Krishna Murthy Jatavallabhula

Massachusetts 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.

Krishna Murthy Jatavallabhula (Preferred)
,
J. Krishna Murthy
,
Krishna Murthy

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.

****@gmail.com
,
****@pilani.bits-pilani.ac.in
,
****@research.iiit.ac.in
,
****@mila.quebec
,
****@mit.edu

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.

Postdoc
Massachusetts Institute of Technology (mit.edu)
2022Present
 
PhD student
University of Montreal (umontreal.ca)
2018Present
 
Intern
NVIDIA (nvidia.com)
20192020
 
MS student
IIIT Hyderabad (research.iiit.ac.in)
20152017
 
Undergrad student
BITS Pilani (pilani.bits-pilani.ac.in)
20112015
 

Advisors, Relations & Conflicts

Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.

Postdoc Advisor
Joshua Tenenbaum
****@mit.edu
2022Present
 
Postdoc Advisor
Antonio Torralba
****@csail.mit.edu
2022Present
 
Coauthor
Ganesh Iyer
****@gmail.com
2017Present
 
Coauthor
Gunshi Gupta
****@gmail.com
2017Present
 
Self
Krishna Murthy Jatavallabhula
****@gmail.com
2015Present
 
PhD Advisor
Liam Paull
****@iro.umontreal.ca
20182022
 
Coauthor
Sanja Fidler
****@cs.utoronto.edu
20192021
 
MS Advisor
Madhava Krishna K
****@iiit.ac.in
20152018
 

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

SLAM, Embodied agents, Differentiable physics, Robotics, Task planning, Differentiable rendering, Deep Learning, Structure from Motion
Present