Pratik Chaudhari

I am an Assistant Professor in the Electrical and Systems Engineering department and a core faculty in the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory. I hold a secondary appointment in the Computer and Information Sciences department and am a member of the Applied Mathematics (AMCS) graduate group, Penn Institute for Computational Science (PICS) and Center for AI-enabled Systems: Safe, Explainable, and Trustworthy (ASSET).

Previously, I held 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.

I defended my PhD thesis in the Computer Science department at University of California, Los Angeles in 2018 where I worked with Stefano Soatto in the UCLA Vision Lab. I have Engineer's (2014) and Master's (2012) degrees in Aeronautics & Astronautics from the Massachusetts Institute of Technology 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 have worked extensively on self-driving cars in the areas of motion planning and computer vision at nuTonomy Inc (now Hyundai-Aptiv Motional).


pratikac at seas dot upenn dot edu

Address: Levine Hall 470

Curriculum Vitae

Research Interests

I am interested in learning, robotics and computer vision. My ambition is to bring the dream of cybernetics closer to reality and enable Embodied Intelligence. The ability to perceive and control the environment, with cognition acting as the glue in between, is the hallmark of intelligent beings; the interplay between these three is the core of my research. My group conducts quite multi-disciplinary research along these lines. We study ideas from statistical physics, optimization, computer vision, and robotics. The work is sometimes very theoretical or very empirical, but more often, it is something in between. So everyone, from engineers and computer scientists to mathematicians and physicists will find something interesting to study in our group.

Research and Teaching Statements A synopsis of my recent research and teaching efforts. You can also read a slightly dated research statement from 2017 to see how these ideas have matured. An aspirational blogpost that stems from some recent work is here.

NeurIPS 2020 Workshop on Deep Learning through Information Geometry We organized a workshop to brainstorm how ideas in information geometry and information theory inform questions of optimization and generalization in deep learning. You can find the recorded talks and accepted submissions at here.


Brief overview

A picture of the prediction space of deep networks

Does the data induce capacity control in deep learning?

Unraveling the mysteries of stochastic gradient descent for deep neural networks

Learning with few labeled data

Research Group

Doctoral students Yansong Gao (AMCS), Christopher Hsu (ESE), Rohit Jena, (CIS co-advised with Jim Gee), Keshava Katti (ESE, co-advised with Deep Jariwala), Jialin Mao (AMCS), Dexter Ong (CIS, co-advised with Vijay Kumar), Rahul Ramesh (CIS), Yifei Shao (CIS, co-advised with Vijay Kumar), Rongguang Wang (ESE, co-advised with Christos Davatzikos), Rubing Yang (AMCS), Fanyang Yu (BE, co-advised with Christos Davatzikos).

Master's students Daiwei Chen (ESE. Now Wisconsin EE PhD), Wei-Kai Chang (Scientific Computing. Now Purdue CS PhD), Megharjun Nanda (Robo), Yingtian Tang (CIS. Now UCSD CS PhD), Haoran Tang (CIS. Now Purdue CS PhD)

Undergraduate students Yu Cao (1st year), Anirudh Cowlagi (2nd year), Siming He (3nd year), Brian Liu (1st year), Aalok Patwa (1st year), Suhaila Shankar (1st year), Yitzy Tanner (2nd year), Max Wang (1st year)

Alumni Sherry Chen (EE/MNT BS, CIS MS, now at Chef Robotics), Ashish Mehta (Robo MS, now at Qualcomm), Christopher Hsu (Robo MS, now at ARL/Penn EE PhD), Wenbo Zhang (Robotics MS, now at, Sebastian Peralta (Physics/ESE BS, Robo MS, now at Amazon), William Qian (CIS BS, Physics MS, now at Harvard Physics PhD)


ESE 546 Principles of Deep Learning
(Syllabus)(Notes: Fall 2022)

ESE 650 Learning in Robotics
(Syllabus) (Notes: Spring 2023)


See Google Scholar for the latest list of publications and citations.


  1. A picture of the energy landscape of deep neural networks PhD, Computer Science, University of California, Los Angeles, 2018 (PDF)
  2. Algorithms for autonomous urban navigation with formal specifications Engineer, Aeronautics-Astronautics, Massachusetts Institute of Technology, 2014 (PDF)
  3. Incremental sampling based algorithms for state estimation SM, Aeronautics-Astronautics, Massachusetts Institute of Technology, 2012 (PDF)