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
Reed-Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for all Rates Below Capacity
Journal Article IEEE Transactions on Information Theory · February 1, 2024 This paper considers the performance of Reed-Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori (bit-MAP) decoding. Its main result is that, for a fixed BMS channel, the family of binary RM code ... Full text CiteLinear Operator Approximate Message Passing: Power Method with Partial and Stochastic Updates
Conference IEEE International Symposium on Information Theory - Proceedings · January 1, 2024 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 CiteAchieving Capacity on Non-Binary Channels with Generalized Reed-Muller Codes
Conference IEEE International Symposium on Information Theory - Proceedings · January 1, 2023 Recently, the authors showed that Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels with respect to bit error rate. This paper extends that work by showing that RM codes defined on non-binary fields, known as generalized ... Full text CiteRecent Grants
Collaborative 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 - 2025HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms
ResearchSenior Investigator · Awarded by National Science Foundation · 2019 - 2023View All Grants
Education, Training & Certifications
University of California, Berkeley ·
2011
Ph.D.