Eli Ben-michael is an Assistant Professor of Statistics and Public Policy at Carnegie Mellon University who studies causal inference and algorithmic decision-making, bridging rigorous statistical theory with real-world policy applications. With a PhD from UC Berkeley and over a decade of experience across academia and industry, he combines postdoctoral training at Harvard with applied internships at Uber and Walmart Labs that focused on spatiotemporal modeling, embeddings, and large-scale ML systems. His joint appointment in Heinz College and the Department of Statistics & Data Science reflects a rare ability to translate methodological advances into actionable insights for decision-making institutions. Eli has a strong teaching and mentorship background from roles at Berkeley and Columbia, and a track record of accelerating algorithms (e.g., dramatic runtime improvements and Spark-based scaleups) in production-like settings. He is comfortable working at the intersection of statistics, CS, and public policy, and often emphasizes interpretable contributions from black-box models to inform policy-relevant decisions. Colleagues describe him as a methodologist who deliberately ties technical innovation to practical impact in high-stakes, social domains.
11 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy (PhD), Statistics, Doctor of Philosophy (PhD), Statistics at University of California, Berkeley
Bachelor of Arts (B.A.), Computer Science-Statistics, Summa Cum Laude, Bachelor of Arts (B.A.), Computer Science-Statistics, Summa Cum Laude at Columbia University in the City of New York
Contributions:142 commits, 6 PRs, 105 pushes in 1 year
maximumentropysyntheticmaximum-entropy
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Eli Ben-michael - Assistant Professor at Carnegie Mellon University