Mohamed Elhoseiny

KAUST

Names

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

Mohamed Elhoseiny (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.

****@cs.rutgers.edu
,
****@fb.com
,
****@gmail.com
,
****@stanford.edu
,
****@kaust.edu.sa
,
****@cs.stanford.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.

Assistant Professor
KAUST (kaust.edu.sa)
2019Present
 
Visiting Faculty Scholar
Stanford University (stanford.edu)
20192020
 
Visiting Researcher
Baidu Research (baidu.com)
20192019
 
Postdoc Researcher
Facebook (fb.com)
20162019
 
Research Associate
Adobe Research (adobe.com)
20152016
 
PhD student
Rutgers University (rutgers.edu)
20112016
 

Advisors, Relations & Conflicts

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

Coauthor
shadi albarqouni
****@tum.de
20192020
 
Coauthor
Xiaolei Huang
****@lehigh.edu
20162016
 
Coauthor
Dimitris Metaxas
****@cs.rutgers.edu
20162016
 
Coauthor
Harpreet Sawhney
****@sri.com
20152016
 
Coauthor
Brian Price
****@adobe.com
20152016
 
PhD Advisor
Ahmed Elgammal
****@cs.rutgers.edu
20112016
 
Coauthor
Babak Saleh
****@cs.rutgers.edu
20112016
 
Coauthor
Dan Yang
****@cqu.edu.cn
20112014
 
Coauthor
Sheng Huang
****@gmail.com
20112014
 
Coauthor
Ahmed Elgammal
****@cs.rutgers.edu
20112014
 
Coauthor
Amr Bakry
****@cs.rutgers.edu
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

Computer Vision, Machine Learning
Present
 
Computer Vision
Present
 
AI
Present
 
Machine Learning
Present