I build forecasting models on real-time data: GDP nowcasting across major economies, recession probabilities, and institutional risk, all validated against only what was actually published at the time.
Research analyst at a quantitative asset manager, building econometric and machine learning models for macro forecasting, equity signals, and multifactor risk. Seeking a markets-facing risk or research seat where model-building meets portfolio decisions.
BA in Economics from the University of Rochester (2025). Senior thesis ranked in the top 3 of 75 papers in the department. Current work spans GDP nowcasting across 10 major economies, alternative-data equity signals, and multifactor risk model development.
An end-to-end system nowcasting current-quarter real GDP growth in 10 major economies from point-in-time macro data. Combines a mixed-frequency dynamic factor model with bridge regressions and machine learning in an inverse-variance ensemble. Validated out-of-sample and benchmarked against the Atlanta Fed's GDPNow; outputs directly informed global macro asset allocation decisions. Built as part of my research role at Versor Investments.
A vintage-honest forecasting pipeline built on ALFRED, the Fed's real-time data archive. Estimates US recession probability at nowcast, 3-, 6-, and 12-month horizons with a yield-curve probit, a mixed-frequency dynamic factor model, and a gradient-boosted classifier. Backtested over 27 years and 330 monthly forecasts using only data as published at the time.
View projectA survival-analysis pipeline predicting which US private nonprofit colleges will close 1 to 4 years ahead, built on a panel of ~1,200 institutions from 1998 to 2024 with every feature lagged to its real publication date. Beats the Department of Education's financial-responsibility score from public data alone (ROC 0.85 vs 0.74).
View projectTest-Optional Policies in College Applications: Applicant Behavior Before and After Widespread Adoption
This study investigates how test-optional admissions policies affect applicant behavior and the academic profiles of admitted students, leveraging the natural experiment created by the widespread adoption of these policies during the COVID-19 pandemic. I hypothesize that test-optional policies encourage lower-performing applicants to strategically withhold scores, leading to increased application volumes, higher 25th percentile test scores, and a compressed interquartile range of reported scores.
Using panel data from the Integrated Postsecondary Education Data System (IPEDS) covering 490 U.S. institutions from 2013 to 2023, I estimate difference-in-differences models that compare outcomes before and after policy changes across institutions that adopted test-optional policies at different times.
The results show that test-optional adoption during COVID led to a roughly 40 percentage point decline in score reporting, a nearly 40% rise in applications to highly selective institutions, a 50-point increase in 25th percentile test scores, and a 15-point narrowing of interquartile ranges, with effects that varied by institutional selectivity. In contrast, institutions that adopted test-optional policies before COVID experienced significantly smaller or statistically insignificant changes, suggesting that large-scale adoption, rather than isolated policy shifts, fundamentally reshaped admissions dynamics.