Pratik Chaudhari

I will be joining the faculty in the ESE department and the GRASP Lab at the University of Pennsylvania starting July 2019. As I start my research group, I will be taking on PhD students in the general areas of robotics, control, machine learning and computer vision; also see the research statement below. If you are applying outside the ESE department, say CIS or MEAM, drop me an email.

I currently hold a joint position as a Senior Applied Scientist at Amazon Web Services and a post-doctoral researcher at the California Institute of Technology in Computing and Mathematical Sciences department.

I recently defended my PhD thesis in the Computer Science department at UCLA and I worked 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.

Resume CV Research Statement

Contact

  pratikac at seas dot upenn dot edu

Web

GitHub Twitter Facebook Google+

Publications (Google Scholar)

Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
P. Chaudhari, S. Soatto
arXiv:1710.11029
International Conference of Learning and Representations, 2018
Video of a talk at IPAM New Deep Learning Techniques workshop
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
Short version at
SysML Conference, Stanford, 2018
Deep Relaxation: partial differential equations for optimizing deep neural networks
P. Chaudhari, A. Oberman, S. Osher, S. Soatto, G. Carlier
arXiv:1704.04932
Research in the Mathematical Sciences (RMS)
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
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.
Most societally beneficial video at International Joint Conference on Artificial Intelligence (IJCAI), 2014.
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

A picture of the energy landscape of deep neural networks
P. Chaudhari
PDF
PhD thesis, Computer Science, UCLA, 2018
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

Talks/Posters