Animesh Garg (Preferred)
Animesh Garg
Georgia Institute of Technology
Names
How do you usually write your name as author of a paper? Also add any other names you have authored papers under.
Emails
Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.
****@cs.stanford.edu
, ****@cs.toronto.edu
, ****@gmail.com
, ****@nvidia.com
, ****@utoronto.ca
, ****@gatech.edu
, ****@berkeley.edu
Personal Links
Add links to your profiles on other sites. (Optional)
Education & Career History
Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.
Assistant Professor
Georgia Institute of Technology (gatech.edu)
2023 – Present
Researcher
NVIDIA (nvidia.com)
2018 – Present
Assistant Professor
University of Toronto (toronto.edu)
2019 – 2023
Postdoc
Stanford University (stanford.edu)
2016 – 2018
PhD student
University of California Berkeley (berkeley.edu)
2011 – 2016
MS student
Georgia Institute of Technology (gatech.edu)
2010 – 2011
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coworker
Anima Anandkumar
****@nvidia.com
2018 – Present
Coworker
Dieter Fox
****@nvidia.com
2018 – Present
Coworker
Sanja Fidler
****@nvidia.com
2018 – Present
Postdoc Advisor
Fei-Fei Li
****@cs.stanford.edu
2016 – 2018
Postdoc Advisor
Silvio Savarese
****@stanford.edu
2016 – 2018
PhD Advisor
Ken Goldberg
****@berkeley.edu
2011 – 2016
PhD Advisor
Pieter Abbeel
****@cs.berkeley.edu
2011 – 2016
Expertise
For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing
Causal Inference
2018 – Present
3D Vision
2017 – Present
Representation Learning
2016 – Present
Computer Vision
2016 – Present
Reinforcement Learning
2014 – Present
Robot Learning
2014 – Present
Robotics
2011 – Present