Research

My recent work has spanned foundation model pretraining, the science of scaling, inference algorithms, and more besides.

I'm interested in understanding what systems centered around foundation models will look like in 5 years, and how they will touch our lives in unthinkable ways. Reasoning models and computer use/software agents are two nascent examples, but I think and hope that AI systems will play a more transformative role in our day to day lives than mere automation. My research style usually involves studying these models in a scientific way, running experiments across the stack: from pretraining to evals and beyond.

My path into deep learning was a meandering one. I was adopted by two research communities broadly studying artificial intelligence when I was an undergraduate: physicists studying the brain -- who think of neural networks as freshly discovered platonic objects -- and computer systems people who think of them as differentiable graphs nestled inside GPU cores.

At Stanford, I am advised by Percy Liang and Tatsu Hashimoto.