Bo Han (Preferred)
, bo han
Bo Han
HKBU
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
, ****@riken.jp
, ****@student.uts.edu.au
, ****@comp.hkbu.edu.hk
, ****@a.riken.jp
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.
Assistant Professor
HKBU (comp.hkbu.edu.hk)
2020 – Present
Adjunct Scientist
RIKEN (riken.jp)
2020 – Present
Researcher
Microsoft Research (microsoft.com)
2022 – 2022
Postdoc
RIKEN (riken.jp)
2019 – 2020
PhD student
University of Technology Sydney (uts.edu.au)
2015 – 2019
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coauthor
Bernhard Schölkopf
****@tuebingen.mpg.de
2021 – Present
Coauthor
Kun Zhang
****@cmu.edu
2021 – Present
Coauthor
Mingming Gong
****@unimelb.edu.au
2021 – Present
Coauthor
Chen Gong
****@njust.edu.cn
2019 – Present
Coauthor
Nannan Wang
****@xidian.edu.cn
2019 – Present
Coauthor
Mingyuan Zhou
****@mccombs.utexas.edu
2018 – Present
Coauthor
Quanming Yao
****@connect.ust.hk
2018 – Present
Coauthor
Gang Niu
****@gmail.com
2018 – Present
Coauthor
Tongliang Liu
****@sydney.edu.au
2018 – Present
Postdoc Advisor
Masashi Sugiyama
****@k.u-tokyo.ac.jp
2019 – 2020
PhD Advisor
Ivor Tsang
****@uts.edu.au
2015 – 2019
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
Causal Representation Learning
2021 – Present
Graph Representation Learning
2021 – Present
Federated Learning
2021 – Present
Meta and Few-shot Learning
2020 – Present
Adversarial Learning
2018 – Present
Label-noise Learning
2018 – Present
Deep Learning
2017 – Present
Weakly Supervised Learning
2015 – Present