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.

Bo Han (Preferred)
,
bo han

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

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)
2020Present
 
Adjunct Scientist
RIKEN (riken.jp)
2020Present
 
Researcher
Microsoft Research (microsoft.com)
20222022
 
Postdoc
RIKEN (riken.jp)
20192020
 
PhD student
University of Technology Sydney (uts.edu.au)
20152019
 

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
2021Present
 
Coauthor
Kun Zhang
****@cmu.edu
2021Present
 
Coauthor
Mingming Gong
****@unimelb.edu.au
2021Present
 
Coauthor
Chen Gong
****@njust.edu.cn
2019Present
 
Coauthor
Nannan Wang
****@xidian.edu.cn
2019Present
 
Coauthor
Mingyuan Zhou
****@mccombs.utexas.edu
2018Present
 
Coauthor
Quanming Yao
****@connect.ust.hk
2018Present
 
Coauthor
Gang Niu
****@gmail.com
2018Present
 
Coauthor
Tongliang Liu
****@sydney.edu.au
2018Present
 
Postdoc Advisor
Masashi Sugiyama
****@k.u-tokyo.ac.jp
20192020
 
PhD Advisor
Ivor Tsang
****@uts.edu.au
20152019
 

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

Out-of-distribution Learning
2022Present
 
Graph Representation Learning
2021Present
 
Federated Learning
2021Present
 
Meta and Few-shot Learning
2020Present
 
Adversarial Learning
2018Present
 
Label-noise Learning
2018Present
 
Deep Learning
2017Present
 
Weakly Supervised Learning
2015Present