My research aims to leverage insights from both disciplines to advance each other, ultimately unifying them under a single conceptual framework. I have interned at FAIR, Microsoft, and am a cofounder of the open-source RLHF research group CarperAI.

My research covers a broad range of topics intersecting with generative modeling including LLMs, Reinforcement Learning, Diffusion Models, and statistical/approxmation theory for generative models. Recently, I’ve been deeply engaged in exploring ways to enhance the reasoning capabilities of Language Models (LLMs) for knowledge discovery, utilizing techniques from reinforcement learning. On the theoretical front, I focus on approximation and statistical theories for generative models, with a strong emphasis on validating these theories through empirical observations.