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Temporally Stable Models for Data Embedding, Regime Detection, and Portfolio Optimization
A chain is only as strong as its weakest link – especially in AI platforms, where robustness must persist through time. We call this requirement Robust Rolling (R2): methods that are not only robust at deployment but remain stable and adaptive as data and regimes shift. Our R2 series strengthens each stage of the AI stack – from embeddings and dimensionality reduction to clustering and regime detection – so the overall engine maintains temporal reliability. Building on this foundation, we introduce an Explainability Index (EI) for portfolio optimization and a practical evaluation framework that increases transparency and consistency in assessing investment funds and managers.
Bio: Ali Hirsa is a professor, director of the Center for Artificial Intelligence in Business Analytics and Financial Technology, and director of the Financial Engineering Program in the Industrial Engineering & Operations Research Department at Columbia University. He is also chief scientific officer at ASK2.ai and managing partner at Sauma Capital, LLC, a New York Hedge Fund. Previously, he was a partner and head of analytical trading strategy at Caspian Capital Management, LLC. Ali has worked in a variety of quantitative positions at Morgan Stanley, DV Trading, Banc of America Securities, and Prudential Securities. Ali was also a fellow at Courant Institute of New York University in the Mathematics of Finance Program from 2004 to 2014. Ali is the author of “Computational Methods in Finance,” second edition, Chapman & Hall/CRC 2024, co-author of “An Introduction to Mathematics of Financial Derivatives,” third edition, Academic Press, and the editor-in-chief of the Journal of Investment Strategies. He is a frequent speaker at academic and practitioner conferences. Ali received his Ph.D. in applied mathematics from the University of Maryland at College Park under the supervision of Howard C. Elman and Dilip B. Madan.