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.

As Chief Executive Officer, Swati Abbott is leading the effort for Blue Health Intelligence (BHI®) to leverage data insights to develop innovative analytics solutions. This strategy includes extending the company's reach to external audiences, while enhancing BHI's value to Blue Cross and Blue Shield companies.

Prior to joining BHI, Abbott served as President of Elsevier/MEDai, an industry leader in predictive analytics, where she provided leadership and strategic vision to position the company at the forefront of the healthcare industry. Before MEDai, Abbott was Managing Director for the Medical Management Strategic Business Unit at ViPS, where she led strategic solutions development for medical management data warehousing and HEDIS reporting for healthcare payer clients. Abbott was also involved in leading SoftMed Systems, Inc. to create and provide solutions for hospitals and integrated delivery networks in the key areas of medical chart abstraction, quality assurance, utilization management, risk management and regulatory reporting.

As a respected industry leader, Abbott is a regular speaker at industry conferences, presenting on topics related to predictive modeling, medical home and healthcare clinical analytics. She is also a contributor and member of the editorial board for Predictive Modeling magazine and has been quoted in notable industry publications regarding the use and future of predictive analytics, the state of healthcare analytics and quality improvement initiatives. In 2010, Frost and Sullivan recognized Abbott as one of the Movers and Shakers in healthcare.

Abbott holds a Master of Science in computer science and a Bachelor of Science in physics from Delhi University, India.

Ms Veronique Duverneuil is the Director of Global Brand Analytics Team within the Global Brand Protection Organization at Hewlett Packard Company. She is managing a worldwide team of professional program managers and analysts specialized in the detection and minimization of fraud, in the Services, Sales and Supply Chain areas.

Ms Duverneuil has been with Hewlett-Packard for 24 years where she had various positions, in finance, IT, recycling business, Channel program management and Compliance & Audit, prior joining Global Security Services (GSS) in 2010.

Ms Duverneuil holds a Business Administration Degree from the Grenoble Ecole de Management Business School in France, with a major on new technologies.

Ms Duverneuil is married. She resides in Grenoble, France.

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.

Robert Grossman is a partner at Analytic Strategy Partners and a professor at the University of Chicago.  From 2002 to 2016, he was the Founder and Managing Partner of Open Data Group, which provided data science consulting services to a wide variety of companies, including those in financial services, location services, computational advertising and cybersecurity. From 1996 to 2001, he was the Founder and CEO of Magnify, which developed predictive analytic software for the financial services and computational advertising industries.  Magnify was sold to ChoicePoint in 2003 and is now part of the RELX Group.  He is the Frederick H. Rawson Professor of Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science at the University of Chicago, where he leads a data science research group that is developing systems and algorithms for managing, analyzing and sharing large biomedical and environmental datasets.

Tom Kern is a Risk Modeling Manager at Paychex, Inc. Under the Risk Management umbrella, Tom helps to coordinate and execute a wide range of projects centered on predictive modeling, optimizing processes in all departments from sales strategy to internal operations and mitigating risk throughout the company.

Tom joined Paychex in 2012. Prior to Paychex, Tom was a Predictive Modeling Analyst with a large digital marketing agency, servicing major clients in the financial services, insurance, and automotive industries. He holds a MA from Boston University in Applied Statistics, and a BA from Boston University in Applied Mathematics. Tom is a four-time PAW speaker.

Mahesh is the Founder and CEO of Tiger Analytics. He started Tiger Analytics with a desire to bring his experience in management science to help organizations achieve superior performance through the application of advanced analytics.Before founding Tiger Analytics, Mahesh was on the faculty of the Smith School of Business and Rutgers Business School.

Mahesh has conducted research in the areas of data mining and statistical modeling and has successfully applied his research to solve problems related to forecasting, pricing, promotions, and customer segmentation for a wide range of businesses across industries. Mahesh holds a Ph.D. in Operations Research and Marketing from MIT, and a B.Tech in Computer Science from IIT Bombay.

Wei Liang, Ph.D., has been a Risk Modeling Analyst in the Modeling & Risk Review group at Paychex, Inc. since January 2011. His work involves analyzing the outcome of regular and strategic business activities, providing insight and assessment for marketing, up-sell, retention and risk-management efforts, creating predictive models to add business intelligence.

With a deep understanding of statistical methods and being well versed in the statistical software such as SAS, R and Stata, Wei plays an important role in the effort for compiling the Paychex American Small Business Index.

He is also highly skilled in methods such as Monte Carlo Simulations and Time Series Analysis. Prior to Paychex, he held post doctoral positions in several universities and conducted research in laser and material science.

Wei holds a Ph.D in Physics from Arizona State University, a B.S. in Physics from Sichuan University in China and a M.A. in Biostatistics from the University at Buffalo.

KV Nathan with closer to three decades of knowledge assimilated experience has pioneered the Supply Chain, Process Transformation, Analytics Delivery, Project Management and People Management across Singapore and India.

KV Nathan is the Senior Delivery Manager, Global Analytics at Hewlett-Packard Company and is responsible to create Analytics capability in Warranty and Customer Service Domain. As part of this role, KV Nathan is responsible for delivery of analytics related to customer service and warranty within Global Analytics.

Before this, Nathan was part of HP Singapore holding APJ role and has executed numerous process improvement projects for cost savings in Supply Chain, Logistics, Returns Management , Quality management ,Customer support and Warranty.

Prior to HP, Nathan was holding the position of Project and Quality Manager in one of the leading Power Company in India and shifted to Oil & Gas Manufacturing Sector in Singapore. He transformed the Process Systems and was solely responsible for getting ISO 9001: 1994 Certifications for the Oil & Gas Company in Singapore.

Nathan has graduated from Faculty of Engineering and Technology of Annamalai University, India. He has obtained a Lead ISO Auditor, Six Sigma trained and Consultant of SAP S&D Module.

Nathan possesses a clear understanding of the Supply Chain & Warranty industry, technology trends with the distinction of instituting new practices to achieve superior business excellence at the lowest overall cost.

Claudia Perlich leads the machine learning efforts that power Dstillery's digital intelligence for marketers and media companies. With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world.

Claudia is the past winner of the Advertising Research Foundation's (ARF) Grand Innovation Award and has been selected for Crain's New York’s 40 Under 40 list, Wired Magazine's Smart List, and Fast Company's 100 Most Creative People.

Claudia holds multiple patents in machine learning. She has won many data mining competitions and awards at Knowledge Discovery and Data Mining (KDD) conferences, and served as the organization's General Chair in 2014.

Prior to joining Dstillery in 2010, Claudia worked at IBM's Watson Research Center, focusing on data analytics and machine learning. She holds a PhD in Information Systems from NYU.

Dr. Nalini Polavarapu has been with Monsanto for over ten years, and manages a global team of analytical and IT professionals, specialized in machine learning, operations research and cloud analytics to deliver better products to market faster through data science. Data science activities range from inventing cognitive systems to enhance decision making, predictive and prescriptive analytics to automate/augment decisions, geo-spatial and image analytics to realize precision farming, and analytics to increase operational efficiency.

Nalini also serves as the senior leader on the Data Science Center of Excellence Council partnering with other senior leaders enterprise wide to drive the efforts to realize the vision of transforming into a digital company through data science. Activities include implementing an enterprise wide talent and community development strategy, scalable technology platforms, best practices for development and deployment of analytical solutions and portfolio management.

Over the course of her career, Nalini held positions of increasing levels of leadership responsibilities managing teams operating across geographies in Americas, EMEA, Asia and Africa, and has successfully established and led an off-shore center in India. She also initiated multiple external partnerships and collaborations with start-ups, large corporations and universities. Being the first data scientist at Monsanto, Nalini played a key role in building a world-class global data science organization which is now at 100+ and growing through hiring and development of data scientists, data science leaders, as well as positions of seniority within the groups.

Nalini is the recipient of highest awards in technology for four consecutive years and a Fellow.

Nalini has a PhD and dual Masters in Computer Science and Bioinformatics from Georgia Institute of Technology in Atlanta, Georgia. Nalini has authored and co-authored several analytical patents, research articles in leading scientific journals and co-authored book chapters on high throughput data analysis and applications.

Mukund Raghunath is the Geography Head for Mu Sigma. He has over 11 years of Engineering and consulting experience with a leading telecommunications company and a leading Sales and Marketing Strategy firm in the US. He has helped several Fortune 500 companies in the Pharmaceutical and Healthcare space address a broad range of business issues. He also has extensive experience in product planning, development and management in the telecom sector.

Mukund has a Masters Degree in Computer Science from the University of Illinois and an MBA with honors from the University of Chicago, Graduate School of Business.

Eric Siegel, Ph.D., is a former Columbia University professor who helps companies deploy machine learning. He is the cofounder and CEO of Gooder AI, the founder of the long-running Machine Learning Week conference series, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. A Forbes contributor, Eric publishes op-eds on analytics and social justice.

Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. A Forbes contributor, Eric and his books have been featured in BBC, Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

Marco Vriens has a PhD in marketing analytics and is a globally recognized expert on advanced analytics and marketing research.  He has applied a wide range of analytical methodologies including conjoint, regression, marketing mix models, brand models, switchable consumer, segmentation, audience measurement and optimization, text analytics, machine learning, multi-dimensional scaling, etc.

He is the author/editor of 3 books: The Insights Advantage: knowing how to win (2012), Handbook of Marketing Research (Sage, 2006), and Conjoint Analysis in Marketing (1995).  He published more than 50 papers in both academic journals and industry publications, including the Journal of Marketing Research, Marketing Science, Marketing Insights magazine, etc. His new book: From Data to Decision: Handbook for the Modern Business Analyst will appear in the Fall of 2018.