Karol Hausman (Preferred)
Karol Hausman
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.
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
, ****@google.com
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.
Adjunct Professor
Stanford University (stanford.edu)
2021 – Present
Research Scientist
Google Brain (google.com)
2018 – Present
PhD student
University of Southern California (usc.edu)
2013 – 2018
MS student
Technical University of Munich (tum.edu)
2011 – 2013
MS student
Warsaw University of Technology (pw.edu.pl)
2007 – 2012
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coworker
Chelsea Finn
****@cs.stanford.edu
2018 – 2021
Coworker
Sergey Levine
****@eecs.berkeley.edu
2017 – 2021
Coworker
Jeannette Bohg
****@stanford.edu
2017 – 2021
PhD Advisor
Gaurav Sukhatme
****@usc.edu
2012 – 2018
PhD Advisor
Martin Riedmiller
****@google.com
2017 – 2017
Coworker
Nicolas Hess
****@google.com
2017 – 2017
Coworker
Jost Tobias Springenberg
****@google.com
2017 – 2017
PhD Advisor
Sergey Levine
****@eecs.berkeley.edu
2016 – 2017
PhD Advisor
Gaurav Sukhatme
****@usc.edu
2013 – 2017
PhD Advisor
Stefan Schaal
****@usc.edu
2013 – 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
Imitation Learning, Learning from Demonstrations
2016 – Present
Deep Reinforcement Learning
2016 – Present
Deep Reinforcement Learning, Robotics
2014 – Present
Robot Learning
2013 – Present