Pratik Chaudhari

I am an Assistant Professor in the Electrical and Systems Engineering department and the GRASP Robotics Laboratory. I hold a secondary appointment in the Computer and Information Sciences department and am a member of the Applied Mathematics (AMCS) and Penn Institute for Computational Science (PICS) graduate groups.

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 an 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 computer vision, planning and control 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 machine learning, in particular deep neural networks, 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 control theory. 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.

You can read a (slightly outdated) research statement here.

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

New Students I am currently not looking for new students to work with. But if you are an existing student at Penn, send me an email and we can set up a time to talk.


Brief overview

Generalization of deep networks

Optimization in deep networks

Learning with few labeled data

Research Group

Doctoral students Yansong Gao (AMCS), Christopher Hsu (ESE), Jialin Mao (AMCS), Rahul Ramesh (CIS), Rongguang Wang (ESE, co-advised with Christos Davatzikos), Rubing Yang (AMCS), Fanyang Yu (BE, co-advised with Christos Davatzikos)

Master's students Megharjun Nanda (Robo), Yingtian Tang (CIS), Daiwei Chen (ESE), Wei-Kai Chang (Scientific Computing), Haoran Tang (CIS)

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

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


ESE 546 Principles of Deep Learning
(Syllabus) (Notes: Fall 2021)

ESE 650 Learning in Robotics
(Syllabus) (Notes: Spring 2021)


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)