I am a fifth-year PhD student in the Department of Electrical Engineering
at Stanford University where I am advised by Mert Pilanci
My research interests are in convex optimization and deep learning. Currently, I focus on the understanding of neural network through a convex perspective and on improving the speed and memory of first-order methods using sketching tools. Previously, I developed reinforcement and imitation learning algorithms for safety-critical applications.
I obtained a B.S. and M.S. at the Ecole Polytechnique, and I completed the Part III of the Mathematical Tripos at Cambridge University. I was also a visiting student researcher at UC Berkeley with Laurent El Ghaoui.
My Google Scholar and Github pages.
Email: lacotte (at) stanford (dot) edu
Ph. D., Electrical Engineering, Stanford University, Current.
M. S., Part III of the Mathematical Tripos, Cambridge University, 2016.
M. S., Applied Mathematics, Ecole Polytechnique, 2015.
B. S., Mathematics and Physics, Lycée Louis-le-Grand (2010-2012) and Ecole Polytechnique, 2014.
- (Fall 2020) EE263, Introduction to Linear Dynamical Systems, TA.
- (Spring 2020) EE364B/CS364B, Convex Optimization II, TA.
- (Spring 2019) AA203, Optimal and Learning-based Control, TA.
Reviewer for NeurIPS, ICML, ICLR, AISTATS, ECC, IJRR.