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Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.

• Big Data
• Business Analytics
• Healthcare Analytics
• +4 more

Hailiang Chen serves as Professor in Innovation and Information Management, Assistant Dean (Taught Postgraduate), and Director of the Artificial Intelligence Research Institute at HKU Business School, The University of Hong Kong. His academic journey includes a PhD and MS from Purdue University and a BM from Tsinghua University. Doctoral Degree: Management Information Systems, Purdue University Master Degree: Economics, Purdue University Bachelor Degree: Information Management and Information Systems, Tsinghua University Professor Chen's research spans artificial intelligence, FinTech, social media analytics, and platform economics, with significant contributions to understanding how digital interactions shape financial markets and consumer behavior. His work frequently examines the intersection of technology adoption and economic outcomes, particularly in cryptocurrency markets, live-stream commerce, and venture capital decision-making. His research methodology combines large-scale data analysis with experimental designs to uncover causal relationships in digital ecosystems. His publications in elite journals like Journal of Financial Economics and Management Science demonstrate consistent impact, with multiple ESI Highly Cited Papers. Current projects include Gov-RAG for e-government services and comparative studies of AI search tools. His research has received continuous funding from Hong Kong's Research Grants Council for five consecutive years (2019-2023). Faculty Outstanding Researcher Award, HKU Business School (2022-23) INFORMS ISS Sandra A. Slaughter Early Career Award (2022) Association for Information Systems Early Career Award (2019) Three ESI Highly Cited Papers (Top 1% in field) Professor Chen actively contributes to academic service as Associate Editor for Journal of Management Information Systems and MIS Quarterly , and serves as Program Chair for the International Conference on Smart Finance. His industry collaborations include Alibaba, HSBC, and China Construction Bank, bridging academic research with real-world business applications in AI implementation and digital transformation.

• Artificial Intelligence
• FinTech
• Social Media
• +8 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

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

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

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +4 more

Shiqi Wang is an Associate Professor in the Department of Computer Science at City University of Hong Kong. He holds a Ph.D. from Peking University (2014) and a B.Sc. from Harbin Institute of Technology (2008). His career includes postdoctoral and research roles at the University of Waterloo, Nanyang Technological University, and Microsoft Research Asia. He specializes in semantic/visual communication, AI content management, and image/video quality assessment. Education: Ph.D. in Computer Application Technology (2014), Peking University B.Sc. in Computer Science and Technology (2008), Harbin Institute of Technology Research focuses on Large Visual-Language Models (LVLMs) , Generative Face Video Coding , and Information Forensics . Recent work includes video coding innovations, AI-driven quality assessment, and bias mitigation in facial analysis. Awards include the IEEE Multimedia Rising Star Award (2021) , NSFC Excellent Young Scientist Fund (2020) , and multiple best paper awards at IEEE conferences. He serves as Associate Editor for IEEE Transactions on Image Processing and leads MPEG standardization efforts for generative video coding. Professional activities include TPC roles at ICML, CVPR, and ACM Multimedia. His lab actively collaborates on standards for generative AI and multimedia systems, with a focus on ethical AI and cross-domain applications.

• Semantic and Visual Communication
• AI Generated Content Management
• Information Forensics and Security
• +3 more

Bryan H. Druzin serves as Associate Professor of Law and Legal Theory at The Chinese University of Hong Kong (CUHK) Faculty of Law, teaching jurisprudence and ethics. Previously, he held academic positions at King’s College London, Brunel University London, and the University of Toronto Faculty of Law, and served as former Director of CUHK’s LLM Programmes. His educational background includes a B.A., LL.B., and LL.M. from the University of British Columbia and a PhD in law from King’s College London. Professor Druzin’s research explores institutional theory, international law, global governance, and AI governance through an interdisciplinary lens combining rational choice, game theory, and network dynamics. His seminal contribution applies network effect theory to explain legal standardization and international system stability, examining how governance structures emerge and collapse without centralized authority. This work bridges legal theory with real-world challenges like geopolitical fragmentation and AI regulation. His recent publications (2016-2025) reveal a cohesive trajectory analyzing anarchic governance systems, with growing emphasis on AI governance and geopolitical fragmentation. The articles consistently employ network theory to investigate institutional resilience, norm evolution, and treaty compliance across diverse contexts from investment arbitration to climate agreements. His scientific recognition includes: CU Research Excellence Award 2015 University of British Columbia Graduate Scholarship Award 2007 Hugh M Brock Scholarship Award 2002 No details about student advising or specific research grants appear in the source material. Professor Druzin maintains active affiliations with CUHK Law’s Centre for Legal Innovation and Digital Society (CLINDS), where he co-organized a 2025 conference on AI legal responses, and serves as Non-resident Senior Fellow at Copenhagen’s Centre for International Law, Conflict and Crisis (CILG).

• Institutional Theory
• International Law
• Global Governance
• +3 more

Ravishankar Ramanathan is an Assistant Professor at the University of Hong Kong's School of Computing and Data Science, Department of Computer Science. His research spans Quantum Cryptography, Quantum Information Theory, and Human-Robot Interaction. He holds a PhD from the National University of Singapore (2013), with prior postdoctoral roles at The National Quantum Information Centre (Poland), Université Libre de Bruxelles, and the University of Oxford. His work includes foundational studies in quantum mechanics and advanced robotics applications, with over 30 refereed publications in top journals like Nature Communications and Physical Review Letters. Research interests focus on quantum device-independent protocols, randomness amplification, and human-centric robotics systems. His lab (QIFT Group: https://qift.weebly.com/) explores both theoretical quantum mechanics and practical robotics solutions for mobility assistance and social interaction. Recent publications highlight contributions to quantum cryptography (e.g., noise-tolerant protocols) and autonomous systems (e.g., robotic wheelchair navigation and social robot design). He collaborates internationally, bridging quantum theory with real-world robotics applications.

• Quantum Cryptography
• Quantum Information Theory
• Device-Independent Quantum Information Processing
• +3 more