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Galen Reeves

Associate Professor in the Department of Electrical and Computer Engineering
Electrical and Computer Engineering
140 Science Dr., 321 Gross Hall, Durham, NC 27708

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

In the News


Published September 21, 2021
Meet the Newly Tenured Faculty of 2021

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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 Cite

Linear 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 Cite

Achieving 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 Cite
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Recent Grants


CAREER: Theoretical Foundations for Probabilistic Models with Dense Random Matrices

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2018 - 2025

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms

ResearchSenior Investigator · Awarded by National Science Foundation · 2019 - 2023

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Education, Training & Certifications


University of California, Berkeley · 2011 Ph.D.

External Links


website