July 2024 – present · Palo Alto, CA
AI Labs Researcher · BlackRock
At AI Labs, I work on problems in finance and operations using convex optimization, statistical modeling, and machine learning.
Recently, I have worked on portfolio optimization and risk modeling. Earlier, I worked on operational-risk forecasting and a system that recommends salary ranges to support regulatory compliance.
I worked full-time from July 2024 through September 2025, then part-time for the remainder of the 2025–26 academic year. The position is full-time for Summer 2026 alongside my teaching role and will return to part-time in September 2026.
June 2022 – March 2024 · Palo Alto, CA
AI Labs Intern · BlackRock
I developed a tax-aware bond portfolio optimization strategy with a custom tax-alpha term and built a backtesting framework in Python, CVXPY, and pandas to evaluate it on both synthetic and real data. In the real-data evaluation, the methodology improved on a baseline ladder-based strategy.
I developed a method for reducing the risk of factor portfolios. In backtests, it improved Sharpe ratios while maintaining returns.
I worked full-time during the summers and part-time during the academic year. Stephen Boyd, Emmanuel Candès, Trevor Hastie, and Mykel Kochenderfer advised me on this work.
June 2021 – August 2021
Student Researcher · SURIM, Stanford University
Through Stanford's SURIM program, I worked in an undergraduate research group on Tokuyama's formula, mentored by Slava Naprienko. Santi Aranguri and I developed a new direct proof of the formula.