Cheng Zhang (Preferred)
Cheng Zhang
Microsoft
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.
****@disneyresearch.com
, ****@kth.se
, ****@microsoft.com
Personal Links
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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.
Principal Researcher
Microsoft (microsoft.com)
2019 – Present
Researcher
Microsoft (microsoft.com)
2018 – 2018
Postdoc
Disney Research, Disney (disneyresearch.com)
2017 – 2018
Postdoc
KTH (kth.se)
2016 – 2016
PhD student
KTH Royal Institute of Technology (kth.se)
2011 – 2016
PhD student
KTH (kth.se)
2011 – 2016
MS student
KTH Royal Institute of Technology (kth.se)
2009 – 2011
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coworker
Sebastian Nowozin
****@microsoft.com
2018 – Present
Coauthor
Hedvig Kjellstrom
****@kth.se
2016 – Present
Postdoc Advisor
Stephan Mandt
****@disneyresearch.com
2017 – 2017
PhD co-Advisor
Carl Henrik Ek
****@bristol.ac.uk
2012 – 2016
PhD Advisor
Carl Henrik Ek
****@bristol.ac.uk
2012 – 2016
PhD Advisor
Hedvig Kjellstrom
****@kth.se
2011 – 2016
Coauthor
Carl Henrik Ek
****@bristol.ac.uk
2011 – 2016
Coauthor
Tom Minka
****@microsoft.com
2011 – 2016
Coauthor
Andreas Damianou
****@sheffield.ac.uk
2011 – 2016
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
Causality
2017 – Present
Variational Autoencoder
2016 – 2017
Approximate Inference
2013 – 2017
Variational Inference
2011 – 2017
Computer Vision
2011 – 2017
Probabilistic Graphical models
2011 – 2017
Topic Modeling
2011 – 2015