at the 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 the cognitive service management team, focusing 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. She’s a member of the IBM Academy of Technology and a senior member of the IEEE. In 2018, she was recognized with the IEEE TCSVC Award for Women in Services Computing. She’s an IBM Master Inventor, with over 160 patents filed and 50 granted. She’s also the author of over 90 papers in top international conferences and journals. Maja is a cofounder of a number of workshops, including Enterprise Crowdsourcing and Ubiquitous Crowdsourcing and Social Web for Disaster Management, collocated with leading international conferences. Previously, Maja was a research scientist at the Mercedes-Benz Research and Technology Center in Palo Alto, working in the field of telematic services. Maja holds a PhD from the University of Cambridge, UK, for her work on context aware service composition using AI planning, as well as an MSc from International University in Germany and a BSc from the University of Auckland, New Zealand.

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.

Ted Way is a senior program manager on the Azure Machine Learning engineering team at Microsoft, where he works on bringing machine learning to the edge and hardware acceleration of AI. He’s passionate about telling the story of how AI will empower people and organizations to achieve more and has been invited as a keynote speaker for two Microsoft partner conferences. He has twice received the Microsoft Executive Briefing Center’s Distinguished Speaker Award, awarded to only five out of over 1,000 speakers. He holds 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.

Nick Werstiuk is Director of OM for Spectrum Computing and PowerAI. He and his team are responsible for the vision and roadmap of the Spectrum Computing portfolio of offerings, driving expansion from HPC and Analytics into AI and Cloud. Since joining Platform in 2004 he has led a range of new product, and corporate strategy initiatives, which included the acquisition of Platform by IBM.

Aric Whitewood is cofounder 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. Aric focuses on the combination of neuroscience, artificial intelligence, 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). Previously, he was head of data science at Credit Suisse Zurich, where he ran AI projects across a number of businesses and geographic locations and served as the bank’s subject-matter expert in machine learning, regularly presenting to both the bank’s management and its major clients. He holds a PhD in electronic engineering from UCL.

Wenming Ye is an AI and ML solutions architect at Amazon Web Services, helping researchers and enterprise customers use cloud-based machine learning services to rapidly scale their innovations. Previously, Wenming had diverse R&D experience at Microsoft Research, an SQL engineering team, and successful startups.

Haizi Yu is a fifth-year doctoral student in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where he’s a research assistant in the Coordinated Science Laboratory. His research interest spans automatic concept learning, interpretable machine learning, automatic knowledge discovery, optimization, computational creativity, and music intelligence. He holds an MS in computer science from Stanford University and a BS from the Department of Automation at Tsinghua University.

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

Andrew Zaldivar is a senior developer advocate for Google AI. His job is to help to bring the benefits of AI to everyone. Andrew develops, evaluates, and promotes tools and techniques that can help communities build responsible AI systems, writing posts for the Google Developers blog, and speaking at a variety of conferences. Previously, Andrew was a senior strategist in Google’s Trust and Safety Group and worked on protecting the integrity of some of Google’s key products by using machine learning to scale, optimizing, and automating abuse-fighting efforts. Andrew holds a PhD in cognitive neuroscience from the University of California, Irvine and was an Insight Data Science fellow.

Matthew Zeiler is the founder and CEO of Clarifai, where he is applying his pioneering research in applied artificial intelligence to create developer-friendly products that allow enterprises to quickly and seamlessly integrate AI into their workflows and customer experiences. An artificial intelligence expert, Matt led groundbreaking research in computer vision, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, that has propelled the image recognition industry from theory to real-world practice.

Yi Zhuang is a senior staff machine learning software engineer at Twitter, where he leads a team building a platform for working with ML models. He works on uniting ML practitioners around a single ML platform, bringing consistency to ML practices at Twitter. Previously, Yi led a team 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.

Yulia Zvyagelskaya is a data scientist at Dow Jones, where she’s responsible for the development and implementation of machine learning applications. Yulia has developed several AI-driven projects in the fields of computer vision and natural language processing. She holds master’s degrees in NLP (computational linguistics and artificial intelligence) and big data management and analytics. Yulia has won several international artificial intelligence and big data competitions.