Associate Professor Department of Electrical and Computer Engineering Department of Statistical Science Duke University Email: galen.reeves@duke.edu Office Rm 211E Old Chemistry Department of Statistical Science Duke box #90251 Durham, NC 27708 |
My research interests include information theory, statistics, machine learning, and signal processing. I am particularly interested in fundamental questions concerning the amount of data needed for an inference or learning task, the representation of uncertainty in high-dimensional and complex spaces, and the gap between practical and combinatorial methods. My approach to research is highly interdisciplinary and builds upon ideas from engineering, statistics, mathematics, theoretical computer science, and statistical physics.
My work with R. Rossetti on Approximate message passing for the matrix tensor product model has been uploaded to arxiv.
My work with H. Pfister showing that Generalized Reed-Muller codes achieve capacity on non-binary channels was presented at ISIT 2023
In Fall 2023 I'll be teaching two courses: ECE 587 / STA 563 - Information Theory [course information] and STA 0 711: Probability and Measure Theory [course information]
My work with Z. Goldfeld, K. Greenewald, T. Nuradha on k-sliced mutual information was presented at NeurIPS 2022
My work with J. Behne on Rank-one matrix estimation with groupwise heteroskedasticity was presented at AISTATS 2022
My work with S. Goldt, B. Loureiro, F. Krzakala, M. Mézard, and L. Zdeborová on Gaussian equivalence of generative models for learning with two-layer neural networks was presented at Mathematical and Scientific Machine Learning 2022.
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.