Minsu Cho

POSTECH

Names

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

Minsu Cho (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
,
****@postech.ac.kr
,
****@snu.ac.kr
,
****@ens.fr
,
****@inria.fr

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.

Associate Professor
POSTECH (postech.ac.kr)
2020Present
 
Assistant Professor
POSTECH (postech.ac.kr)
20162019
 
Postdoc + Starting Researcher
INRIA (inria.fr)
20122016
 
Post Doc
Ecole Normale Superiere de Paris (ens.fr)
20122016
 
PhD student (unified master & PhD)
Seoul National University (snu.ac.kr)
20052012
 
Undergrad student
Seoul National University (snu.ac.kr)
19972001
 

Advisors, Relations & Conflicts

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

Coworker
Bohyung Han
****@snu.ac.kr
2016Present
 
Coauthor
Suha Kwak
****@inria.fr
2014Present
 
Coauthor
Ivan Laptev
****@inria.fr
20152016
 
Coworker
Bumsub Ham
****@yonsei.ac.kr
20142016
 
Postdoc Advisor
Jean Ponce
****@inria.fr
20122016
 
Coauthor
Suha Kwak
****@dgist.ac.kr
20152015
 
Coauthor
Junchi Yan
****@sjtu.edu.cn
20112014
 
Coauthor
Cordelia Schmid
****@inria.fr
20112014
 
Coauthor
Karteek Alahari
****@inria.fr
20132013
 
PhD Advisor
Kyoung Mu Lee
****@snu.ac.kr
20052012
 

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

Vision and laguage
Present
 
Unsupervised learning
Present
 
Graph neural networks
Present
 
Object recognition
Present
 
Graph matching
Present
 
Computer vision
Present
 
Weakly supervised learning
Present
 
Visual correspondence
Present