Caglar Gulcehre (Preferred)
, aglar Glehre
, Çaglar Gülçehre
Caglar Gulcehre
Deepmind
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.
****@gmail.com
, ****@umontreal.ca
, ****@iro.umontreal.ca
, ****@google.com
Personal Links
Add links to your profiles on other sites. (Optional)
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
Deepmind (google.com)
2017 – Present
Intern
Maluuba (maluuba.com)
2016 – 2017
PhD student
University of Montreal (umontreal.ca)
2012 – 2017
Microsoft
Microsoft (microsoft.com)
2016 – 2016
IBM
International Business Machines (ibm.com)
2015 – 2016
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coworker
Nando de Freitas
****@google.com
2017 – Present
Coworker
Matt Hoffman
****@google.com
2017 – Present
Coworker
Tom Le Paine
****@google.com
2017 – Present
PhD Advisor
Yoshua Bengio
****@gmail.com
2012 – 2018
Coauthor
Samira Ebrahimi Kahou
****@gmail.com
2013 – 2013
Coauthor
Christopher J Pal
****@polymtl.ca
2013 – 2013
Coauthor
Pierre Froumenty
****@polymtl.ca
2013 – 2013
Coauthor
Xavier Bouthillier
****@umontreal.ca
2013 – 2013
Coauthor
Roland Memisevic
****@iro.umontreal.ca
2013 – 2013
Coauthor
Pascal Vincent
****@iro.umontreal.ca
2013 – 2013
Coauthor
Aaron Courville
****@umontreal.ca
2013 – 2013
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
multiagent deep reinforcement learning
2016 – Present
reinforcement learning, imitation learning, demonstrations, attention models
2014 – Present
deep learning
2011 – Present
nlp, natural language understanding
2011 – 2019
optimization
2012 – 2017
cognitive science, cognitive neuroscience
2009 – 2011