Baoyuan Wu (Preferred)
Baoyuan Wu
The Chinese University of Hong Kong, Shenzhen
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
, ****@kaust.edu.sa
, ****@tencent.com
, ****@cuhk.edu.cn
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.
Associate Professor
The Chinese University of Hong Kong, Shenzhen (cuhk.edu.cn)
2020 – Present
Principal Research Scientist
Tencent (tencent.com)
2019 – 2020
Senior Research Scientist
Tencent (tencent.com)
2016 – 2018
Post Doc
KAUST (kaust.edu.sa)
2014 – 2016
PhD student
Institute of automation, Chinese academy of science (nlpr.ia.ac.cn)
2009 – 2014
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coauthor
Yiming Li
****@mails.tsinghua.edu.cn
2020 – 2023
Coauthor
Bernard Ghanem
****@kaust.edu.sa
2015 – 2016
Coauthor
Siwei Lyu
****@cs.albany.edu
2011 – 2016
Coauthor
Siwei Lyu
****@albany.edu
2011 – 2014
Coauthor
Shangfei Wang
****@ustc.edu.cn
2011 – 2014
Coauthor
Qiang Ji
****@ecse.rpi.edu
2011 – 2014
Coauthor
Yifan Zhang
****@nlpr.ia.ac.cn
2011 – 2014
Coauthor
Qiang Ji
****@rpi.edu
2011 – 2014
Coauthor
Bao-gang Hu
****@gmail.com
2011 – 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
backdoor learning
2019 – 2023
adversarial examples
2018 – 2023
model compression
2018 – 2021
multi-label learning
2014 – 2018