Associate Professor Department of Electrical and Computer Engineering Department of Statistical Science Duke University Email: galen.reeves@duke.edu Phone: 9196684042

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 highdimensional 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.
04/2021  Here is a recent talk on the The InformationTheoretic limits of the matrix tensor product model, which I presented at the NYU / ETH MAD+ Seminar.
04/2021  In Fall 2021 I'll be teaching STA 0 711: Probability and Measure Theory
06/2020  My work with S. Goldt, M. Mézard, F. Krzakala, and L. Zdeborová on Gaussian equivalence of generative models for learning with twolayer neural networks has been posted to arxiv.
06/2020  My work with J. Barbier on Informationtheoretic limits of a multiview lowrank symmetric spiked matrix model was presented at ISIT 2020.
05/2020  My paper InformationTheoretic Limits for the Matrix Tensor Product has been posted to arxiv.
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 highdimensional statistics. He received the NSF CAREER award in 2017.