Dean Abbott is an internationally recognized thought leader and innovator in data science and predictive analytics, with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis, and many more. He is frequently included in lists of the top pioneering and influential data scientists in the world. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. He holds a bachelors degree in computational mathematics from Rensselaer Polytechnic Institute and a masters degree in applied mathematics from the University of Virginia.

Javed Ahmed is a Senior Data Scientist with Metis, where he focuses on corporate training programs in Machine Learning and analytics. A financial economist by background, he has extensive experience developing analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors. Javed holds a PhD in Finance and MA in Statistics from U.C. Berkeley, as well as undergraduate degrees in Finance and Systems Engineering from the University of Pennsylvania.

Vladimir Barash is Director Graphika Labs. He has received his Ph.D. from Cornell University, where he studied Information Science and wrote his thesis on the flow of rumors and virally marketed products through social networks. At Graphika, Vladimir's research focuses on deep learning applications of network analysis, detection and deterrence of disinformation operations on networks, and causal mechanisms of large-scale social behavior.

In addition to his research duties, Vladimir has a decade's experience working with big data, from scientific computing (Matlab, scipy) to parallel processing technologies (Hadoop / Hive) to data storage and pipelining (Redis, mongodb, MYSQL) at the terabyte scale. At Graphika, Vladimir has co-designed and implemented systems that process tens of millions every six hours to deliver timely information on influencers and conversation leaders in online communities tailored to client interests. Vladimir is proficient in over a dozen programming languages and frameworks and has designed production-ready systems for every stage of big data analysis, from collection to client-facing presentation via web, spreadsheet or graphic visualization.

Vladimir has been active in the Social Media Research Foundation (SMRF) and the NodeXL project, helping build a network analysis package that brings relational data analysis at scale to the fingertips of any interested user, without requiring specialized knowledge or technical training beyond familiarity with Microsoft Excel. NodeXL has enabled users in academia, industry and the general public to analyze tens of thousands of social networks, from networks of politicians voting on bills to networks of motorcycle enthusiasts working together. As part of his work with SMRF and the NodeXL team, Vladimir has contributed a chapter on Twitter analysis to Analyzing Social Media Networks with NodeXL: Insights from a Connected World.

Vladimir's work has received awards at the International Conference for Weblogs in Social Media and Bits on Our Minds. He has presented his research at academic and industrial campuses all over North America and Europe, including: Xerox/PARC, Microsoft, Colgate University, Northeastern University, UMCP and Oxford University (Oxford Internet Institute). He currently resides in Somerville, MA.

Robert is a Senior Data Scientist at SAS where he builds end-to-end artificial intelligence applications.  He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored a book on computer vision and has developed several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Robert also started a private analytics company while working at North Carolina State University that focused on predicting future home sales. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability.  Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions.  As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Sergei Burkov is the founder and CEO of Alterra.ai, a Deep Learning / NLP startup. He previously co-founded and led three Silicon Valley startups, of which one, Dulance, was acquired by Google back in 2006, where he became the first head of its Moscow R&D Center. He holds PhD in Theoretical Physics and worked at Cornell University.

James Casaletto is studying bioinformatics and biomedical engineering at UC Santa Cruz.  Previously, he worked at MapR Technologies where he designed, implemented, and deployed complete solution frameworks for big data. He has written and delivered courses on MapReduce programming, data engineering, and data science on Hadoop to thousands of students around the world.

Nicola is a Research Scientist at Datavisor, the world’s leading AI-powered Fraud and Risk Platform for enterprises, where he develops deep learning models to fight fraud and online abuse. He uses self-supervised learning, attention, and other state-of-art algorithms to detect content abuse and other malicious activities and protect the experience of other users on the platform.Nicola gained his Ph.D. at the University of Padova in Cognitive Science before moving to Cornell University for a postdoc, where he explored the integration of computational models of neurons within neural networks.

Pranjal helped set up Cisco Innovation Labs after dropping out of his PhD and handles Machine Learning/Product there. He also serves as an Entrepreneur-in-Residence at Vonzos Partners. Previously he conducted ML research at Adobe Research, IBM Research, University of Alberta, Purdue University and Northwestern University. He attended Stanford GSB in the Ignite program and is an On Deck Fellow.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, and Washington DC. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Luba Gloukhova leads and executes advanced machine learning projects for high tech firms and major research universities in Silicon Valley. She also preaches what she practices, serving as the founding chair of Deep Learning World – the premier conference covering the commercial deployment of deep learning – and delivering highly-rated talks at many other events as well. Luba previously supported Stanford faculty as an internal consultant at the university's Graduate School of Business, conceiving and generating innovative solutions to accelerate research.

Before that, Luba gained industry experience in high frequency trading analysis, catastrophe risk modeling, and marketing analytics. She received her master’s in analytics from the University of San Francisco and two bachelors degrees from Berkeley: applied mathematics and economics. Luba also teaches yoga and enjoys an active lifestyle.

Nicolas is a Senior Analytics Expert at McKinsey & Company in Melbourne and the Chief Data Scientist for QuantumBlack Australia. He leads data science teams to extract actionable insights from data in a pragmatic and responsible way across a range of fields, including telecommunications, IoT and financial services. Having more than 15 years of post-PhD experience in research, development and commercialisation of product innovation in big data analytics and predictive modelling, his areas of expertise include: data science, machine learning, analytics transformation programs, DevOps for analytics and ethics in AI. Prior to joining McKinsey, Nicolas led data science at Dun & Bradstreet and analytics at a Big Data start-up in the US. 

Dr Nathan Kirchner's accomplishments resulted in him being named as one of Australia’s Most Innovative Engineers by Australia’s peak engineering body Engineers Australia & as one of Australia's and the US' Top Ten Young Scientists by Popular Science magazine, along with seeing him receive a number of other international awards and recognitions. He is the Founder | CTO at Presien - a cutting edge AI vision systems, a Special Advisor for Robotics | Ventures at one of the worlds largest private construction companies, a Director of the Robotics Australia Group peak body & sits on the Advisory Board of Queensland Robotics. He is an active academic researcher in robotics as an Honorary Professor at the Ohio State University. Previously to this he served multiple academic appointments at Stanford University and the University of Technology Sydney. 

Dr Kirchner's speciality is uncovering and imagining opportunities for emergent robotics & future technologies in the real world and forging viable R&D to Deployment (R&D2D) pathways to their realisation. One of his multi-award winning portfolio projects - Blindsight by Presien (formerly Toolbox Spotter) AI computer vision for heavy industries - has recently evolved into a $7m VC funded spinoff at which he is the Founder | CTO. He has over 15+ years in industry, and 10+ years in academia, initiating, shaping, driving and leading cutting-edge, research driven disruptive innovation.

Ian Knopke recieved his Ph.D. in Computer Science from McGill University, specializing in music search systems. He also worked as a researcher at academic institutions in the US and the UK before joining the BBC as their first data scientist, to work on applied R&D projects for TV, Radio, Sport, News, and the World Service Group. He later worked on data science problems for the Financial Times, Springer Nature, and Elsevier, as well as a couple of AI startups specializing in NLP. Ian joined Thomson Reuters as a Senior Data Scientist in the Reuters Applied Innovation group in 2020 and has also taken on an additional role as a new father in March.

Carter Lin is a Data Science manager at Stripe with a Stanford Ph.D. degree. He manages a team of data scientists to minimize risk and abuse while preserving good user experience and optimizing operations. His team is solving challenging problems in the areas of fraud, credit risk, identity, compliance, account security, operation forecast and optimization with analytics and machine learning.

Patrick Miller is the NYC lead of Google's Enterprise AI team. His team builds scalable, cutting-edge machine learning solutions to internal Google problems. Before Google, Patrick led machine learning at Macmillan, a major trade publisher. He's a core contributor to Cognoma, a cancer genomics ML research tool. Patrick has a Master's in Computer Science from the Georgia Institute of Technology.

Robert A. Muenchen (muenchen.bob@gmail.com) is the author of R for SAS and SPSS Users, and co-author of R for Stata Users and An Introduction to Biomedical Data Science. He is also the creator ofr4stats.com, a popular web site devoted to analyzing trends in data science software, reviewing such software, and helping people learn the R language.

Bob is an ASA Accredited Professional Statistician™ who focuses on helping organizations migrate from SAS, SPSS, and Stata to the R Language. He has taught workshops on data science topics for more than 500 organizations and has presented workshops in partnership with the American Statistical Association, RStudio, DataCamp.com, and Revolution Analytics. Bob has written or co-authored over 70 articles published in scientific journals and conference proceedings and has provided guidance on more than 1,000 graduate theses and dissertations at the University of Tennessee.

Bob has served on the advisory boards of SAS Institute, SPSS Inc., BlueSky Statistics, and the Statistical Graphics Corporation. His contributions have been incorporated into SAS, SPSS, JMP, jamovi, BlueSky Statistics, STATGRAPHICS, and numerous R packages. His research interests include data science software, graphics and visualization, machine learning, and text analytics.

Suhas Pillai is a Deep Learning Engineer at Center for Deep Learning in Electronics Manufacturing (CDLe). At CDLe, his work focuses primarily on applying deep learning in semi conductor manufacturing, where there is data scarcity. His work at the Center has demonstrated that using synthetic data can leverage the potential of applying deep learning in areas where there is a dearth of data. In the past, he has worked in applying deep learning in Speech recognition, NLP and Computer Vision domains. He was associated with couple of startups, where he was responsible for developing Deep learning algorithms for detecting objects in satellite imagery and creating cognitive search engines. He holds a Masters in Computer Science from Rochester Institute of Technology.

Dr. Felix Reinhart studied computer science at Bielefeld University. In 2011, he received a Ph.D. at the Research Institute for Cognition and Robotics (CoR-Lab). Felix was visiting researcher at NASA JPL and Birmingham University. At the Fraunhofer Institute for Mechatronic Systems Design, Felix was Senior Expert for Industrial Data Science. Since 2018, Felix Reinhart is Data Scientist at Miele.

Gowdhaman Sadhasivam is a Senior Computer Vision Scientist at Orbital Insight, Inc. His current work is focused on designing and implementing highly scalable Computer Vision algorithms to understand satellite and aerial imagery. Prior to his current role, he worked as a Senior Data Scientist where he solved Computer Vision and Machine Learning problems and built enterprise products. He also worked as a Software Engineer where he developed and optimized computationally expensive applications. Gowdham received his Master of Science degree in Computer Science from University of Illinois at Chicago, and Bachelor of Engineering in Computer Science and Engineering from Anna University, India.

Aashish Sheshadri has a master's degree in Computer Science from the University of Texas at Austin. He is currently part of strategic machine learning enablement at PayPal. His interests lie in machine learning enablement and research.

Dr. Marc A. Smith is a sociologist specializing in the social organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group. Smith co-founded the Social Media Research Foundation (http://www.smrfoundation.org/), a non-profit devoted to open tools, data, and scholarship related to social media research. He contributes to the open and free NodeXL project (http://nodexl.codeplex.com) that adds social network analysis features to the familiar Excel spreadsheet. NodeXL enables social network analysis of email, Twitter, Flickr, WWW, Facebook and other network data sets. Along with Derek Hansen and Ben Shneiderman, he is the co-author and editor of Analyzing Social Media Networks with NodeXL: Insights from a connected world, from Morgan-Kaufmann which is a guide to mapping connections created through computer-mediated interactions. Smith has published research on social media extensively, providing a map to the landscape of connected communities on the Internet.

Previously Nathan was a data scientist on the Wells Fargo Enterprise Analytics and Data Science team where he led a small team as head of Natural Language Processing (NLP) and Speech Capabilities Development, and was focused on building out Wells Fargo's capabilities in areas related to NLP, deep learning and data science product design. Nathan holds a Masters in Predictive Analytics from Northwestern University and is working on his second Masters in Computer Science from Georgia Tech. He has been with Wells Fargo for the past five years and worked in marketing analytics prior to his current role.

James Taylor is Executive Partner at Blue Polaris and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Yizhar has more than 15 years of experience working as a data scientist (from the time it was still called "statistics"). He worked in many industries and across disciplines: from clinical trials for biotech, behavioural analysis for gaming, fintech consultancy to large scale NLP/image based recommendation systems. Yizhar worked with big data, small data and everything in between. Bayesian by belief, but a big believer in GSD.

Giovanni Turra has been working since 2013 developing Computer Vision, Machine Learning and Deep Learning software solutions in the emerging field of Digital Microbiology Imaging and Data Analysis.

He is part of the imaging team in Copan working both on dozens of already installed automations and on new R&D tools and solutions.

He has a PhD in Technology for Health (from University of Brescia) focusing its research on the application of machine learning and deep learning solutions for analysis of Chromogenic and nonselective media.

Hadrien Van Lierde is a Machine Learning Engineer with WeBank, China’s leading digital bank backed by Tencent. He received his B.Sc. and M.Sc. in Mathematical Engineering from UCLouvain (Belgium), and Ph.D. from the City University of Hong Kong (Hong Kong, China) with research topics ranging from Complex Networks to Social Media Analysis and Natural Language Processing. Now based in Shenzhen, he joined WeBank to contribute to the company’s core mission of providing underbanked individuals and SMEs with high-quality banking services. WeBank’s AI lab is dedicated to the automation of the banking services delivered to its fast-growing base of 200 million customers. In that context, Hadrien’s current work focuses primarily on the development of a multilingual AI-empowered Customer Service System, thereby contributing to the design of cutting-edge Deep Learning models for various NLP tasks. Hadrien’s core areas of interest also include literature, learning foreign languages and traveling the world.

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and domains. He is a published author with books on machine learning and deep learning. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he is working as Analytics Lead in the capacity of a Director at AstraZeneca UK.

Han Wang is the tech lead of Lyft Machine Learning Platform, focusing on distributed computing and training. Before joining Lyft, he worked at Microsoft, Hudson River Trading, Amazon and Quantlab. Han is the creator of the Fugue project, aiming at democratizing distributed computing and machine learning.

Alex Wu is a senior engineer at Nauto where he works on optimizing the vision models that power core safety features like Forward Collision Warning. Before that, he worked on object depth estimation at Deepscale, a self-driving startup that was later acquired by Tesla. Alex completed his computer science degree at UCLA, where he spent a lot of his time at the Ahmanson-Lovelace Brain Mapping Center applying deep learning to 2D and 3D brain segmentation. He is passionate about technologies that improve people's lives, and spends his free time camping and listening to podcasts.

Dr. Jintao Zhang achieved his PhD. in machine learning in 2012. Since then he has been working in various companies as data science, machine learning, and engineering roles, with extensive experience on developing distributed machine learning platforms and providing end-to-end machine learning solutions.