Speakers

Kay Giesecke (Stanford)

Kay Giesecke is Professor of Management Science & Engineering at Stanford University and the Paul Pigott Faculty Scholar in the School of Engineering. He is the Director of the Advanced Financial Technologies Laboratory and the Director of the Mathematical and Computational Finance Program. Kay is a member of the Institute for Computational and Mathematical Engineering.

Kay is a financial engineer. He develops stochastic financial models, designs statistical methods for analyzing financial data, examines simulation and other numerical algorithms for solving the associated computational problems, and performs empirical analyses. Much of Kay's work is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance. His research has been funded by the

National Science Foundation, JP Morgan, State Street, Morgan Stanley, American Express, and several other organizations.

Igor Halperin (Fidelity)

Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience 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 experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining NYU Tandon, Igor was an Executive Director of Quantitative Research at JPMorgan, and before that he worked as a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has also co-authored the book “Credit Risk Frontiers” published by Bloomberg

LP. Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. He advices a several fintech and data science start-ups and risk management firms.

Marcos Lopez de Prado (AQR Capital Management)

Marcos López de Prado has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He has recently sold his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he developed high-capacity investment algorithms that consistently delivered superior risk-adjusted returns, receiving up to $13 billion in assets.

Concurrently with the management of investments, between 2011 and 2018 Marcos was a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, and SSRN ranks him as the most-read author in economics. Among several monographs, Marcos is the author of the graduate textbook Advances in Financial Machine Learning (Wiley, 2018).

Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a financial machine learning course at the School of Engineering. In 2019, Marcos received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.