Kevin J. Shih

NVIDIA

Names

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

Kevin J. Shih (Preferred)
,
Kevin Jonathan Shih

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.

****@illinois.edu
,
****@nvidia.com

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.

Research Scientist
NVIDIA (nvidia.com)
2017Present
 
PhD student
University of Illinois, Urbana Champaign (illinois.edu)
20112017
 

Advisors, Relations & Conflicts

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

Coauthor
Bryan Plummer
****@bu.edu
Present
 
Coworker
Anthea Li
****@cs.stanford.edu
2020Present
 
Coworker
Bryan Catanzaro
****@nvidia.com
2017Present
 
Coauthor
Saurabh Singh
****@illinois.edu
2015Present
 
Advisor
Derek Hoiem
****@illinois.edu
20112017
 
Coauthor
Arun Mallya
****@illinois.edu
20152015
 
Coauthor
Vignesh Jagadeesh
****@ece.ucsb.edu
20142015
 
Coauthor
Ian Endres
****@here.com
20112015
 
Coauthor
Derek Hoiem
****@illinois.edu
20112015
 
Coauthor
Robinson Piramuthu
****@yahoo.com
20112014
 
Coauthor
Wei Di
****@ebay.com
20112014
 
Coauthor
Derek Hoiem
****@uiuc.edu
20112014
 
Coauthor
Vignesh Jagadeesh
****@ebay.com
20112014
 
Coauthor
Robinson Piramuthu
****@ebay.com
20112014
 
Coauthor
Vignesh Jagadeesh
****@gmail.com
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

Vision-Language
Present
 
Visual Attention
Present
 
Video Prediction
Present
 
Fine-Grained Object Classification
Present
 
Keypoint Localization
Present
 
Object Detection
Present
 
Visual Question Answering
Present
 
Unsupervised Landmarks
Present