Kevin Zhang

I am a first-year PhD student in the EECS department at MIT, where I am fortunate to be advised by Stephen Bates.

My research focuses on principled methods for causal inference and uncertainty quantification in machine learning, with the goal of developing decision-making models that are statistically grounded and reliable. Topics I am particularly excited about include causal reasoning in AI, calibration, and conformal prediction. I am also broadly interested in applying these methods to problems in science, medicine, and finance.

Previously, I completed my B.A. in Computer Science and Mathematics at Columbia University, where I worked on causal fairness in healthcare with Shalmali Joshi and autoregressive causal models with Nakul Verma. During this time, I also had the pleasure of collaborating with Yixin Wang on structured probabilistic modeling.