Hello, I am Sarah, a theoretical physics and neuroscience postdoctoral research fellow at the Flatiron Institute, which is part of the
I am a graduate of the
applied physics department at Stanford University, where I was theorist in the
My current research interests focus on using methods from nonequilibrium statistical physics and large deviation theory
to study biological computation, from the micoscopic scale of single receptor computations to macroscopic reinforcement learning. I am interested generally in statistical physics,
computational neuroscience, and machine learning.
My latest paper (with Subhaneil Lahiri) studies thermodynamic limits on sensors modeled as continuous time Markov chains, using stochastic thermodynamics and large deviation theory. We can place interesting bounds on sensors of this type by first deriving a thermodynamic uncertainty relation for densities in subsets of Markov chain states.With Chris Stock and Sam Ocko, I have also recently been working on a project deriving biologically plausible synaptic update rules that provably preserve the task an RNN is trained to do, while also increasing the network robustness. We can think of this synaptic update rule as traversing a manifold in synaptic weight space of networks that perform exactly the same task, to find the network with the best noise robustness quality.
Links to my papers and preprints are listed below.
Christopher H. Stock, Sarah E. Harvey, Samuel A. Ocko, Surya Ganguli. (2021). Synaptic balancing: a biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance. PLOS Computational Biology (link). Preprint: https://arxiv.org/abs/2107.08530.
Todd Karin, Xiayu Linpeng, M. M. Glazov, M. V. Durnev, E. L. Ivchenko, Sarah Harvey, Ashish K. Rai, Arne Ludwig, Andreas D. Wieck, and Kai-Mei C. Fu. July 2016. Giant permanent dipole moment of two-dimensional excitons bound to a single stacking fault. Physical Review B, 94(4). doi:10.1103/physrevb.94.041201. (link)
University of Washington B.S., Physics and Astronomy, summa cum laude, 2015
Stanford University Ph.D., Applied Physics, 2022
US mail: Sarah Harvey 162 Fifth Avenue Center for Computational Neuroscience New York, NY 10010 Email: email@example.com Twitter: @SarahLizHarvey