Professional Experience

Microsoft Research New England. Research Intern. May 2022 – August 2022, Cambridge, MA.

Worked with Markus Mobius, Nicole Immorlica, and Brendan Lucier on two models of memory and recollection. Our first model studies how well a decision maker can estimate an underlying state by selectively recalling various anecdotes arriving over time (signals drawn from the conditional distribution) under a memory constraint. Our second model provides a micro-foundation for the recollection of anecdotes as opposed to statistics (lower-dimensional representations of many anecdotes) by characterizing when remembering samples from the distribution is more advantageous for answering unanticipated queries (“unknown unknowns”).

Board of Governors of the Federal Reserve System. Dissertation Fellow. June 2019 – September 2019, Washington, DC.

Collaborated with economists in the Systemic Financial Institutions and Markets section at the FRB. During my tenure, I finished my paper (joint with D. Acemoglu, A. Ozdaglar, and A. Tahbaz-Salehi) on Systemic Credit Freezes in Financial Lending Networks and began working on three separate (but related) projects: (i) dynamic regulation of financial networks under uncertainty, (ii) systemic risk in derivatives trading networks, and (iii) measuring interconnectedness and liquidity bottlenecks. Advised by Celso Brunetti.

Citadel, LLC. Quantitative Researcher. June 2016-August 2017, Chicago, IL.

Focus of internship was on analysis of IMS health and other healthcare data (including FDA, insurance provider, and clinical trial data) for healthcare sector equity strategies and equity option volatility and momentum strategies. Additional projects involved: (i) sentiment analysis of Bloomberg news and readership data, and (ii) technical signals for cointegrated time series in “pairs-trading” type strategies. Advised by Bryan Landman.

Morgan Stanley. Quantitative Trading Intern. June 2015-August 2015, New York, NY.

Rotation through four teams: index derivatives, exotic derivatives, automated market-making (AMM), and single-name options (SNO). Projects included implementing local and stochastic volatility models for exotic derivative pricers, dimension reduction for market-making data to infer meaningful statistics for historical AMM quotes and fills, and network-based alpha models for single-name stocks. Advised by Amir Khandani.

Weiss Asset Management. Trading Intern. June 2014-August 2014, Boston, MA.

Worked on project involving: (i) fundamental analysis, (ii) statistical arbitrage, and (iii) statistical learning models. Examples include: (i) valuation of closed-end funds and convertible bonds, in both domestic and foreign markets, (ii) event-driven statistical models in primary and secondary equity offerings, and (iii) Bayesian forecasting models of the holdings of closed-end funds based on monthly updates to net asset value (NAV). Advised by Anthony Genello.