Professor Rohe earned his Ph.D. in Statistics from the University of California, Berkeley, in 2011 and has taught at UW–Madison since then, with courtesy appointments in Educational Psychology, Electrical and Computer Engineering, and the School of Journalism and Mass Communication.

His research has focused on spectral methods for large social and biological graphs, statistical reading comprehension, and—most recently—the methodology of AI-assisted extraction from scientific literature.

With his Ph.D. student Auden Krauska, Rohe co-developed a UW–Madison undergraduate course on statistical reading comprehension and the codebook-based approach to peer review that became the conceptual foundation for Data Mint. He works at the intersection of statistical methodology and applied tooling: building infrastructure that makes the upstream, artisanal work of evidence synthesis legible, reproducible, and shareable.

You can visit his university webpage here.

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