Organizers
Dr. Igor Halperin is an AI researcher and a Group Data Science leader at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, and portfolio optimization. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering.
Dr. Svitlana Vyetrenko is an Executive Director at J.P. Morgan AI Research leading a team that focuses on generative time series models, multi-agent simulations and reinforcement learning. She is also a Lecturer at the University of California at Berkeley. Svitlana was previously a Vice President at JP Morgan Macro Linear Quantitative Research, and an Associate at Goldman Sachs Equity Strategies. She holds a PhD in Applied and Computational Mathematics from California Institute of Technology.
Mr. Thomas J. De Luca is a Senior Researcher, investor behavior, in Vanguard’s Investment Strategy Group, responsible for conducting research on investor preferences and decision-making and applying behavioral insights to real-world settings. His expertise includes financial modeling, data analysis, and investor behavior. Tom’s research examines the behavior of self-directed individual investors, including retirement withdrawal trends, ESG (environmental, social, and governance) fund usage, and investor reactions to the COVID-19 pandemic. Prior to joining Vanguard in 2014, Tom served as a captain and meteorologist in the U.S. Air Force. Tom holds a B.A. in mathematics from Cornell University, M.S. degrees in applied mathematics and meteorology from the Naval Postgraduate School, an M.B.A. with distinction from the Kellogg School of Management, and an M.P.S. in data analytics from Pennsylvania State University.
Prof. Alberto Rossi is a Professor of Finance at the McDonough School of Business, Georgetown University. He is also the Director of the AI, Analytics, and Future of Work Initiative at Georgetown and an Academic Fellow of the Luohan Academy. His research interests include FinTech, Household Finance, Machine Learning, and Asset Pricing. His recent work studies how robo-advisors can help individuals make better financial decisions and how to predict stock market returns using machine learning algorithms. He has worked extensively in analyzing big data, has collaborated with major brokerage houses, FinTech firms, and asset managers around the world. Before McDonough, he was an Associate Professor with tenure at the R.H. Smith School of Business, University of Maryland. He also worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his Ph.D. in Economics from the University of California, San Diego.
Prof. Yongjae Lee is an Assistant Professor in the Department of Industrial Engineering at Ulsan National Institute of Science and Technology (UNIST). Dr. Lee utilizes quantitative techniques such as ML/AI and optimization to analyze financial data and derive optimal decisions. He is particularly interested in analyzing individual and household financial activity to draw useful insights and design customized services. Dr. Lee is an advisory editorial member for the Journal of Financial Data Science, and has applied ML/AI techniques to develop financial services with through projects with several companies. He received his B.S. degree in computer science and mathematical sciences and Ph.D. degree in industrial and systems engineering from KAIST.
Prof. John R.J. Thompson (Lead Organizer) is an Assistant Professor at the University of British Columbia whose areas of expertise are nonparametric and applied statistics and machine learning. John is a member of the Financial Wellness Lab at Western University which aims to design data-driven tools that Canadians can use to improve their financial resilience, reduce financial stress, and support better financial decisions. John's current applied research in finance includes modelling the behaviors of Canadian investors under the guidance of financial advisors, and designing effective financial measures and Robo-tools that aid financial advisors in supporting their clients’ investment portfolios.