WILLIAM T. REDMAN
I am a Senior Research Scientist at Johns Hopkins Applied Physics Lab (APL), working on problems in the intersection of machine learning, neuroscience, and robotics in the Intelligent Systems Center (ISC). My CV.
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Before joining APL, I was in a Dynamical Neuroscience (DYNS) PhD student at UC Santa Barbara (UCSB), where I worked in Prof. Michael Goard's systems neuroscience lab as a Chancellor's Fellow. Prior to being at UCSB, I got my bachelor's degrees in mathematics and physics from New York University (NYU).
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UCSB is home to an exciting and growing neuroscience community, and DYNS is a wonderfully unique program for people interested in engaging with the many different facets of the field. Feel free to send any questions about DYNS my way!
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NEWS: I'm a Publicity Chair for the new Conference on Parsimony and Learning (CPAL). Check out the website for more details on this exciting, interdisciplinary conference!
RESEARCH
My research interests are in understanding how the brain performs computations that underly and support critical cognitive processes, such as memory and learning, and utilizing this insight to expand the capabilities of machine learning methods. In particular, I have four broad focus areas: 1) neural computations; 2) sparse machine learning; 3) dynamics of learning; 4) learning of dynamics.
See below, and my Google Scholar and ResearchGate for more information.
NEURAL COMPUTATIONS
Coming soon.
SPARSE MACHINE LEARNING
Coming soon.
DYNAMICS OF LEARNING
Coming soon.
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LEARNING OF DYNAMICS
Coming soon.
PUBLICATIONS
PEER REVIEWED:
9) E. R. J. Levy, S. Carrillo-Segura, E. H. Park, W. T. Redman, J. Hurtado, S. Y. Chung, A. A. Fenton, "A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields". Cell Reports (2023)
8) W. T. Redman, M. Fonoberova, R. Mohr, Y. Kevrekidis, and I. Mezić, "Algorithmic (Semi-)Conjugacy via Koopman Operator Theory". IEEE Conference on Control and Decision (CDC 2022)
7) W.T. Redman, N.S. Wolcott, L. Montelisciani, G. Luna, T.D. Marks, K.K. Sit, C.-H. Yu, S.L. Smith, and M.J. Goard, Long-term Transverse Imaging of the Hippocampus with Glass Microperiscopes. eLife (2022)
6) W. T. Redman, T. Chen, Z. Wang, and A. S. Dogra Universality of Winning Tickets: A Renormalization Group Perspective. International Conference on Machine Learning (ICML 2022).
5) W. T. Redman, M. Fonoberova, R. Mohr, Y. Kevrekidis, and I. Mezić, An Operator Theoretic View on Pruning Deep Neural Networks. International Conference on Learning Representations (ICLR 2022).
4) W. T. Redman, On Koopman Mode Decomposition and Tensor Component Analysis. Chaos Fast Track (2021).
3) A. S. Dogra*, and W. T. Redman* Optimizing Neural Networks via Koopman Operator Theory. Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (* contributed equally)
2) W. T. Redman, Renormalization group as a koopman operator. Physical Review E Rapid Communication (2020)
1) W. T. Redman, An O(n) method of calculating Kendall correlations of spike trains. PLoS One (2019)
IN PROGRESS:
4. W. T. Redman, Z. Wang, A. Ingrosso, and S. Goldt, "Sparsity Enhances Non-Gaussian Data Statistics During Local Receptive Field Formation".
3. W. T. Redman, J. M. Bello-Rivas, M. Fonoberova, R. Mohr, I. G. Kevrekidis, and I. Mezić, "Identifying Equivalent Optimization of Machine Learning Methods".
2. W. T. Redman, D. Huang, M. Fonoberova, and I. Mezić, "Koopman Learning with Episodic Memory".
1. W. T. Redman, S. A. M. Acosta, X.X. Wei, and M. J. Goard, "Robust Variability of Grid Cell Properties Within Individual Grid Modules Enhances Encoding of Local Space".
ABOUT
I grew up in Hopewell, New Jersey, a little town outside of Princeton and Trenton. I found math class repulsive and, until I had a rather sudden change of heart my senior year, spent most of the period working on ways to avoid paying attention. Outside of research, I enjoy hiking and cycling (both of which are complicated by my innately poor sense of direction) and trying new beers.