Michael Chang (Preferred)
, Micahel Chang
, Michael Benjamin Chang
Michael Chang
University of California Berkeley
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.
****@mit.edu
, ****@gmail.com
, ****@berkeley.edu
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.
PhD student
University of California Berkeley (berkeley.edu)
2017 – Present
Undergrad student
Massachusetts Institute of Technology (mit.edu)
2013 – 2017
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
PhD Advisor
Sergey Levine
****@eecs.berkeley.edu
2017 – Present
PhD Advisor
Tom Griffiths
****@princeton.edu
2017 – Present
Coauthor
Chelsea Finn
****@cs.stanford.edu
2018 – 2019
Coauthor
Jiajun Wu
****@cs.gmail.com
2018 – 2019
Coauthor
Pieter Abbeel
****@berkeley.edu
2018 – 2019
Coauthor
Abhishek Gupta
****@berkeley.edu
2017 – 2019
Coauthor
Sjoerd van Steenkiste
****@idsia.ch
2017 – 2018
Coauthor
Klaus Greff
****@startmail.com
2017 – 2018
Coauthor
Jürgen Schmidhuber
****@idsia.ch
2017 – 2017
Coauthor
Joshua Tenenbaum
****@mit.edu
2015 – 2017
Coauthor
Tomer Ullman
****@gmail.com
2015 – 2017
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, Artificial Intelligence, Reinforcement Learning, Compositionality, RNNs, Intuitive Physics
Present