Mohamed Elhoseiny (Preferred)
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.
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
Personal Links
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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)
2019 – Present
Visiting Faculty Scholar
Stanford University (stanford.edu)
2019 – 2020
Visiting Researcher
Baidu Research (baidu.com)
2019 – 2019
Postdoc Researcher
Facebook (fb.com)
2016 – 2019
Research Associate
Adobe Research (adobe.com)
2015 – 2016
PhD student
Rutgers University (rutgers.edu)
2011 – 2016
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
2019 – 2020
Coauthor
Xiaolei Huang
****@lehigh.edu
2016 – 2016
Coauthor
Dimitris Metaxas
****@cs.rutgers.edu
2016 – 2016
Coauthor
Harpreet Sawhney
****@sri.com
2015 – 2016
Coauthor
Brian Price
****@adobe.com
2015 – 2016
PhD Advisor
Ahmed Elgammal
****@cs.rutgers.edu
2011 – 2016
Coauthor
Babak Saleh
****@cs.rutgers.edu
2011 – 2016
Coauthor
Dan Yang
****@cqu.edu.cn
2011 – 2014
Coauthor
Sheng Huang
****@gmail.com
2011 – 2014
Coauthor
Ahmed Elgammal
****@cs.rutgers.edu
2011 – 2014
Coauthor
Amr Bakry
****@cs.rutgers.edu
2011 – 2014
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