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Professor Michael C.L. CHAU serves as Professor and Deputy Area Head of Innovation and Information Management at The University of Hong Kong's Faculty of Business and Economics. He also holds the position of Associate Director at the HKU HKJC Centre for Suicide Research and Prevention, and serves on the HKU Senate and Court. His academic journey includes a Ph.D. in Management Information Systems from the University of Arizona and a B.Sc. in Computer Science (Information Systems) from the University of Hong Kong. Dr. Chau's research spans business analytics, artificial intelligence, web mining, fintech, smart health, and security informatics. His work focuses on applying data/text/web mining techniques to business, education, and social domains. He leads the Artificial Intelligence Research Group at HKU Business School, which has secured significant funding from the Hong Kong Research Grants Council, Food and Health Bureau, and other agencies for projects including 'The Invisible Hand in Crowdfunding' and 'Factors Moderating the Predictive Power of Social Media Sentiment on Stock Returns'. His publication portfolio includes over 150 articles in premier journals like MIS Quarterly, JMIS, and IEEE Transactions, with recent work exploring large language models, hate speech detection, and disaster-related social media analysis. His research demonstrates strong interdisciplinary connections across computer science, information systems, and business applications. INFORMS ISS Design Science Award (2020) IEEE ITSS Leadership Award in Intelligence and Security Informatics (2020) AIS Sandra Slaughter Service Award (2016) HKU Outstanding Young Researcher Award (2014) Faculty Research Postgraduate Supervision Award (2020 & 2025) Cited over 9,400 times (h-index = 47) Dr. Chau actively mentors doctoral students and has supervised numerous PhD graduates who now hold academic positions at institutions including Fudan University, Xi'an Jiaotong-Liverpool University, and the University of Maryland. His research grants portfolio demonstrates sustained funding success across diverse areas including blockchain, mental health applications, and social media analytics. The Artificial Intelligence Research Group he leads maintains strong industry connections through projects with Hong Kong Red Cross and the Hospital Authority.

• Business Analytics
• Artificial Intelligence
• Web Mining and Social Media
• +8 more

Dr. Lingpeng Kong is an Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He obtained his PhD from Carnegie Mellon University in 2017, co-advised by Noah Smith and Chris Dyer. Previously, he was a research scientist at Google DeepMind (2017–2020). His research focuses on natural language processing (NLP), machine learning, and deep learning, particularly in structured prediction and representation learning. He co-directs the HKU NLP Lab. Notable contributions include work on syntactic parsing, neural architecture design, and lifelong learning. He has received an Outstanding Paper Award at EACL 2017. Dr. Kong teaches courses such as Natural Language Processing (COMP3361/COMP7607) and Machine Learning (COMP3314), and has advised multiple research projects at HKU.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Bo Han is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science, where he leads the Trustworthy Machine Learning and Reasoning (TMLR) Group. He also holds a visiting scientist position at the RIKEN Center for Advanced Intelligence Project (RIKEN AIP) in Japan. His research focuses on developing trustworthy and efficient machine learning systems, particularly under imperfect data conditions such as noisy labels, out-of-distribution data, and weak supervision. Bo Han's research interests span Machine Learning , Deep Learning , Foundation Models , Causal Representation Learning , Weakly and Self-supervised Learning , Robustness and Security in Machine Learning , Federated Learning , and AI for Science . His work aims to build intelligent systems that can reliably learn and reason from complex, imperfect real-world data. His recent publications reveal a strong trend toward trustworthy foundation models , robust reasoning with large language models , out-of-distribution detection , privacy-preserving learning , and causal robustness . His research integrates theoretical foundations with practical applications, often published in top-tier venues like NeurIPS, ICML, ICLR, and TPAMI. Notable Awards and Honors: Outstanding Paper Award, NeurIPS Most Influential Paper, NeurIPS IEEE AI's 10 to Watch Award IJCAI Early Career Spotlight INNS Aharon Katzir Young Investigator Award Dean's Award for Outstanding Achievement RGC Early CAREER Scheme Bo Han has been actively involved in the academic community, serving as a Senior Area Chair and Area Chair for NeurIPS, ICML, and ICLR, and as an Associate Editor for IEEE TPAMI, MLJ, and JAIR. He has advised numerous PhD and research students and leads a globally distributed research group. His work is supported by major grants from RGC, NSFC, GDST, RIKEN, and industry partners including Microsoft, Alibaba, Tencent, and Baidu. He also leads research initiatives in Trustworthy Machine Learning , including projects on federated learning, model unlearning, privacy-preserving AI, and robust foundation models, often in collaboration with industry and international institutions.

• Machine Learning
• Deep Learning
• Foundation Models
• +11 more

Dr. Yingyu Liang is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Computer Science and the Musketeers Foundation Institute of Data Science. Previously, he held an Associate Professor position at the University of Wisconsin-Madison and was a postdoc at Princeton University. He earned his PhD from Georgia Tech and holds M.S. and B.S. degrees from Tsinghua University. His research focuses on theoretical foundations of machine learning, particularly optimization and generalization in deep learning, robust machine learning, and practical applications. Key contributions include analyzing neural network feature learning, adversarial robustness, and contrastive learning efficiency. Education: PhD (Georgia Tech, 2014), M.S. (Tsinghua, 2010), B.S. (Tsinghua, 2008) Awards: NSF CAREER Award Labs/Groups: Research Group focused on theoretical ML and algorithm design Publications span top venues like NeurIPS, ICML, and ICLR, addressing topics such as deep learning theory, adversarial robustness, and graph representation learning.

• Machine Learning
• Optimization in Deep Learning
• Generalization in Neural Networks
• +3 more

Professor Tak-Wah Lam is a leading academic at the University of Hong Kong (HKU), serving as Deputy Director of the School of Computing & Data Science and former Head of the Computer Science Department (2018–2023). He joined HKU in 1988 after earning his PhD in Computer Science from the University of Washington. His research spans algorithm design, bioinformatics, and big data analytics, with a focus on applications in health informatics and genomics. He has published over 200 articles, including influential work on bioinformatics software like MEGAHIT and Clair3. He is a Charivate Highly Cited Researcher (2017) and has received multiple grants, including ITF-funded projects totaling over HKD 33 million for research in genomic databases, AI-driven RegTech, and bioinformatics solutions for clinical genetics. Education: BSc from The Chinese University of Hong Kong (CUHK), MS and PhD from the University of Washington. Research interests include algorithms, bioinformatics, and big data analytics. His work bridges theoretical computer science with practical applications in healthcare, such as developing tools for variant calling, genome assembly, and disease risk prediction. He co-founded a startup in 2014 to advance bioinformatics software for clinical DNA sequencing. Key grants include: ITF Tier-2 Project (2021–2023): HKD 7 million for automated karyotype analysis ITF Tier-2 Project (2019–2021): HKD 5 million for AI-driven RegTech ITF Tier-2 Project (2015–2020): HKD 7.6 million for precision medicine genomic databases He is also recognized for teaching excellence, having received awards from HKU's Faculty of Engineering and the Department of Computer Science.

• Algorithms
• Bioinformatics
• Big Data Analytics
• +3 more

Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.

• Computational Photography
• 3D Vision
• AI Security
• +12 more

Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.

• Computer Graphics
• Computer Vision
• Deep Learning
• +3 more

Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021). Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities. Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness. Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.

• Machine Learning
• Cybersecurity
• Artificial Intelligence
• +6 more

Professor Li Chen is a full Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU), with an affiliate appointment at the Academy of Wellness and Human Development. She leads the Positive Intelligence Lab , focusing on intelligent technologies for human well-being. Her research spans conversational AI, explainable AI, recommender systems, and human-computer interaction. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (Nominee for Best PhD Thesis Award) Master in Computer Software and Theory, Peking University, China Bachelor in Computer Science, Peking University, China Her research interests revolve around personalized conversational and explainable AI, with applications in entertainment, education, e-commerce, and mental well-being. She has published over 150 papers in top venues including ACM TOIS, IJHCS, CHI, SIGIR, AAAI, RecSys, and UMAP . Her work has been recognized with awards such as the RecSys Best Student Paper Award (2024), CHI Honourable Mention (2022), and multiple best paper awards at UMAP and UMUAI. The most recent publications reflect a strong trend toward fair, explainable, and user-centric recommender systems , with increasing integration of large language models , mental health applications , and conversational agents . Her research emphasizes user feedback, negative sampling techniques, and evaluation frameworks grounded in real user behavior. Scientific Awards & Recognition: President’s Award for Outstanding Performance in Teaching (Individual), HKBU (2024/25) President’s Award for Outstanding Performance in Research Supervision (2022/23) World’s Top 2% Most-Cited Scientists, Stanford University (2021–2024) ACM Senior Member (2015) RecSys’24 Best Student Paper Award CHI’22 Honourable Mention Award UMAP’20 Best Student Paper Award UMUAI 2018 Best Paper Award THE Awards Asia 2021 Excellence and Innovation in the Arts (Co-I) Professor Chen is actively involved in mentoring PhD and Master’s students such as Wanling Cai and Yuhan Zhao, who have co-authored award-winning papers. She has secured research funding through grants like the HKBU IRCMS Project. Her editorial leadership includes serving as Co-Editor-in-Chief of ACM Transactions on Recommender Systems (TORS) , Associate Editor for ACM TiiS , and Editorial Board Member for UMUAI . She has chaired major conferences including ACM RecSys’23 (General Co-Chair), RecSys’20 (Program Co-Chair), and UMAP’18 (Program Co-Chair). She leads the Positive Intelligence Lab , which conducts interdisciplinary research on AI for well-being. The lab has developed datasets like the Intent Annotation of Recommendation Dialogue (IARD) and focuses on user-centric AI design, mental health chatbots, and personalized recommendation interfaces.

• Conversational AI
• Explainable AI
• Recommender Systems
• +7 more

Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.

• Big Data Management
• Data Mining
• Graph Learning
• +9 more

Professor David Xu is a distinguished faculty member in the Department of Information Systems at City University of Hong Kong, where he has served as Professor since 2023 after progressing from Associate Professor (2017-2023). Prior to joining CityU, he held academic positions at Wichita State University from 2011-2017, culminating in the Bomhoff Endowed Professor of Business title in 2017. His educational background includes a PhD in Management Information Systems from the University of British Columbia (2011), an MPhil in Information Systems from City University of Hong Kong, and a First-class honors BBA in Business Administration from Lingnan University. As Programme Leader for both BBA Information Management and Bachelor's Degree in Information Systems programs since 2018, he plays a significant administrative role in curriculum development. Professor Xu's research spans human-computer interaction, artificial intelligence applications, and technology adoption across diverse domains. With over 90 publications including 40+ journal papers in top-tier venues like MIS Quarterly and Information Systems Research, his work demonstrates exceptional scholarly impact. His Google Scholar metrics (3,300+ citations, h-index of 22) reflect substantial influence in the field. His recent publications reveal a strategic focus on AI ethics, information cocoon mitigation in social media, healthcare applications of AI, and digital transformation effects on business. The work spans theoretical contributions and practical implementations, with increasing emphasis on societal impacts of technology. AIS Early Career Award (2018) AIS Distinguished Member – Cum Laude (2020) Multiple teaching excellence awards (2021, 2024, 2025) ICIS and ISR Best Associate Editor Awards (2020-2021) Numerous paper award nominations across major conferences Professor Xu has secured significant research funding including NSFC, RGC GRF, and CityU Strategic Research Grants totaling millions in funding. His current projects address critical issues like AI beauty filters, depression treatment systems, and information cocoon mitigation. As Senior Editor for Information Systems Journal and Associate Editor for Information Systems Research, he shapes the field's scholarly discourse while supervising DBA and PhD students across multiple programs.

• Human-computer interaction
• Artificial Intelligence
• Recommendation systems
• +5 more

Dr. Qiuyu (Luca) Lu is an Assistant Professor (Presidential Young Scholar) at the School of Design of The Hong Kong Polytechnic University and Director of the Interbeing Lab , which focuses on designing intelligence for mutualism between humans, nature, and machines. He holds a Ph.D. in Design and a B.Eng. in Mechanical Engineering from Tsinghua University. Ph.D. in Design, Tsinghua University B.Eng. in Mechanical Engineering, Tsinghua University His research bridges Ecological HCI, Computational Design , and Bio-inspired Engineering , creating sustainable interactive systems that leverage natural processes. Recent projects include: RoboMoRe (2025): LLM-driven robotic morphology optimization Degrade to Function (2024): Programmable eco-degradable interfaces Sustainflatable (2023): Ambient energy harvesting for morphing devices His work has been exhibited internationally at Ars Electronica and the Milan Triennale . Scientific recognition includes a Best Paper Honorable Mention at UIST 2023. Dr. Lu is actively recruiting PhD students and postdocs across disciplines, including design, computer science, mechanical engineering, and materials science, with research tracks in: Experimental Track: Interactive hardware prototyping Computational Track: AI-aided design tools Design Track: User experience frameworks

• Ecological HCI
• Computational Design
• Bio-inspired & Biomimetic Design
• +3 more

Zihao Fu is a Research Assistant Professor at The Chinese University of Hong Kong (CUHK), specializing in Large Language Models (LLMs) and Natural Language Processing (NLP) . His work bridges computational methods with social sciences, focusing on model instability, repetition, hallucination, and algorithmic fairness. He has held research positions at the Oxford Internet Institute and the Language Technology Lab at the University of Cambridge. Education Ph.D. in Systems Engineering and Engineering Management (2017–2021), CUHK M.Eng. in Aeronautics and Astronautics (2012–2015), Beihang University B.Eng. in Automation Science (2008–2012), Beihang University His research explores the theoretical foundations of LLMs, addressing challenges like repetition , instability , and hallucination . He integrates linguistics , cognitive science , and fairness frameworks to enhance both foundational understanding and real-world applications of language technologies. Recent work includes biomedical NLP applications ( BAND dataset ), parameter-efficient model adaptation, and fairness toolkits ( OxonFair ). Zihao leads projects like BioCaster (disease outbreak monitoring) and CAM-Tool (cloud task distribution). He is actively mentoring postdoctoral researchers at CUHK and CUHK (Shenzhen), with expertise in machine learning , knowledge base integration , and parallel computing systems . His industry experience includes developing distributed algorithms at Alibaba Cloud’s PAI platform.

• Large Language Models
• Natural Language Processing
• Machine Learning
• +7 more

Dr. Can Liu is an Assistant Professor at the School of Creative Media, City University of Hong Kong, where she leads the ERFI Lab (Laboratory of Empirical Research for Future Interfaces). Her research focuses on designing future interfaces for ubiquitous technologies through empirical understanding of human cognition and behavior, with emphasis on multimodal interaction combining physical and digital elements. Education: PhD in Human-Computer Interaction, Université Paris-Sud (France), INRIA labs ex)situ and ILDA MSc in Media Informatics, RWTH Aachen University (Germany) Dr. Liu's research spans three primary domains: AI-assisted Input (using LLMs/NLP to enhance text manipulation and speech interfaces), Spatial Computing (multimodal interfaces for AR/VR and large displays), and Hybrid/Remote Collaboration (supporting intuitive remote interaction through understanding collocated collaboration). Her work integrates empirical user studies with real-world system deployments in public spaces. Recent publications demonstrate strong trends toward LLM-integrated interfaces, wearable computing applications, and novel interaction techniques for foldable devices. Her team consistently publishes at top venues including CHI, UIST, and CSCW, with increasing focus on practical AI integration for everyday tasks. Awards and Recognition: Best Paper Award at ACM CHI 2014 (top 1%) Honourable Mention at ACM CHI 2012 (top 5%) Dr. Liu actively mentors PhD students and researchers while securing substantial research funding including Google Faculty Research Awards, National Natural Science Foundation grants, and RGC Early Career Schemes. She serves on numerous program committees including ACM CHI (Associate Chair 2024, 2022, 2021, 2020, 2019, 2017) and co-organizes research initiatives like the HCIX Summer Research Program. Her laboratory ecosystem includes the ERFI Lab, affiliation with the Augmented Materiality Lab and Kowloon Interaction Center, and active participation in the Greater Bay HCI community, supporting both fundamental research and industry collaboration with partners including Google, Huawei, and Lenovo.

• Human-Computer Interaction
• Artificial Intelligence
• Spatial Computing
• +5 more

Xiaoyu Zhang is a tenure-track Assistant Professor at the School of Creative Media, City University of Hong Kong , where she conducts research at the intersection of computer science and art design. Her work focuses on integrating data visualization and artificial intelligence (particularly natural language processing ) for applications in education , smart manufacturing , and human well-being . She previously held a Postdoc Research Fellow position at the ETH AI Center , Zurich, and earned her Ph.D. in Computer Science from University of California, Davis under Prof. Kwan-Liu Ma, a Master’s from Zhejiang University, and a dual bachelor’s in Digital Media Art/Advertising from Xiamen University. Ph.D. in Computer Science, University of California, Davis M.Sc. in Computer Science, Zhejiang University B.Sc. in Digital Media Art, Xiamen University Her research bridges visual analytics , human-AI collaboration , and explainable AI through interdisciplinary methods. Key application domains include education technology, industrial AI, and health informatics. Recent publications (2021-2025) span venues like IEEE TVCG, IEEE VIS, ACM CHI, and ACL, with a 2022 Honorable Mention Best Paper Award at IEEE VIS. She has also secured multiple US patents and received recognition as UC Davis' Best Graduate Researcher in 2022. She has served as Associate Chair at ACM CHI (2025, 2026), organized the 6th Workshop on Visualization for AI Explainability at IEEE VIS 2023, and contributed to the AI House Davos 2024 committee. Industrial experience includes internships at Meta , Microsoft Research Asia , and Bosch Research . Her teaching portfolio spans courses in Information Visualization , Creative Coding , and Media Computing at CityU, ETH Zurich, and UC Davis.

• data visualization
• visual analytics
• human-computer interaction
• +6 more