I am a Research Fellow at the Flatiron Institue working jointly between the Centers for Computational Mathematics and Computational Neuroscience as well as a Visiting Scholar at NYU. I completed my PhD in the Neural Dynamics and Computation Lab at Stanford University co-advised by Surya Ganguli and Shaul Druckmann and supported by the DOE Computational Science Graduate Fellowship. Through this program, I also worked in Sandia National Labs’ Extreme-Scale Data Science group as a visiting researcher, leading to frequent collaborations with Tammy Kolda.
My research combines theoretical and empirical approaches to understand and improve deep learning, low-rank models, and recurrent networks in the brain. This has led to the opportunity to work with many excellent collaborators, including Jonathan Frankle, Karolina Dziugaite, Mansheej Paul, Stanislav Fort, Alex Williams, and Scott Linderman.
Prior to my PhD, I completed the MPhil in Scientific Computing (Machine Learning Focus) at the University of Cambridge working with Carola Bibiane Schönlieb and Martin Benning. I did my undergrad at Arizona State University and Barrett, the Honors College where I worked on statistical signal processing.
Contact: brettlarsen [at] flatironinstitute.org
- Machine Learning and Data Science
- High-Dimensional Loss Landscapes
- Numerical Optimization
- Randomized Algorithms
- Low-Rank Models
- Robust Statistics
PhD in Physics, 2022
MS in Statistics, 2020
MPhil in Scientific Computing, 2016
University of Cambridge
BSE in Electrical Engineering, 2015
BS in Physics, 2015
Arizona State University (ASU)