Quant Finance. Since 2021, I have been a quant finance researcher. Most recently, I was in the systematic equities group at DE Shaw (2021-2025, VP 24-25). Right now, I'm on a non-compete period before joining a new firm in fall 2026.
Academia. Before finance, I spent four years in the Department of Computer Science at Cornell University — three as an Assistant Professor, one as a postdoc. My research designed numerical methods and algorithmic frameworks for better and bigger analyses of complex data, backed by grants from the ARO and NSF. I also taught classes covering data science, machine learning, matrix computations, and numerical optimization.
The research was published in leading journals including Science, PNAS, and SIAM Review and at top conferences like NeurIPS, ICML, ICLR, KDD, and WWW; earned an NSF CAREER Award, a KDD Best Paper Award, and a JP Morgan Chase AI Faculty Award; and was covered in Quanta Magazine, SIAM News, Synced, and Futurity. As part of this research, I mentored postdocs, PhD students, and undergraduates who have gone on to faculty positions, AI-based industry roles, and top PhD programs.
Education. Prior to Cornell, I spent nine formative years in the Bay Area, where I completed my PhD and MS in computational mathematics at Stanford and my BS in EECS and BA in applied math at UC Berkeley. During summers, I interned at Google four times, as well as at Sandia National Laboratories and HP Labs, gaining experience across industry research and large-scale systems.
The Early Years. I was born and raised in and around Madison, Wisconsin. I like to think that I maintain my Midwestern values.