I am a Research Fellow at the Kempner Institute at Harvard.
My research focuses on understanding the foundations of modern machine learning. My PhD thesis focused on building principled approaches to the offline reinforcement learning problem. Now, I am primarily working at the intersection of foundation models and decision making, spanning problems throughout the learning pipeline from data collection through finetuning and generation.
Prior to coming to Harvard, I did my PhD at NYU advised by Joan Bruna and supported by an NDSEG fellowship. During that time, I interned at Google Brain, MSR, and FAIR. And before that, I undergraduated at Yale.
If you are interested in collaborating, please reach out via email!