Pratik Chaudhari

I am a PhD candidate in the Computer Science department at UCLA and I work with Stefano Soatto in the Vision Lab.

I have Engineer's and Master's degrees in Aeronautics & Astronautics from MIT where I worked with Emilio Frazzoli at the Laboratory of Information and Decision Systems (LIDS). I was in the Aerospace Engineering department at IIT Bombay for my undergraduate studies until 2010.

I am interested in deep learning, robotics and computer vision. I draw from physics, optimization and probability to solve problems in these domains. I have worked extensively on self-driving cars in the areas of computer vision, motion-planning and stochastic estimation at nuTonomy Inc.

I am currently on the academic job market.

CV Research Statement Teaching Statement

Resume

Contact

  pratikac at ucla dot edu

Web

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Publications (Google Scholar)

Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
P. Chaudhari, S. Soatto
arXiv:1710.11029
Short version PDF at
Advances in Approximate Bayesian Inference, NIPS 2017.
SIAM Conference on Imaging Science, 2018.
Parle: parallelizing stochastic gradient descent
P. Chaudhari, C. Baldassi, R. Zecchina, S. Soatto, A. Talwalkar, A. Oberman
arXiv:1707.00424
Code
Deep Relaxation: partial differential equations for optimizing deep neural networks
P. Chaudhari, A. Oberman, S. Osher, S. Soatto, G. Carlier
arXiv:1704.04932
In review, Communications of Pure and Applied Mathematics (CPAM)
Short version PDF at
Principled Approaches to Deep Learning, ICML 2017
SIAM Conference on Analysis of Partial Differential Equations, 2017
Asilomar Conference on Signals, Systems, and Computers, 2017
Entropy-SGD: Biasing gradient descent into wide valleys
P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina
arXiv:1611.01838
International Conference of Learning and Representations, 2017
Code
On the energy landscape of deep networks
P. Chaudhari, S. Soatto
arXiv:1511.06485
Advances in non-convex analysis and optimization, ICML 2016
Incremental synthesis of minimum-violation control strategies for robots interacting with external agents
P. Chaudhari, T. Wongpiromsarn, E. Frazzoli
PDF
American Control Conference (ACC), 2014
Code
Sampling-based algorithms for optimal motion planning using process algebra specifications
P. Chaudhari, V. Varricchio, E. Frazzoli
PDF
IEEE Conference on Robotics and Automation (ICRA), 2014
Video
Most societally beneficial video at International Joint Conference on Artificial Intelligence (IJCAI), 2014.
Game theoretic controller synthesis for multi-robot motion planning
Part I : Trajectory based algorithms

M. Zhu, M. Otte, P. Chaudhari, E. Frazzoli
arXiv:1402.2708
IEEE Conference on Robotics and Automation (ICRA), 2014
Incremental sampling-based algorithm for minimum-violation motion planning
L. Reyes-Castro, P. Chaudhari, J. Tumova, S. Karaman, E. Frazzoli, D. Rus
arXiv:1305.1102
IEEE Conference on Decision and Control (CDC), 2013
Code
Watch a video of the demonstration here.
Sampling-based algorithms for continuous-time POMDPs
P. Chaudhari, S. Karaman, D. Hsu, E. Frazzoli
PDF
American Control Conference (ACC), 2013
Code
Sampling-based algorithm for filtering using Markov chain approximations
P. Chaudhari, S. Karaman, E. Frazzoli
PDF
IEEE Conference on Decision and Control (CDC), 2012
Code

Theses

Algorithms for autonomous urban navigation with formal specifications
P. Chaudhari
PDF
Engineers's thesis, Aeronautics and Astronautics, MIT, 2014
Incremental sampling based algorithms for state estimation
P. Chaudhari
PDF
Master's thesis, Aeronautics and Astronautics, MIT, 2012

Invited talks