Shanghang Zhang

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.

Shanghang Zhang (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.

****@gmail.com
,
****@andrew.cmu.edu
,
****@petuum.com
,
****@eecs.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.

Postdoc Research Fellow
University of California Berkeley (berkeley.edu)
20202021
 
Researcher
Petuum Inc. (petuum.com)
20182020
 
PhD student
Carnegie Mellon University (cmu.edu)
20132018
 
PhD student
University of Lisbon (ist.utl.pt)
20132018
 
Intern
Adobe Systems (adobe.com)
20172017
 

Advisors, Relations & Conflicts

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

Coworker
Jian Du
****@andrew.cmu.edu
2016Present
 
Coworker
Guanhang
****@andrew.cmu.edu
2015Present
 
Coworker
Guanhang Wu
****@andrew.cmu.edu
2015Present
 
Coworker
Soummya Kar
****@andrew.cmu.edu
2010Present
 
PhD Advisor
Jose M. F. Moura
****@andrew.cmu.edu
1995Present
 
Coauthor
Congzheng Song
****@cornell.edu
20192020
 
Coauthor
Najmeh Sadoughi
****@petuum.com
20192020
 
Coauthor
Pengtao Xie
****@petuum.com
20192020
 
Coauthor
Eric Xing
****@petuum.com
20192020
 
Coauthor
Xiaodong Xie
****@pku.edu.cn
20112014
 
Coauthor
Wen Gao
****@pku.edu.cn
20112014
 
Coauthor
Huizhu Jia
****@pku.edu.cn
20112014
 

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 on graph
2015Present
 
Machine Learning
2013Present
 
Deep Learning
20132019
 
NLP
20132019
 
Computer Vision
20132019
 
zero shot learning
20132019
 
domain adaptation
20132019