Michael Eickenberg (Preferred)
Michael Eickenberg
Flatiron Institute
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.
****@berkeley.edu
, ****@nsup.org
, ****@inria.fr
, ****@gmail.com
, ****@flatironinstitute.org
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.
Researcher
Flatiron Institute (flatironinstitute.org)
2020 – Present
Postdoc
University of California Berkeley (berkeley.edu)
2017 – 2020
Postdoc
Ecole normale superieure (ens.fr)
2015 – 2017
PhD student
Inria Saclay (inria.fr)
2012 – 2015
PhD student
INRIA (inria.fr)
2012 – 2015
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Postdoc Advisor
Jack Gallant
****@berkeley.edu
2017 – 2020
Postdoc Advisor
Stephane Mallat
****@ens.fr
2015 – 2017
Coauthor
Gael Varoquaux
****@inria.fr
2014 – 2017
Coauthor
Alexandre Gramfort
****@inria.fr
2012 – 2017
Coauthor
Bertrand Thirion
****@inria.fr
2012 – 2017
Coauthor
Claude Comtat
****@cea.fr
2015 – 2015
Coauthor
Olivier Grisel
****@inria.fr
2015 – 2015
Coauthor
Philippe Ciuciu
****@cea.fr
2014 – 2015
Coauthor
Alexandre Abraham
****@gmail.com
2014 – 2015
PhD Advisor
Bertrand Thirion
****@inria.fr
2012 – 2015
Coauthor
Jean Kossaifi
****@imperial.ac.uk
2014 – 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
neuroscience
Present
machine learning
Present
signal processing
Present