I am a Computer Science Ph.D. student at Columbia University, focusing on theoretical computer science, with particular interest in machine learning, algorithms, and statistics. I am extremely fortunate to be advised by Professor Daniel Hsu and Professor Alex Andoni. I am supported by an NSF Graduate Research Fellowship.
For more details, either check out this website or see my [CV] .
My primary area of research is theoretical computer science: in particular, provably resource-efficient algorithms for fitting statistical models (“algorithmic statistics”). Several of my interests can be summarized by the phrase “non-worst-case analysis for machine learning algorithms.” In applications, I am particularly interested in natural language understanding, neuroscience, economics, and robotics.
In Summer 2019 I visited the Simons Institute at Berkeley for the Foundations of Deep Learning program.
I graduated from Princeton with an A.B. Mathematics degree with honors in 2016 and an M.S.E. in Computer Science in 2017, where I was lucky to have Professor Sanjeev Arora and Professor Ken Norman as thesis advisors. I was a member of Sanjeev Arora’s Unsupervised Learning Group, where I studied provable methods for machine learning (also a part of NLP @ Princeton and ML Theory @Princeton), in particular focusing on natural language understanding. I was also a member of Ken Norman’s Computational Memory Lab at the Princeton Neuroscience Institute, where I applied machine learning to fMRI analysis methods.