Shubhendu Trivedi (Preferred)
Shubhendu Trivedi
Massachusetts Institute of Technology
Names
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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.
****@ttic.edu
, ****@csail.mit.edu
, ****@sparkneuro.com
, ****@zs.com
, ****@united.com
Personal Links
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Education & Career History
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Research Associate
Massachusetts Institute of Technology (mit.edu)
2019 – Present
Research Affiliate
Massachusetts Institute of Technology (mit.edu)
2018 – 2019
Institute Fellow
Brown University (Institute for Computational and Experimental Research in Mathematics) (brown.edu)
2018 – 2019
Research Assistant
Toyota Technological Institute at Chicago (ttic.edu)
2013 – 2018
Graduate Researcher
University of Chicago (uchicago.edu)
2013 – 2018
Graduate Fellow
Worcester Polytechnic Institute (wpi.edu)
2011 – 2012
Advisors, Relations & Conflicts
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Coauthor
Endre Szemerédi
****@rutgers.edu
Present
Postdoc Advisor
Tommi Jaakkola
****@csail.mit.edu
2019 – Present
Postdoc Advisor
Regina Barzilay
****@csail.mit.edu
2019 – Present
Coauthor
Brian D. Nord
****@fnal.gov
2017 – Present
Coauthor
Zhen Lin
****@illinois.edu
2017 – Present
Coauthor
Cengiz Pehlevan
****@seas.harvard.edu
2020 – 2021
Fellowship Mentor
Brendan Hassett
****@math.brown.edu
2018 – 2019
PhD Advisor
Gregory Shakhnarovich
****@ttic.edu
2013 – 2018
Coauthor
David McAllester
****@ttic.edu
2014 – 2015
Coauthor
Samory Kpotufe
****@columbia.edu
2014 – 2014
MS Advisor
Neil Heffernan
****@wpi.edu
2011 – 2012
MS advisor
Gabor Sarkozy
****@cs.wpi.edu
2011 – 2012
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 Learning
2019 – Present
Conformal Prediction
2019 – Present
Learning Dynamical Systems
2018 – Present
Machine Learning for Physics/Chemistry
2017 – Present
Group Equivariant Neural Networks
2016 – Present
Graph Neural Networks
2016 – Present
Neural Networks
2012 – Present
Learning on graphs
2011 – Present
Metric and Similarity Learning
2011 – Present
Clustering
2009 – Present
Data Mining
2008 – Present