Chaitanya K. Joshi

University of Cambridge

Names

How do you usually write your name as author of a paper? Also add any other names you have authored papers under.

Chaitanya K. Joshi (Preferred)
,
Chaitanya Krishna Joshi
,
Chaitanya K Joshi
,
Chaitanya Joshi

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
,
****@i2r.a-star.edu.sg
,
****@cam.ac.uk
,
****@cl.cam.ac.uk

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.

PhD student
University of Cambridge (cam.ac.uk)
2022Present
 
Researcher
Institute for Infocomm Research, A*STAR, Singapore (i2r.a-star.edu.sg)
20202021
 
Researcher
Nanyang Technological University, Singapore (ntu.edu.sg)
20192020
 
Undergrad student
Nanyang Technological University, Singapore (ntu.edu.sg)
20152019
 

Advisors, Relations & Conflicts

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

PhD Advisor
Pietro Liò
****@cam.ac.uk
2022Present
 
Coauthor
Taco Cohen
****@qti.qualcomm.com
2022Present
 
Coauthor
Vijay Prakash Dwivedi
****@e.ntu.edu.sg
20202021
 
Coauthor
Louis-Martin Rousseau
****@polymtl.ca
20202021
 
Coauthor
Quentin Cappart
****@polymtl.ca
20202021
 
Coauthor
Xavier Bresson
****@ntu.edu.sg
20192021
 
Coauthor
Thomas Laurent
****@lmu.edu
20192021
 
Coauthor
Peng Xu
****@ntu.edu.sg
20192021
 
Coauthor
Boi Faltings
****@epfl.ch
20172018
 
Coauthor
Fei Mi
****@epfl.ch
20172018
 

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

Graph Neural Networks
Present
 
Graph Representation Learning
Present
 
Geometric Deep Learning
Present