Naila Murray (Preferred)
Naila Murray
Meta AI
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.
****@naverlabs.com
, ****@xrce.xerox.com
, ****@cvc.uab.es
, ****@gmail.com
, ****@fb.com
, ****@meta.com
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.
Researcher
Meta AI (meta.com)
2020 – Present
Researcher
Naver Labs Europe (naverlabs.com)
2017 – 2020
Researcher
Xerox Research Centre Europe, Xerox (xrce.xerox.com)
2013 – 2017
PhD student
Computer Vision Center, Universitat Autónoma de Barcelona (cvc.uab.es)
2008 – 2012
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coauthor
Peter Koniusz
****@data61.csiro.au
2021 – 2022
Manager
Jon Almazan
****@xrce.xerox.com
2015 – 2018
Coauthor
Jon Almazan
****@xrce.xerox.com
2015 – 2015
Coauthor
Albert Gordo
****@inria.fr
2015 – 2015
Coauthor
Albert Gordo
****@xrce.xerox.com
2015 – 2015
Coauthor
Herve Jegou
****@gmail.com
2011 – 2015
Coauthor
Luca Marchesotti
****@beautifey.es
2011 – 2015
Coauthor
Luca Marchesotti
****@xrce.xerox.com
2011 – 2015
Coauthor
Herve Jegou
****@inria.fr
2011 – 2014
Coauthor
Maria Vanrell
****@uab.cat
2011 – 2014
Coauthor
Robert Benavente
****@cvc.uab.cat
2011 – 2014
Coauthor
Florent Perronnin
****@xrce.xerox.com
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
image retrieval, cross-modal retrieval
Present
saliency estimation
Present
image aesthetics
Present