Zhen Dai

University of Chicago

Names

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

Zhen Dai (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.

****@uchicago.edu
,
****@berkeley.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.

PhD student
University of Chicago (uchicago.edu)
20182023
 
MS student
University of Cambridge (cam.ac.uk)
20172018
 
Undergrad student
University of California Berkeley (berkeley.edu)
20132017
 

Advisors, Relations & Conflicts

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

Coauthor
Mina Karzand
****@ttic.edu
2020Present
 
Coworker
Bradley J. Nelson
****@statistics.uchicago.edu
2020Present
 
Coworker
Aaron Chen
****@uchicago.edu
2020Present
 
Coworker
Ali Vakilian
****@ttic.edu
2020Present
 
Coworker
Yury Makarychev
****@ttic.edu
2020Present
 
PhD Advisor
Lek-Heng Lim
****@uchicago.edu
2018Present
 
Coworker
Zehua Lai
****@uchicago.edu
2018Present
 
Coauthor
Nathan Srebro
****@ttic.edu
2018Present
 
Coauthor
Ke Ye
****@hotmail.com
20212022
 
Coauthor
Blake Woodworth
****@ttic.edu
20192020
 
Coauthor
Kumar Kshitij Patel
****@ttic.edu
20192020
 
Coauthor
Sebastian U. Stich
****@epfl.ch
20192020
 
Coauthor
Brian Bullins
****@ttic.edu
20192020
 
Coauthor
H. Brendan McMahan
****@google.com
20192020
 
Coauthor
Ohad Shamir
****@weizmann.ac.il
20192020
 

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

Deep Learning
Present
 
Convex Optimization
Present
 
Combinatorial Optimization
Present