Roger M Stein

Roger M. Stein has worked for over 35 years leading quantitative and research groups at major financial firms and start-ups while also conducting a broad academic research and teaching agenda at MIT,  NYU, Hebrew University and elsewhere.  He  has started and helped found several companies (including BridgeBio which IPOed in 2019), and he is currently advising a portfolio of health, finance, social-media and data-driven start-ups.

Dr. Stein has held senior leadership positions in several global financial firms, including serving as President, Moody’s Research Labs;  Chief Analytic Officer, State Street GX; and Head of Research at Moody’s KMV.   The quantitative and AI tools, models and methodologies that he and his teams have developed over the past three decades have become industry standards.  They are in use today at a hundreds of global financial firms and have formed the basis for financial regulation and congressional legislation.

He is also the recipient of the 2019 Harry Markowitz Award, and his TED Talk, A bold new way to fund drug research has been viewed almost 1,000,000 times.  In addition to his technical publications, Dr. Stein’s  products, research and innovative analytic techniques have been featured in popular media outlets including Bloomberg, The Economist, The Financial Times, Forbes, Globes, The Jerusalem Post, Scientific American, The New Yorker, and WIRED.

Dr. Stein  is also currently on the faculty at both the NYU Stern School of Business where he teaches finance and AI, and at the NYU Center for Data Science where he teaches applied machine learning and analytics at the graduate level.  Previously, he was a Senior Lecturer in Finance at the Sloan School, MIT, a researcher at the MIT Laboratory for Financial Engineering and a researcher at The Center for Risk Management Research and UC Berkeley. Periodically, he has taught as a visiting professor at Hebrew University.

Dr. Stein’s  academic and commercial work focuses on a broad set of topics in quantitative and human decision making.  These include:

– financial risk management
– machine learning
– AI
– health finance
– consumer healthcare analytics
– FinTech and FinTech strategy
– stochastic simulation
– quantitative model validation
– systemic risk
– quantitative analysis of physical security risks.

In addition, he has done extensive work in biomedical finance including pioneering work developing Research Backed Obligations for funding risky, long-shot investments; in addition to as research in epidemiology,  health policy and clinical trial design.   He serves on the editorial boards of several finance and data science journals.

Dr. Stein has published over eighty professional and peer-reviewed academic articles and open-source packages.   His work is often interdisciplinary and has been published in top-tier journals in a range of disciplines including Science, Finance, Medicine, Machine Learning and Computer Science.  His publications have been cited thousands of times by researchers across a variety of fields. He has also co-authored two full-length texts on applied analytics: Active Credit Portfolio Management in Practice  (Wiley, 2009), on practical credit risk management; and Seven Methods for Transforming Corporate Data into Business Intelligence (Prentice Hall, 1997), one of the first applied business texts on developing commercial applications of machine learning, AI and data analytics.   He is a frequent invited speaker at industry, academic and regulatory venues and has also published in popular media including Scientific American and the Wall Street Journal.

In addition to his professional work he was the founder and president of the Consortium for Systemic Risk Analytics at MIT and served on the Board of PlaNet Finance, USA. He is a member of the Advisory Council of the Museum of Mathematics and on the Academic Advisory Board of the EC’s SYstemic Risk Tomography Project (SYRTO).

Dr. Stein holds  Ph.D. and Masters degrees from the Stern School of Business, New York University, and has been practicing Aikido since 1980.