Seungjun Nah

NVIDIA

Names

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

Seungjun Nah (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
,
****@nvidia.com

Education & Career History

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Research Scientist
NVIDIA (nvidia.com)
2022Present
 
Postdoc
Seoul National University (snu.ac.kr)
20212021
 
PhD student
Seoul National University (snu.ac.kr)
20142021
 
Guest Scientist
Max Planck Institute for Intelligent Systems, Max-Planck Institute (tue.mpg.de)
20192019
 
Guest Scientist
Max Planck Institute for Intelligent Systems, Max-Planck Institute (tuebingen.mpg.de)
20192019
 
Research Intern
Microsoft (microsoft.com)
20172017
 
Undergrad student
Seoul National University (snu.ac.kr)
20102014
 

Advisors, Relations & Conflicts

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

Coauthor
Jaerin Lee
****@snu.ac.kr
20202022
 
Coauthor
Sanghyun Son
****@snu.ac.kr
20172022
 
Coauthor
Heewon Kim
****@gmail.com
20172022
 
PhD Advisor
Kyoung Mu Lee
****@snu.ac.kr
20142022
 
Coauthor
Junkyu Park
****@snu.ac.kr
20212021
 
Coauthor
Suyoung Lee
****@snu.ac.kr
20202021
 
Coauthor
Radu Timofte
****@vision.ee.ethz.ch
20172021
 
Coworker
Andreas Geiger
****@tue.mpg.de
20192019
 
Coauthor
Sungyong Baik
****@snu.ac.kr
20192019
 
Coauthor
Gyeongsik Moon
****@snu.ac.kr
20192019
 
Coauthor
Seokil Hong
****@snu.ac.kr
20182019
 
Coauthor
Bee Lim
****@gmail.com
20172017
 
Coauthor
Tae Hyun Kim
****@gmail.com
20162017
 

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

diffusion models
2022Present
 
deep learning, neural network architecture
2019Present
 
dataset generation
2016Present
 
deblurring, super-resolution, denoising
2015Present