Chaitanya K. Joshi (Preferred)
, Chaitanya Krishna Joshi
, Chaitanya K Joshi
, Chaitanya Joshi
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.
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
Personal Links
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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)
2022 – Present
Researcher
Institute for Infocomm Research, A*STAR, Singapore (i2r.a-star.edu.sg)
2020 – 2021
Researcher
Nanyang Technological University, Singapore (ntu.edu.sg)
2019 – 2020
Undergrad student
Nanyang Technological University, Singapore (ntu.edu.sg)
2015 – 2019
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
2022 – Present
Coauthor
Taco Cohen
****@qti.qualcomm.com
2022 – Present
Coauthor
Vijay Prakash Dwivedi
****@e.ntu.edu.sg
2020 – 2021
Coauthor
Louis-Martin Rousseau
****@polymtl.ca
2020 – 2021
Coauthor
Quentin Cappart
****@polymtl.ca
2020 – 2021
Coauthor
Xavier Bresson
****@ntu.edu.sg
2019 – 2021
Coauthor
Thomas Laurent
****@lmu.edu
2019 – 2021
Coauthor
Peng Xu
****@ntu.edu.sg
2019 – 2021
Coauthor
Boi Faltings
****@epfl.ch
2017 – 2018
Coauthor
Fei Mi
****@epfl.ch
2017 – 2018
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