Computing’s Top 30: Bo Han

Bo Han is one of our "Computing's Top 30 Early Career Professionals" for 2025. This program seeks to highlight an esteemed group of rising stars who earned this honor for their exceptional early-career achievements and role in driving advancements across the computing landscape. This interview explores the way this young professional currently contributes to the industry.

Introduction

I am currently an Associate Professor in Machine Learning and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University (HKBU), and a BAIHO Visiting Scientist of Imperfect Information Learning Team at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), where my research focuses on machine learning, deep learning, foundation models, and their applications. I was a Visiting Associate Professor at HKUST(GZ) DSA (2025), a Visiting Research Scholar at MBZUAI MLD (2024), a Visiting Faculty Researcher at Microsoft Research (2022) and Alibaba DAMO Academy (2021), and a Postdoc Fellow at RIKEN AIP (2019-2020). I received my Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019). I have co-authored two machine learning monographs, including Trustworthy Machine Learning under Imperfect Data (Springer Nature) and Trustworthy Machine Learning from Data to Models (Foundations and Trends). I have served as Senior Area Chairs of NeurIPS and ICML, and Area Chairs of ICLR, UAI and AISTATS. I have also served as Associate Editors of IEEE TPAMI, MLJ and JAIR, and Editorial Board Members of JMLR and MLJ. I am an ACM Distinguished Speaker and an IEEE Senior Member. I received paper awards, including Outstanding Paper Award at NeurIPS, Most Influential Paper at NeurIPS, and Outstanding Student Paper Award at NeurIPS Workshop, and service awards, including Notable Area Chair at NeurIPS, Outstanding Area Chair at ICLR, and Outstanding Associate Editor at IEEE TNNLS.

What inspired you to pursue a career in technology?

It was Prof. Andrew Ng – one of the most influential experts in the field of machine learning – opened my eyes to machine learning. It was around 2012; I took an open-source course delivered by Prof. Andrew Ng from Stanford University and learned about machine learning from the course. I was very excited after the course, and I wanted to be a researcher and build my career on machine learning. At that time, machine learning was not that hot yet in Asia. Now, everybody talks about machine learning and AI.

What do you consider your highest achievement so far; How do you plan to continue or build on that success?

As a junior faculty member, I have received the RGC Early CAREER Scheme, IEEE AI’s 10 to Watch Award, IJCAI Early Career Spotlight, INNS Aharon Katzir Young Investigator Award, IEEE Computing’s Top 30 Early Career Professional Award, RIKEN BAIHO Award, Dean’s Award for Outstanding Achievement, and Microsoft Research StarTrack Scholars Program. Among them, I think the highest achievement so far should be IEEE AI’s 10 to Watch Award from IEEE Computer Society. This fantastic award inspires me a lot and makes me feel confident when I tackle many technique challenges to date.

Who do you draw inspiration from and how did that motivate you in your education or career?

I draw inspiration from Prof. Masashi Sugiyama, who was my Postdoc advisor in RIKEN AIP. Specifically, there are three that come to mind. First, we should enhance international collaborations, including research collaborations with Stanford, CMU, Cornell, UT Austin, UTokyo, USYD, UMelbourne, and RIKEN AIP etc. Second, we should enhance community services, including serving area chairs for conferences and associate editors for journals. Third, we should cultivate our leadership in the research community, including establishing Asian Trustworthy Machine Learning (ATML) Fellowships with Prof. Tongliang Liu. Moreover, we should keep focused and treat our research career as a marathon.

How are you currently involved in the tech community aside from your job (volunteering, open-source projects, mentoring, etc)?

I lead a research group called Trustworthy Machine Learning and Reasoning (TMLR) Group at HKBU, and we continuously open source our project codes on GitHub. Recently, we opened source a project called AlphaApollo, which is an agentic reasoning framework that integrates multiple models and tools to enable iterative, verifiable, and self-evolving reasoning. It supports a wide range of agentic reasoning paradigms, including tool-integrated reasoning, agentic post-training (multi-turn SFT and reinforcement learning), and agentic self-evolution. AlphaApollo incorporates multiple post-training algorithms such as PPO, GRPO, and DAPO, and provides dataset-backed agentic evaluation pipelines. AlphaApollo also offers flexible and extensible agentic environments and tool-set configurations, allowing users to easily customize, extend, and scale agentic reasoning workflows.

Is there any emerging technology or industry segment you find exciting or interesting?

Recently, the most exciting thing from my view is agentic AI, which is also a new trend for the whole AI community. To be specific, we observed OpenClaw AI Agent, which is an open-source framework designed to turn traditional chatbots into autonomous “digital workers”. It acts as a personal assistant that runs on your local machine, performing real-world tasks like cleaning inboxes, managing calendars, and browsing the web.

What advice would you give to young professionals or recent graduates who are trying to enter your field?

I have two genuine suggestions. First, we should keep positive for all research challenges. Delving into crowdsourcing research in machine learning when I was a PhD student at University of Technology Sydney, like many young researchers, I struggled through some hard times. After two years of research, I had some minor results, but I was rejected by all the top-tier ML/AI conferences continuously. However, I kept positive and interned at RIKEN AIP and published a “Co-teaching” paper in NeurIPS 2018 (now with more than 3000 citations in Google Scholar). Second, we should keep the status of “always day one”. We should keep focused and treat our research career as a marathon. We should try to update our mindset by learning new things and evolve ourselves to adapt to new AI trends.

Make Connections

Linkedin: Bo Han, Associate Professor at Hong Kong Baptist University, Hong Kong

Build Your Legacy

IEEE Computer Society is sunsetting the Computing’s Top 30, but if you’re looking for ways to start building your own achievements within the community, you can explore ways to get involved at  IEEE Computer Society.