I am a second-year PhD student in Statistics and Machine Learning at Cornell University, advised by Martin Wells and Yuchen Wu.
I am interested in statistical machine learning and establishing rigorous foundations for statistical and machine learning methods, while also developing new algorithms guided by theoretical insights. I am fortunate to have been supported by a Cornell University Fellowship.
Before Cornell, I was an undergraduate at UC Berkeley studying Computer Science, where I was advised by Iain Carmichael and Ryan Tibshirani.
If you're an undergrad or Master's student at Cornell and are interested in collaborating, please reach out!
Selected recent work across optimization, alignment, generative models, and differential privacy.
A video with Sithija Manage breaking down how I approached graduate school applications and wrote my Statement of Purpose. We discuss what to include, how to describe research experiences with technical detail, and how to tailor your SOP for committee-driven vs PI-driven admissions.
An interview about my journey through graduate school applications. We discuss my undergraduate research at Berkeley, Stanford, and USC, the admissions process, writing statements of purpose, building mentorships, and evaluating program fit.
A podcast episode on the Liftoff Podcast with Aman Manazir. I share how I got interested in competitive math, balanced internships and research at Berkeley, Stanford, and USC, and the decision between quant research and a PhD at Cornell.