Experience
Current Work and Research
AI Consultant at BlackRock (June 2022–Present)
I am currently finishing work on a tax-aware portfolio optimization project. For this project, I developed a statistical model to generate realistic synthetic data for backtesting. I also developed a backtesting suite, including a tax computation module, written in Python and primarily with Pandas. For backtesting purposes, the portfolio optimization is done using CVXPY.
I have also consulted on a project intended to reduce portfolio risk. To this end, I presented a methodology that demonstrably reduced risk without sacrificing returns.
Research (June 2023–Present)
I recently finished collaborating with Nikhil Devanathan and Stephen Boyd on a paper about an efficient method to attribute least-squares model performance to features using Shapley values. I am currently working on new projects in optimization and statistics.
Past Work and Research
Teaching Assistant for EE 364A, Convex Optimization (January 2023–March 2023)
I served as a teaching assistant for EE 364A, a large graduate class of 235 students. I provided individual guidance to students having trouble with the material through detailed responses on online class forum and office hours. I also designed additional course content outside of class and contributed two problems to the final exam. Additionally, I aided with course logistics.
Student Researcher at Stanford Undergraduate Research Institute in Mathematics (June 2021–August 2021)
During SURIM, I collaborated with Santi Aranguri, Chavdar Lalov, and Robin Truax under the guidance of a graduate student mentor, Slava Naprienko. We successfully developed two novel proofs for Tokuyama's formula, a combinatorial formula for a function important in representation theory. The paper, though unsubmitted, is available here.