I am a Ph.D. student in Computer Science at Columbia University,  advised by David Blei.  
I am broadly interested in machine learning and causal inference. In the past couple of years, I've worked on bringing machine learning insights to causal effect estimation.

Recently, I have been thinking about how to ensure large language models produce factual responses? (My hunch: insights from causality can help! )

I enjoy brainstorming about weird & ambitious research ideas. Please reach out if you want to chat!