Dean Abbott is President of Abbott Analytics and currently is the Bodily Bicentennial Professor in Analytics at UVA Darden School of Business. He is an internationally recognized thought leader and innovator in data science and predictive analytics with more than three decades of experience solving a wide range of private and public sector problems. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).

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.

Muneeb Alam is a data science specialist at QuantumBlack, a McKinsey Company. He has experience in the public and social sector, healthcare, energy and basic materials. He holds a B.A. in astrophysics from Columbia University and an MSc. in business analytics from Imperial College Business School.

Meghan joined Spencer Stuart in July 2018 leading data and analytics for the firm. She is responsible for creating and implementing a strategy and roadmap to advance the data and analytics capabilities across Spencer Stuart, focusing on delivering greater impact to our clients and for the firm. Her background is in physics and data science and she has extensive experience helping organizations leverage data and analytics to improve their businesses. Meghan likes to share her passion for data, science, diversity and analytics including by regularly speak at industry and scholastic events catered to attracting and retaining the next generation of the workforce. She has also been involved in a range of diversity and inclusion initiatives; among these, as the Co-founder and Co-Chair of the Women in Actuarial & Analytics group at Travelers, the first Diversity Business Network at Travelers, a group dedicated to the recruitment, retention, and advancement of women in analytical roles and winner of the 2011 NALC Above-and-Beyond Award. 

Ashish manages Events recommendations and trends at Twitch. He uses AI/ML to generate insights from vast amounts of data and build interesting B2C, B2B and enterprise products. He has over 20 years of experience in technology ranging from founding startups to large companies. He has an MBA in marketing from Kellogg School of Management and a B.Tech from IIT BHU, India.

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.

Steve Bishop is a Data Scientist for Dow Jones & Company, Inc. in Princeton, NJ. Steve has developed a variety of analytical tools for internal stakeholders, and has promoted the value of data-driven insights across the company’s B2B Sales and Product teams through customer risk and user segmentation models. He has a BSBA from the University of Miami with a concentration in Management Science.

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.

Valerie Carey joined Paychex in 2018, and focuses on exploratory analysis and client-facing analytics.  Prior to Paychex, Valerie was employed as a data scientist and a business analyst in healthcare related fields.  In addition, she has worked writing automated tests for a Unix operating system, and has a PhD in biophysics from Cornell University. 

While enjoying the fun of data munging and the joy of discovery, Valerie’s deepest passion is building trust in data products and processes.  She prefers a comprehensive approach to data projects, emphasizing education and communication, automated testing, and iterative feedback.  Valerie believes that explainable AI is just one part of a journey to a comprehensible and useful model. 

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.

As SVP of Data Analytics for Altair, Mark is responsible for the global strategy for the Data Analytics business unit. This includes sales, pre-sales, support, consulting/services and product guidance. Altair's Data Analytics suite is focused on helping organization leverage AI and machine learning to make better business decisions.Mark possesses over 15 years of sales experience with a distinct focus on business intelligence, data mining and analytics. His deep understanding of Predictive Analytics and Machine Learning coincide with his need to nurture and maintain strong business relationships. He is passionate about collaborating with different organizations to discover the best solution to their specific challenges across multiple channels.

Matt Eckert is a Sr. Data Scientist at Marriott International applying Machine Learning to the digital domain. He has also help build out the Advanced Data Science platform for development and activation of models. He holds a B.S. in Computer Science from University of Maryland- College Park and M.S. in Computer Science from University of Maryland- Baltimore County

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. 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.

Bill Franks is the Director of the Center for Statistics and Analytical Research at Kennesaw State University. He is also Chief Analytics Officer for The International Institute For Analytics (IIA) and serves on several corporate advisory boards. Franks is also the author of the books Winning The Room, Taming The Big Data Tidal Wave, The Analytics Revolution, and 97 Things About Ethics Everyone In Data Science Should Know. He is a sought after speaker and frequent blogger who has over the years been ranked a top global big data influencer, a top global artificial intelligence and big data influencer, a top AI influencer, and was an inaugural inductee into the Analytics Hall of Fame. His work, including several years as Chief Analytics Officer for Teradata (NYSE: TDC), has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations.  You can learn more at http://www.bill-franks.com.

Mikhail Golovnya has been prototyping new machine learning algorithms and modeling automation for the past 20 years. He has been a major contributor to Salford Systems / Minitab’s on-going search for technological improvements to among the most important algorithms in Machine Learning: CART® Decision Trees, MARS® Non-linear Regression, TreeNet® gradient boosting, and Random Forests®. Mikhail has presented at multiple conferences and seminars. He has also been teaching the mathematical foundations and applications of major predictive learning algorithms, both classical and modern. Having two master’s degrees, one in rocket science from Kharkov State Polytechnic University (Ukraine) and another in statistical computing from the University of Central Florida (Orlando), he currently serves in the role of Senior Advisory Data Scientist and is leading the next generation of Minitab machine learning product development. 

Peter Grabowski is a longtime Googler and former Nest employee. He's currently the manager of the Enterprise Machine Learning team in Austin. Previously, he managed a data engineering team at Nest and helped build the Assistant for Kids team at Google. Outside of Google, he teaches machine learning as part of UC Berkeley's Master's in Data Science, and is a managing partner of PXN Residential, LLC.

Eui-Hong (Sam) Han is Vice President, Advanced Data Science at Marriott International. Previously he was Director, Data Science & AI at The Washington Post where his team built tools and services to provide personalized customer experience, to empower newsroom with data-driven decisions and to provide targeted advertising capability. His expertise includes AI, machine learning, data mining, information retrieval and high-performance computing. He holds PhD in Computer Science from the University of Minnesota.

Matt Klubeck is a Data Scientist for Dow Jones & Company, Inc. in Princeton, NJ. He leverages data modeling to surface actionable insights for strategic B2B initiatives, including optimizing resource allocations in the business and reducing customer churn. Matt has also led Dow Jones’ Data Academy courses in data visualization and Python. He received his Bachelor’s degree in Mathematics from The College of New Jersey and a Master’s in BI & Analytics from Saint Joseph’s University.

Anastasiia Kornilova is a Machine Learning Scientist at Booking.com. In the last two years at Booking.com, she worked on the personalization of the website to the user needs (vacation rentals segment ) with a focus on a machine learning based quality rating system for alternative accommodations. Prior to Booking.com, Anastasiia has five years of experience in applying ML in various domains: healthcare, e-commerce, hospitality. Her main research interest is ML explainability in production.

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation's first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management.

Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government.

Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Richard Lee is Director of Data Science for John Hancock, the U.S. division of Toronto-based Manulife.

Richard leads the Advanced Analytics & AI group, which supports operations decision analytics across all U.S. businesses.

In his current role, Richard finds opportunities for efficiencies in Life Insurance as well as Long Term Care Insurance operations. Much of his focus is on innovation to enhance the relevance and understanding of analytics and its impact on operations decision-making.

Prior to his current role, Richard has spent 15 years at John Hancock in various analytics roles

Andrey is a Technical Lead Manager at Facebook.

During his career he had a chance to be participate in building multiple generations of ranking and recommendation models as well as Facebook AI Personalization Platform.

His interests include Artificial Intelligence, High Performance Computing, Graph Learning and Personalization.

Jaya Mathew is a Senior data scientist at Microsoft where she is part of the Artificial Intelligence and Research team. Her work focuses on the deployment of AI and ML solutions to solve real business problems for customers across multiple domains. Prior to joining Microsoft, she has worked with Nokia and Hewlett-Packard on various analytics and machine learning use cases. She holds an undergraduate as well as a graduate degree from the University of Texas at Austin in Mathematics and Statistics respectively.

Keith McCormick is an independent data miner, trainer, speaker, and author. For the last several years, his  emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. Keith is skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. He specializes in predictive models and segmentation analysis including classification trees, neural nets, general linear model, cluster analysis, and association rules.

Natalia Modjeska is a Research Director at Omdia (part of Informa Tech) where she leads the team of analysts covering Artificial Intelligence and Intelligent Automation from processors and software to enterprise deployments.

Natalia’s journey into AI started in the late 1990’s with a PhD in NLP from the University of Edinburgh in Scotland. Since then she has worked in range of roles developing, deploying and evangelizing analytics and AI. Her diverse career includes R&D, product and program management, sales, consulting, and client advisory. She has worked with organizations of all sizes and levels of maturity across many industries and geographies helping clients to harness the power of data, advanced analytics and AI for transformative change. In the past four years she has advised 200+ organizations around the globe on topics ranging from strategy and use cases, to execution, best practices, governance, ethics, emerging trends, and vendor due diligence.

Natalia is passionate about helping clients to demystify AI/ML, deploy these technologies responsibly and achieve sustainable business benefits. As part of this effort, she also serves as an AI expert on the ISO/IEC JTC 1/SC 42 - AI Standards working group and volunteers with several non-profits to develop responsible AI certification; and to increase AI literacy and improve government through innovative technologies.

Robert A. Muenchen () 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.

Haig R. Nalbantian is a Senior Partner at Mercer and a founder/leader of Mercer's Workforce Sciences Institute. A labor /organizational economist, he has been instrumental in developing Mercer's unique capability to measure the economic impact of human capital practices. Those capabilities have been applied in numerous projects he has directed for leading companies in the U.S. and abroad across a broad range of industries, including energy, high technology, manufacturing, consumer products, financial services, media and information services, telecommunications, and professional services. He has also consulted to organizations in the public and not-for-profit sectors. In recent years, Haig has worked extensively with high-profile organizations in the Middle East, with particular focus on strategic workforce planning, workforce strategies and metrics.

Haig came to Mercer from National Economic Research Associates which he joined in 1989. Earlier, he was on the faculty of economics at New York University and was a research scientist at its C.V. Starr Center for Applied Economics. He is an internationally recognized expert in incentives, human capital measurement and management and their links to organizational performance. He has published widely on these topics in books and articles in leading academic and professional journals, such as the American Economic Review, The Journal of Labor Economics, The Harvard Business Review, Compensation and Benefits Review, WorldatWork, among many others. His HBR article, "Making Mobility Matter," received the Academy of Management's 2010 award for "Outstanding Practitioner-oriented Publication" in 2009.

Nalbantian co-authored the prize-winning book on human capital measurement and management, Play to Your Strengths (McGraw Hill, 2004). He is also editor of and chief contributor to the book, Incentives, Cooperation and Risk Sharing and is a frequent speaker before industry groups, professional associations and academic audiences across the globe. He led the research team and co-authored the 2012 World Economic Forum/Mercer study of global talent mobility, "Talent Mobility Good Practices: Collaboration at the Core of Driving Economic Growth."

Haig earned his BA in English and Economics at New York University and his graduate degrees in economics from Columbia University. He is a member of the American Economic Association.

Kumaran Ponnambalam is a technology leader with 20+ years of experience in AI, Big Data, Data Processing & Analytics. His focus is on creating robust, scalable AI models and services to drive effective business solutions.  He is currently leading AI initiatives in the Emerging Technologies & Incubation Group in Cisco. In his previous roles, he has built data pipelines, analytics, integrations, and conversational bots around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in AI and Big Data.

Nick is the Director of Data Science at Cape Fear Collective, a non-profit which supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. Prior to CFC, he honed his data science and consulting skills in the marketing analytics space, transforming the way Fortune 500+ companies (Lowe's Southwest Airlines, P&G, and many others) think about their customer strategy and value proposition. He lives in Raleigh, North Carolina.

Thomas Schleicher is a results-focused executive with nearly 20 years of experience in delivering actionable, data-based insights and profit-optimizing results for various high-profile clients across multifaceted, competitive industries. He is currently Senior Director of Measurement Science at National Consumer Panel, a joint venture of Nielsen and IRI. NCP is the largest longitudinal consumer panel in the world, and it provides the quality data its parent companies leverage to share consumer insights with their respective clients.

Prior to his current role, Tom has had stints at Ipsos-ASI, Bayer HealthCare (Pharmaceuticals), and smaller analytic shops including Symphony Marketing Solutions and Spire, a Loyalty Marketing Analytics firm. Trained as an Experimental Social Psychologist (Ph.D. earned at the University of Wisconsin - Milwaukee), he is currently augmenting his statistical and social research methods expertise as he nears completion of his online Certificate in Predictive Analytics at UC-Irvine.

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.

David Stephenson has over 20 years of experience leading analytics initiatives across a wide range of industries, including as Head of Global Business Analytics at eBay Classifieds Group. In addition to consulting for dozens of companies, including Adidas, Miro, ABN Amro, and Sky Germany, Dr. Stephenson also serves as part time faculty at the University of Amsterdam Business School and has developed and delivered data science trainings for hundreds of analytics professionals around the globe.

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.

Martin Szugat is founder and managing director of Datentreiber, a data strategy consultancy supporting companies to digitally transform to a data-driven business. He invented the Data Strategy Design method and provides a free Data Strategy Designkit. Prior to Datentreiber, Martin Szugat was managing director of SnipClip, an agency for social media marketing & analytics. With a degree in bioinformatics, he has conducted research in machine learning and worked as a freelance software developer, consultant & author. Since 2014, he is responsible for the Predictive Analytics World & Deep Learning World conferences in Europe as program director.

James Taylor is the CEO of Decision Management Solutions 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.