Cheng Zhang

KTH

Names

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

Cheng Zhang (Preferred)

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

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.

Post Doc
KTH ( )
Present
Researcher
Microsoft (microsoft.com)
20182018
 
Postdoc
Disney Research, Disney (disneyresearch.com)
20172018
 
Postdoc
KTH (kth.se)
20162016
 
PhD student
KTH Royal Institute of Technology (kth.se)
20112016
 
PhD student
KTH (kth.se)
20112016
 
MS student
KTH Royal Institute of Technology (kth.se)
20092011
 

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
2018Present
 
Coauthor
Hedvig Kjellstrom
****@kth.se
2016Present
 
Postdoc Advisor
Stephan Mandt
****@disneyresearch.com
20172017
 
PhD co-Advisor
Carl Henrik Ek
****@bristol.ac.uk
20122016
 
PhD Advisor
Carl Henrik Ek
****@bristol.ac.uk
20122016
 
PhD Advisor
Hedvig Kjellstrom
****@kth.se
20112016
 
Coauthor
Carl Henrik Ek
****@bristol.ac.uk
20112016
 
Coauthor
Andreas Damianou
****@sheffield.ac.uk
20112016
 
Coauthor
Tom Minka
****@microsoft.com
20112016
 

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

Variational Autoencoder
20162017
 
Approximate Inference
20132017
 
Variational Inference
20112017
 
Computer Vision
20112017
 
Probabilistic Graphical models
20112017
 
Topic Modeling
20112015