Zachary T. Rewolinski

Statistics PhD Candidate @ UC Berkeley

zachrewolinski@berkeley.edu zachrewolinski Zachary T. Rewolinski Zachary Rewolinski

I am a fourth-year PhD candidate in the Department of Statistics at UC Berkeley, advised by Bin Yu. Currently, I am interning at Microsoft Research, where I am working with Chenglong Wang and Weijia Xu in the Deep Learning Group.

My broad research interests aim to ensure that machine learning models are capable of making trustworthy real-world decisions in high-impact fields. Recently, I have been focused on a topic I refer to as procedural data science, which seeks to improve predictions and inferences by optimizing the underlying data science pipeline (i.e., the ‘procedure’ of doing data science). This includes analyzing the impacts of complex, often-overlooked interactions between early data preparation and modeling choices, and then using these insights to design systems where sequential choices compound into more reliable outputs. 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.

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.

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