I am a PhD Candidate in Operations Research at the Massachusetts Institute of Technology (MIT) advised by Prof. Swati Gupta. My research focuses on optimization and learning for high-stakes decision-making under uncertainty, including equitable resource allocation, mixed-fidelity bandit learning, and human–AI collaboration. My research has applications in healthcare, climate resilience, and policy.
I am supported by an NSF Graduate Research Fellowship, and I previously received MIT’s Henry Gabbay Ph.D. Fellowship.
Outside of research, you can find me at a local rock climbing gym, going on my millionth “thinking” walk of the day, or baking.
[Summer 2026] I spent the summer as a Strategy & Analytics Intern at Morgan Stanley Wealth Management in New York, building optimization models to help Relationship Managers prioritize client outreach.
[July 2026] I presented “When Should AI Defer? Responsible Automation for Deceased Donor Kidney Allocation” at the 2026 INFORMS Healthcare Conference in Raleigh, NC. I’ll also be presenting this work at the 2026 MIT-MGB Symposium and the 2026 INFORMS Annual Meeting in San Francisco!
[October 2025] I co-chaired an incredible session titled “Learning with Humans, AI, and Limited Data” with my advisor Swati Gupta at INFORMS 2025. Speakers included Thomas Daillak (MIT), Priyank Agrawal (Columbia), Nihar Shah (CMU), Pavithra Harsha (IBM Research), and myself.
[May 2025] Our paper on “Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning” has been accepted to ICML 2025!
