Speaker: Dr. Bo Han
         Hong Kong Baptist University

Title"  "Exploring Trustworthy Machine Learning under Imperfect Data"

Date:   Monday; 15 April 2024

Time:   4:00pm - 5;00pm

Venue:  Lecture Theater F
        (Leung Yat Sing Lecture Theater), near lift 25/26


Trustworthy machine learning is one of emerging and critical topics in modern 
machine learning, since most real-world data are easily imperfect, such as 
online transactions, healthcare, cyber-security, and robotics. Intuitively, 
trustworthy intelligent system should behave more human-like, which can learn 
and reason from imperfect data including labels, features, systems and 
prompts. Therefore, in this talk, I will introduce trustworthy machine 
learning from several human-inspired views, including reliability, robustness, 
adaptability and safety. Specifically, reliability will consider uncertain 
cases, namely reliable learning with noisy labels. Robustness will discuss 
adversarial conditions, namely robust learning with adversarial features. 
Adaptability will explore the algorithm interactions, namely adaptive learning 
with federated systems. Safety will investigate harmful prompts in foundation 
models, namely safe reasoning with jailbreak attacks. Furthermore, I will 
introduce the newly established Trustworthy Machine Learning and Reasoning 
(TMLR) Group at Hong Kong SAR and Greater Bay Area.


Bo Han is an Assistant Professor in Machine Learning at Hong Kong Baptist 
University and a BAIHO Visiting Scientist at RIKEN AIP, where his research 
focuses on machine learning, deep learning, foundation models and their 
applications. He was a Visiting Faculty Researcher at Microsoft Research and a 
Postdoc Fellow at RIKEN AIP. He has co-authored two machine learning 
monographs by MIT Press and Springer Nature. He has served as Area Chairs of 
NeurIPS, ICML, ICLR, UAI and AISTATS. He has also served as Action Editors and 
Editorial Board Members of JMLR, MLJ, TMLR, JAIR and IEEE TNNLS. He received 
Outstanding Paper Award at NeurIPS, Notable Area Chair at NeurIPS, Outstanding 
Area Chair at ICLR, and Outstanding Associate Editor at IEEE TNNLS.