Julien Martel

Stanford University

Names

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

Julien Martel
,
Julien N. P. Martel

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.

****@ini.ch
,
****@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.

Postdoc
Stanford University (stanford.edu)
20192021
 
PhD student
Swiss Federal Institute of Technology (ethz.ch)
20142018
 
MS student
University of Zurich (uzh.ch)
20132014
 

Advisors, Relations & Conflicts

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

Postdoc Advisor
Gordon Wetzstein
****@stanford.edu
20192021
 
Coauthor
Piotr Dudek
****@machester.ac.uk
20142021
 
PhD Advisor
Matthew Cook
****@ini.ethz.ch
20142018
 
Coauthor
Srinivas Turaga
****@mit.edu
20162016
Coauthor
Jan Funke
****@ini.ch
20142016
Coauthor
Jan Funke
****@iri.upc.edu
20142016
Coauthor
Alessandro Giusti
****@idsia.ch
20142014
Coauthor
Luca Gambardella
****@idsia.ch
20142014
Coauthor
Dan Ciresan
****@idsia.ch
20142014
Coauthor
Juergen Schmidhuber
****@idsia.ch
20142014
Coauthor
Stephan Gerhard
****@janelia.hhmi.org
20142014
Coauthor
Matthew Cook
****@ini.uzh.ch
20112014
Coauthor
Albert Cardona
****@janelia.hhmi.org
20112014
Coauthor
Jan Funke
****@ini.uzh.ch
20112014
Coauthor
Christophe Garcia
****@liris.cnrs.fr
20112014
Coauthor
Hanspeter Pfister
****@seas.harvard.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

Deep Learning, Meta learning
Present
 
Computer Vision
Present
 
Sensors, Imaging Sensors, Computational Sensors, Focal Plane Processors
Present
 
Computational Imaging
Present