When a series and its time reversal are statistically distinguishable, the arrow of time reveals what drives what — the idea behind my work on directional irreversibility.
I lead research that moves from rigorous finance to decisions that matter — in mortgage and real estate markets, and in how institutions put AI to work.
My career has run on two tracks at once: research that meets the standards of top finance journals, and leadership of teams that turn that research into systems institutions rely on.
As an academic, I study household and consumer finance, mortgage markets and real estate, with publications in the Journal of Financial and Quantitative Analysis, the Journal of Economic Dynamics and Control and the Quarterly Journal of Finance. I teach finance, real estate and quantitative methods at Columbia, NYU and Yeshiva University, and previously held faculty appointments at the University of Miami and CU Boulder.
In industry, I led AI and quantitative research at Franklin Templeton in private equity and commercial real estate, one of the largest U.S. real estate investment managers, building agentic AI, LLM and retrieval systems and owning AI in production. Before that I spent a decade in mortgage credit, cross-asset research, loss forecasting and structured credit at Rocktop Partners, Nuveen/TIAA, UBS, Santander and Deloitte.
Selected work. Full list and earlier versions on SSRN.
Applies the Directional Irreversibility diagnostic to identified shocks in asset pricing, asking not only whether a shock moves prices but how long its footprint remains directional.
Ginnie Mae issuers may buy seriously delinquent loans out of MBS pools at par. Conditional on default, a one-percentage-point higher interest rate spread raises the probability of an early buyout by 7–9 percentage points: issuers select the loans most valuable when they reperform, at the expense of MBS investors, and buyout activity depends on issuers' access to capital.
Model-free reinforcement learning is used to derive how a mortgage servicer should act toward a borrower, replacing heuristic industry rules. Using post-securitization soft information and a new measure of borrower responsiveness, the servicer learns the borrower's type dynamically, anticipating strategic behavior and raising cooperation.
Building research functions and putting models and AI into production.
Led AI and quantitative research at one of the largest U.S. real estate investment managers. Built agentic AI, LLM/RAG and knowledge-graph document systems, and owned AI in production.
Mortgage credit and Ginnie Mae early-buyout models.
Cross-asset quantitative research.
Loss forecasting, structured credit and derivatives.
Undergraduate, MBA, MSF and MSRE programs.