Overview
Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Associate Professor with a joint appointment in the Department of Electrical Computer Engineering and the Department of Statistical Science. He completed his PhD in Electrical Engineering and Computer Sciences at the University of California, Berkeley in 2011, and he was a postdoctoral associate in the Departments of Statistics at Stanford University from 2011 to 2013. His research interests include information theory and high-dimensional statistics. He received the NSF CAREER award in 2017.
Current Appointments & Affiliations
Associate Professor in the Department of Electrical and Computer Engineering
·
2020 - Present
Electrical and Computer Engineering,
Pratt School of Engineering
Associate Professor of Statistical Science
·
2020 - Present
Statistical Science,
Trinity College of Arts & Sciences
Recent Publications
Linear operator approximate message passing (OpAMP)
Journal Article Information and Inference · December 1, 2025 This paper introduces a framework for approximate message passing (AMP) in dynamic settings where the data at each iteration is passed through a linear operator. This framework is motivated in part by applications in large-scale, distributed computing wher ... Full text CiteInformation-Theoretic Proofs for Diffusion Sampling
Conference IEEE International Symposium on Information Theory Proceedings · January 1, 2025 This paper provides an elementary, self-contained analysis of diffusion-based sampling methods for generative modeling. In contrast to existing approaches that rely on continuous-time processes and then discretize, our treatment works directly with discret ... Full text CiteFundamental Limits for High-Dimensional Factor Regression Models
Conference IEEE International Symposium on Information Theory Proceedings · January 1, 2025 High-dimensional factor models enable the analysis of complex interactions in structured data. In this paper, we introduce a generalization of the matrix tensor product framework that incorporates covariate information. We rigorously derive fundamental lim ... Full text CiteRecent Grants
Collaborative Research: CIF: Small: New Frontiers of Approximate Message Passing: Distributed Processing, Stochastic Updates, and Non-Asymptotic Bounds
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2025 - 2028Collaborative Research: NSF-BSF CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems
ResearchCo-Principal Investigator · Awarded by National Science Foundation · 2023 - 2026CAREER: Theoretical Foundations for Probabilistic Models with Dense Random Matrices
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2018 - 2025View All Grants
Education, Training & Certifications
University of California, Berkeley ·
2011
Ph.D.