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Prof. Hayden Kwok Hay SO is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Electrical and Electronic Engineering. He currently serves as Acting Director of the School of Innovation and previously co-directed the Computer Engineering Program. His research focuses on reconfigurable computing systems, FPGA-based architectures, and their applications in AIoT, medical imaging, and high-performance computing. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (1998–2007). Prof. So has been recognized with awards such as the IEEE-HKN Teaching Award (2021), Croucher Innovation Award (2013), and multiple teaching excellence awards. He leads the Computer Architecture & System Research Lab (CASR) and co-founded the Joint Lab on Future Cities (JLFC). His work spans FPGA overlay architectures, graph processing systems, and hardware-software co-design for efficient computing. Key research contributions include advancements in FPGA-based reconfigurable systems, sparse dataflow architectures, and medical imaging accelerators. He has secured grants for projects like 'Advanced machine vision guided aquatic surface vehicles' and 'Efficient and Productive Parallel Data Processing in Hybrid FPGA-CPU Clusters.' Prof. So has advised numerous students and researchers, contributing to over 150 peer-reviewed publications. His current projects explore AI hardware acceleration, neuromorphic computing, and FPGA-driven solutions for big data challenges.

• Reconfigurable Computing
• FPGA-Based Systems
• Computer Architecture
• +7 more

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

Professor Tse-Chun Lin serves as Professor and Head of the Finance Area at HKU Business School, The University of Hong Kong. He joined HKU in 2009 as Assistant Professor, achieving tenure and promotion to Associate Professor in 2015 and full Professor in 2018. His research appears in premier journals including American Economic Review, Journal of Financial Economics, and Review of Financial Studies, with coverage in The Economist, WSJ, and Bloomberg. His academic credentials include: Ph.D. in Economics, University of Amsterdam M.Phil. in Economics, Tinbergen Institute M.B.A., National Chengchi University B.A. in Economics, National Taiwan University Professor Lin's research centers on Behavioral Finance, Empirical Asset Pricing, and Financial Markets. He investigates how cognitive limitations, psychological biases, and information asymmetry influence investor decisions and market dynamics. His work frequently employs large-scale datasets and natural experiments to document phenomena like social media's impact on local information advantages, psychological barriers in cross-firm return predictability, and behavioral drivers of trading anomalies. His methodologies often bridge experimental design with real-world market data to isolate causal relationships. Analysis of his recent publications reveals consistent focus on behavioral mechanisms in financial markets, with increasing emphasis on societal implications (e.g., stock market losses linked to domestic violence) and novel data sources (e.g., social media). His work demonstrates methodological rigor through repeated natural experiments and large-sample analyses, contributing significantly to understanding market inefficiencies and investor psychology. Professor Lin has advised doctoral students including Vesa Pursiainen (co-author on crowdfunding research). As Finance Area Head, he leads academic strategy and curriculum development. His research program reflects sustained funding through HKU appointments and collaborative projects with international scholars.

• Behavioral Finance
• Empirical Asset Pricing
• Financial Market

Dr. Difan Zou is an Assistant Professor in the Department of Computer Science at the University of Hong Kong's School of Computing and Data Science. He holds a PhD in Computer Science from UCLA and degrees in Applied Physics and Electrical Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning theory, optimization, and learning structured data such as time-series and graph data, with an emphasis on understanding deep learning's theoretical underpinnings like optimization trajectories and generalization properties. Dr. Zou's academic background includes a B.S. from USTC's School of Gifted Young (Applied Physics) and a M.S. in Electrical Engineering from the same institution. His work bridges theoretical foundations and practical applications, addressing challenges in adversarial robustness, algorithm design for deep neural networks, and explainable machine learning systems in healthcare and finance. His research projects aim to establish rigorous frameworks for deep learning optimization, develop efficient training algorithms, and integrate conventional statistical models with machine learning for improved interpretability. He has received the Bloomberg Data Science Ph.D. Fellowship and has contributed to top-tier conferences like ICML, NeurIPS, and ICLR.

• Machine Learning Theory
• Deep Learning Theory
• Optimization Algorithms
• +5 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

Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University's School of Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. Additionally, he serves as a Senior Fellow of the Hong Kong Institute for Advanced Study at City University of Hong Kong. His distinguished career spans over four decades, with significant contributions to the fields of simulation, computational probability, and stochastic modeling. Professor Glynn received his Ph.D. in Operations Research from Stanford University in 1982 and his B.S. with Honors in Mathematics from Carleton University in 1978. His academic journey began at the University of Wisconsin at Madison (1982-1987) before returning to Stanford, where he has held various leadership positions including Deputy Chair of MS&E (1999-2005), Director of Stanford's Institute for Computational and Mathematical Engineering (2006-2010), and Chair of MS&E (2011-2015). His research interests focus on simulation , computational probability , queueing theory , statistical inference for stochastic processes , and stochastic modeling . Professor Glynn's work has developed algorithms widely used across the field of Monte Carlo simulation, with applications in financial risk management, service systems engineering, logistics, and retail operations. His recent publications demonstrate continued innovation in areas such as numerical methods for stochastic systems, rare-event simulation, and analysis of queueing systems under various traffic conditions, showing a strong trajectory of advancing both theoretical foundations and practical applications. Professor Glynn's scholarly contributions have been recognized with numerous prestigious awards, including: Fellow of INFORMS (2007) Fellow of the Institute of Mathematical Statistics (1998) John von Neumann Theory Prize from INFORMS (2010) Member of the US National Academy of Engineering (2012) Lifetime Professional Achievement Award, INFORMS Simulation Society (2021) Philip McCord Morse Lecturer, INFORMS (2020) Throughout his career, Professor Glynn has mentored numerous doctoral students whose research has made significant contributions to operations research and related fields. His editorial service has been extensive, including founding Editor-in-Chief of Stochastic Systems and service on the editorial boards of leading journals in operations research, probability, and statistics. His professional service extends to numerous advisory boards and committees at national and international levels, reflecting his standing as a leader in his field.

• Simulation
• Computational Probability
• Queueing Theory
• +2 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

Juanru Fang is an Assistant Lecturer in the Department of Computer Science at the University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. She holds a BEng (2020) and PhD (2025) in Computer Science from the Hong Kong University of Science and Technology (HKUST), supervised by Professor Ke Yi. Her research focuses on data privacy and security, database algorithms, and sampling techniques. Notably, her work has been recognized with a Best Paper Award at SIGMOD 2022. Her research interests span privacy-preserving data management, differential privacy, and algorithm design for database systems. Recent publications explore truncation mechanisms under differential privacy and privacy amplification through sampling. She has contributed to both theoretical foundations and practical applications of privacy-preserving technologies. Juanru has collaborated with institutions like Ashwin Machanavajjhala's group and has published in top venues such as TODS, SIGMOD, and CCS. Her work emphasizes balancing privacy guarantees with query accuracy in complex database environments.

• Data Privacy and Security
• Database Algorithms
• Sampling
• +3 more

Prof. Nan Chen is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He holds a PhD from Columbia University (2006) and directs the Bachelor of Engineering in Financial Technology (FinTech) and the Master of Science in Financial Engineering at CUHK-Shenzhen. His research focuses on quantitative finance, systemic risk, Monte Carlo simulation, and reinforcement learning. He has authored over 40 papers in top journals like Operations Research and Review of Financial Studies . Education : BSc and MSc in Probability & Statistics, Peking University (1998–2001) MPhil/PhD in Operations Research, Columbia University (2005–2006) Research Interests : FinTech and quantitative finance Systemic risk modeling and network analysis Reinforcement learning and stochastic control Monte Carlo methods and computational finance Awards & Grants : Best Student Paper Award, INFORMS (2006) Exemplary Teaching Awards (2009, 2017) Over HK$7M in GRF grants (2008–2023) Second Prize, Jiangsu Province Social Science Award (2023) Public Service : Member of Hong Kong's Payment Systems Tribunal and Insurance Appeals Tribunal, and reviewer for major journals like Mathematical Finance and Operations Research . Key Contributions : Pioneered Ito-Taylor expansions for diffusion models, dual methods for stochastic control, and liquidity risk frameworks. Led development of Hong Kong's first FinTech undergraduate program.

• Financial Engineering
• FinTech
• Quantitative Finance
• +7 more

Ir Professor YUNG Kai-leung, BBS, holds multiple prestigious positions at The Hong Kong Polytechnic University including Sir Sze-yuen Chung Professor in Precision Engineering, Chair Professor of Precision Engineering, Associate Head of the Department of Industrial and Systems Engineering, and Director of the Research Centre for Deep Space Explorations. He is a globally recognized expert in precision engineering with decades of experience in space technology development. Professor Yung earned his MSc from Imperial College London and PhD from the University of Plymouth. His professional background includes positions at BOC Advanced Welding Co. Ltd., British Ever Ready Group, and Cranfield Unit for Precision Engineering before joining PolyU in 1986. His research focuses on: Advanced space instrumentation for lunar and planetary exploration Precision mechatronic systems for extraterrestrial applications Control systems for deep space missions Nanoscale fabrication techniques Logistical optimization for space operations His recent publications demonstrate strong interdisciplinary focus combining space technology with AI, blockchain, advanced materials, and autonomous systems. Research consistently addresses challenges in deep space exploration through innovative engineering solutions. Significant honors include: Bronze Bauhinia Star (BBS) - Hong Kong SAR Government (2015) 49th International Exhibition of Inventions Geneva - Bronze Medal (2024) 1010 Award for Innovation (2001) Editor's Choice Award from MDPI Machines (2022) As Director of the Research Centre for Deep Space Explorations, he leads teams developing critical instrumentation for China's space program including the Camera Pointing System for Chang'e-3/4 lunar missions, Surface Sampling Systems for Chang'e-5/6, and Mars Landing Surveillance Camera for Tianwen-1.

• Precision Engineering
• Space Instrument Design
• Deep Space Explorations
• +4 more

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

• Large Language Models
• Graph Learning
• Recommender Systems
• +3 more

Dr. Weiming Feng is an incoming Assistant Professor at the School of Computing and Data Science of The University of Hong Kong. His research focuses on Theoretical Computer Science, particularly sampling and approximate counting algorithms, with applications in Statistics and Learning Theory. Prior to joining HKU, he held postdoctoral positions at The University of Edinburgh, UC Berkeley, and ETH Zürich. Education: PhD in Computer Science from Nanjing University (2021). Research interests include Discrete Probability, Markov Chain Monte Carlo (MCMC), and algorithmic developments for high-dimensional distributions. His work bridges theoretical foundations and practical applications, addressing challenges in computational efficiency and probabilistic modeling. Notable contributions include advancements in deterministic approximation of statistical distances, MCMC derandomization, and fast sampling techniques for combinatorial problems. His publications span top venues like SODA, FOCS, and STOC, reflecting contributions to algorithm design and computational theory. Lab and Team: While specific lab affiliations are not mentioned, his research is embedded within the broader computational and data science initiatives at HKU's School of Computing and Data Science.

• Theoretical Computer Science
• Discrete Probability
• Sampling Algorithms
• +3 more

Marius Hofert serves as Associate Professor of Statistics in the Department of Statistics and Actuarial Science at The University of Hong Kong's School of Computing and Data Science, with office in Room 228 of the Run Run Shaw Building. His research fundamentally addresses statistical dependence structures and computational methodologies for risk assessment. His core research domains include: Copula theory and dependence modeling for complex stochastic systems Advanced Monte Carlo and quasi-random sampling techniques Quantitative risk management frameworks for financial and insurance applications Recent publications demonstrate increasing integration of machine learning with traditional statistical methods, particularly through generative neural networks for dependence modeling. His critical examinations of AI tools like ChatGPT in quantitative contexts reveal methodological pitfalls while exploring new computational frontiers. As Academic Adviser in Risk Management, he mentors PhD candidates with strong mathematical backgrounds. He actively develops open-source statistical software through R packages including copula , nvmix , and qrmtools , maintaining the Quantitative Risk Management tutorial website (qrmtutorial.org).

• Dependence Modeling
• Computational Statistics
• Quantitative Risk Management
• +3 more

Ms. Annette Frömel serves as a Senior Lecturer in German at The Chinese University of Hong Kong, specializing in language pedagogy and cultural studies. Her academic foundation combines European translation expertise with Hong Kong-based applied linguistics research. Educational background includes: Diplom-Übersetzer in Translation (major: English, minor: Chinese) from School of Applied Linguistics and Cultural Studies, University of Mainz, Germersheim MA in Applied Linguistics from University of Hong Kong (2005), dissertation focused on language arts in German classrooms Her research integrates German Language Teaching methodology with digital pedagogy, emphasizing practical application through cultural immersion and literary analysis. Current work explores multimodal learning resources for German instruction, particularly through Moodle-based roleplay simulations and video demonstrations that bridge linguistic theory with classroom practice. Recipient of the 2020 Teaching Development and Language Enhancement Grant (TDLEG 2019-22 Triennium) for "Sample Videos for Moodle Tasks and Roleplays" project. No formal student advisement information is publicly documented.

• German Language Teaching
• Applied Linguistics
• Translation Studies
• +2 more