Michael M. Bronstein

Twitter

Names

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

Michael M. Bronstein (Preferred)
,
Michael Bronstein

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
,
****@usi.ch
,
****@imperial.ac.uk
,
****@twitter.com

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.

Head of Graph ML
Twitter (twitter.com)
20192020
 
Professor
Imperial College London (imperial.ac.uk)
20182020
 
Principal Engineer
Intel (intel.com)
20122019
 
Professor
Università della Svizzera Italiana (usi.ch)
20102019
 
Professor
Tel Aviv University, Technion (tau.ac)
20162018
 

Advisors, Relations & Conflicts

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

Coauthor
Daniel Cremers
****@tum.de
20152016
 
Coauthor
Emanuele Rodola
****@usi.ch
20152016
 
Coauthor
Davide Boscaini
****@usi.ch
20142016
 
Coauthor
Artiom Kovnatsky
****@usi.ch
20112016
 
Coauthor
Jonathan Masci
****@usi.ch
20112016
 
Coauthor
Pierre Vandergheynst
****@epfl.ch
20112016
 
Coauthor
Emanuele Rodola
****@usi.ch
20112016
 
Coauthor
Alexander Bronstein
****@eng.tau.ac.il
20022016
 
Coauthor
Luca Cosmo
****@dais.unive.it
20152015
 
Coauthor
Davide Eynard
****@usi.ch
20122015
 
Coauthor
Ron Kimmel
****@cs.technion.ac.il
20032014
 
Coauthor
Maks Ovsjanikov
****@lix.polytechnique.fr
20102010
 

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

graph neural networks, graph representation learning, geometric deep learning
20152020