Sitan Chen

University of California Berkeley

Names

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

Sitan Chen (Preferred)

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.

****@mit.edu
,
****@seas.harvard.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
University of California Berkeley (berkeley.edu)
20212023
 
PhD student
Massachusetts Institute of Technology (mit.edu)
20162021
 
MS student
Harvard University (harvard.edu)
20152016
 
Undergrad student
Harvard University (harvard.edu)
20122016
 

Advisors, Relations & Conflicts

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

Coauthor
Adam Klivans
****@cs.utexas.edu
2020Present
 
Coauthor
Sebastien Bubeck
****@microsoft.com
2020Present
 
Coauthor
Zhao Song
****@adobe.com
2020Present
 
Coauthor
Jerry Li
****@microsoft.com
2019Present
 
Coauthor
Morris Yau
****@berkeley.edu
2019Present
 
Coauthor
Frederic Koehler
****@mit.edu
2019Present
 
Coauthor
Raghu Meka
****@cs.ucla.edu
2018Present
 
PhD Advisor
Ankur Moitra
****@mit.edu
20162021
 
Coauthor
Prasad Raghavendra
****@cs.berkeley.edu
20182019
 
Coauthor
Leslie Valiant
****@seas.harvard.edu
20152016
 
Coauthor
Salil Vadhan
****@harvard.edu
20142016
 

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

semidefinite programming, sum-of-squares, extension complexity
20182019
 
approximate counting and sampling, Markov chains, statistical physics
20172019
 
computational learning theory
20162019