I am a Ph.D. candidate in
Computer Science at Cornell University, where I study how to make more reliable conclusions when using machine learning (ML) methods in practice. My work focuses on empirically motivated, theoretically grounded problems in Bayesian inference, model selection, and deep learning. I also do associated tech policy and ethics research, which aims to characterize the relationship between reliability and accountability in AI/ML. I am a co-organizer of
GenLaw, a community of researchers and practitioners interested in emerging issues at the interface of generative AI and the law (our first in-person workshop will be co-located with
ICML '23), and am the leader of the
arbitrar.ai project. During Summer 2022, I interned at
Microsoft Research NYC. In 2021, I was named a
"Rising Star in EECS" by MIT. I am a member of Cornell's initiative on
Artificial Intelligence, Policy, and Practice (AIPP), which has very generously supported my work through funding from the John T. and Catherine D. MacArthur Foundation.