Professional Experience

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.

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.

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).