Zachary T. Rewolinski
Statistics PhD Candidate @ UC Berkeley
I am a third-year PhD candidate in the Department of Statistics at UC Berkeley, advised by Bin Yu.
My research is primarily focused on ensuring that machine learning models are trustworthy of making real-world decisions in high-impact fields such as finance, medicine, criminal justice, and more. My research is supported by the NSF Graduate Research Fellowship.
I am also an Associate (part-time) Analyst with the San Diego Padres Research & Development team. In Summer 2026, I will be joining Microsoft Research as an intern.
I graduated from Rice University in 2023 with a B.S. in Statistics, a B.S. in Computer Science, and a B.A. in Mathematics. While at Rice, I was fortunate to work with Genevera Allen.
Recent News
May 2026: I have passed my qualifying exam! Thank you to my chair, Avi Feller, as well as the rest of my committee: Amanda Coston, Deirdre Mulligan, and Bin Yu.
April 2026: Our paper Local MDI+: Local Feature Importances for Tree-Based Models has been accepted to Transactions on Machine Learning Research!
April 2026: A preprint for our paper Sanity Checks for Agentic Data Science is available.
November 2025: I have returned to the San Diego Padres R&D team as an Associate Analyst.
October 2025: Our paper PCS Workflow for Veridical Data Science in the Age of AI has been accepted to Philosophical Transactions of the Royal Society A!
June 2025: A preprint for our paper PCS Workflow for Veridical Data Science in the Age of AI is available.
June 2025: A preprint for our paper Local MDI+: Local Feature Importances for Tree-Based Models is available.
June 2025: I started my internship with the San Diego Padres Research & Development team.