جست‌وجوی استاد و پژوهشگر

Hayden Kwok Hay SOمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGA-Based Systems
• Computer Architecture
• +۷ مورد دیگر

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.

Liang Yingyuمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization in Deep Learning
• Generalization in Neural Networks
• +۳ مورد دیگر

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.

Cheng, Reynold C.K.مشاهده پروفایل

استاد

• Data Science
• Big Graph Analytics
• Uncertain Data Management
• +۳ مورد دیگر

Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.

Benjamin C.M. Kaoمشاهده پروفایل

استاد

• Database Management Systems
• Data Mining
• Real-time Systems
• +۱ مورد دیگر

Professor Benjamin C.M. Kao is a faculty member in the Department of Computer Science at The University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He holds a BSc from HKU (1989) and a PhD from Princeton University (1995). His career includes roles as a teaching/research assistant at Princeton (1989-1991) and a research fellow at Stanford University (1992-1995). His research focuses on Database Management Systems, Data Mining, Real-time Systems, and Information Retrieval Systems. Notable contributions include S-OLAP for sequence data analysis, collaborative resource discovery in social tagging systems, and algorithms for mining periodic patterns in sequences. He has led research grants such as the GRF-funded 'Online Analytical Processing on Sequence Data' (2008) and computational studies in uncertain data mining (2006). Professor Kao has served on program committees for major computer science conferences and reviewed for leading journals. His work bridges theoretical foundations with practical applications in data systems and information retrieval.

• Big Data Management
• Data Mining
• Graph Learning
• +۹ مورد دیگر

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.

• Astrophysics
• Galaxy Formation
• Stellar Population Synthesis
• +۳ مورد دیگر

Meng Gu is an Assistant Professor in the Department of Physics at The University of Hong Kong, where she conducts research on galaxy formation and stellar population synthesis. She holds the prestigious HKU-100 Scholar position and specializes in understanding the physical mechanisms driving the interplay between star formation and galaxy mass assembly. Education: Bachelor of Science (BSc) from Nanjing University (NJU) Master of Arts (MA) from Harvard University PhD from Harvard University, completed with Prof. Charlie Conroy Prof. Gu's research addresses fundamental questions in astrophysics regarding the stellar initial mass function of galaxies, its global and local variations, and implications for mass measurements. She investigates how environmental factors shape galaxy formation and evolution, particularly focusing on how massive galaxies grow and what spatial information of stellar populations reveals about these processes. Her work combines observational data with advanced modeling techniques to unravel the complex history of galaxy assembly. Analysis of Prof. Gu's publications reveals a consistent focus on massive galaxy evolution, with particular emphasis on the stellar initial mass function, galaxy cluster dynamics, and ultra-diffuse galaxies. Her research spans observational astronomy using major telescope facilities, theoretical modeling, and computational cosmology. The progression of her work shows increasing sophistication in connecting observational data with theoretical frameworks to understand galaxy formation mechanisms. Scientific Awards: HKU-100 Scholar Henry Norris Russell postdoctoral fellow at Princeton University Prof. Gu has secured research funding to utilize major observational facilities including Magellan Telescopes, ESO's Very Large Telescope, and the Sloan Digital Sky Surveys. She is preparing to leverage next-generation facilities like the James Webb Space Telescope (JWST) and Subaru Prime Focus Spectrograph (PFS) survey for future research. Her collaborative work spans multiple international institutions, reflecting the global nature of modern astrophysics research. Prof. Gu is actively involved in observational astronomy projects and collaborates with major research groups working on galaxy evolution. Her work with the MASSIVE survey represents a significant contribution to understanding the most massive galaxies in the nearby universe, utilizing high-quality spectroscopic data to constrain fundamental properties of stellar populations.

• Security and Reliability of VR/AR Systems
• Internet of Things and Mobile Sensing
• Blockchain and Distributed Systems
• +۷ مورد دیگر

Dr. Henry Hong-Ning Dai is an Associate Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He previously held academic positions at Lingnan University and Macau University of Science and Technology, where he advanced from Assistant to Associate Professor. He holds a Ph.D. from the Chinese University of Hong Kong and a D.Eng. from Shanghai Jiao Tong University. Education: Ph.D. in Computer Science and Engineering, Chinese University of Hong Kong (2008) D.Eng. in Computer Technology Application, Shanghai Jiao Tong University (2012) M.Eng. in Computer Science and Engineering, South China University of Technology (2003) B.Eng. in Computer Science and Engineering, South China University of Technology (2000) Dr. Dai's research focuses on security and reliability of VR/AR systems , Internet of Things , blockchain and distributed systems , federated learning , and cyber-physical systems . His work integrates AI, networking, and software engineering to address real-world security and performance challenges in emerging technologies. He has published over 300 papers in top journals and conferences such as IEEE JSAC, TMC, ICSE, INFOCOM, and AAAI, accumulating more than 24,000 citations. The 15 most recent publications (2023–2025) highlight his leadership in VR/AR security (e.g., AcouListener, Meta VR study), blockchain scalability and fairness (e.g., Porygon, Auncel, Justitia), federated and robust learning (e.g., EBS-CFL, FedDP), and edge-AI and wireless security (e.g., HARBOR, Smart Shield). His recent work also explores AI-generated art evaluation and LLM-driven manufacturing systems , showcasing interdisciplinary innovation. Scientific Awards and Recognition: Holder of 1 U.S. patent and 1 Australia innovation patent Winner of more than 17 awards Senior Member of ACM, IEEE, and EAI Dr. Dai has been Principal or Co-Investigator on over 12 research projects totaling HK$16 million, funded by UGC, NSFC, FDCT, and HKBU. He serves as an Associate Editor for IEEE Communications Surveys & Tutorials , IEEE Transactions on Intelligent Transportation Systems , and several other IEEE journals. He has chaired program committees and served on the PC of top conferences including ICSE, KDD, and INFOCOM. He is actively recruiting Ph.D. students and RAs in security, blockchain, and AI. Laboratories and Research Teams: While not explicitly named, Dr. Dai leads a research group focused on secure and intelligent distributed systems, with active projects in blockchain, VR security, and edge AI. His team has developed open-source tools such as VR-SP Detector , PrettySmart , and RLF for smart contract analysis and security assessment.

David Wai-lok Cheungمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• E-commerce Technologies
• +۵ مورد دیگر

Professor David Wai-lok Cheung is a Professor in the Department of Computer Science at the University of Hong Kong (HKU) and Director of the Center for E-commerce Infrastructure Development (CECID). He holds a BSc in Mathematics from the Chinese University of Hong Kong and MSc/PhD in Computer Science from Simon Fraser University, Canada. His research focuses on database systems, data mining, e-commerce technologies, and secure computation. Key contributions include pioneering work on privacy-preserving data mining, XML schema design, and e-commerce infrastructure development. He has led numerous prestigious grants totaling over HK$60M and developed widely adopted open-source tools like the ebXML gateway. Professor Cheung has received awards such as the HKU Outstanding Researcher Award (1999) and the Distinguished Contribution Award (2009 PAKDD). He actively contributes to academia through leadership roles in conferences like PAKDD and CIKM. His research spans secure computation, data interoperability, and bioinformatics, with over 150 publications in top venues like SIGMOD, VLDB, and KDD. **Grants & Funding**: Highlighted grants include HK$6.6M for eLogistics Appliance (2007-2008) and HK$9.198M for a service-oriented e-transaction platform (2006-2008). **Publications**: Over 150 papers in areas like privacy-preserving data mining, XML query processing, and spatiotemporal data analysis. **Service**: Member of RGC Engineering Panel, Hong Kong Deposit Protection Scheme board, and Certification Board for IT Professional Certification.

Alan William Doughertyمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Medical Robotics
• +۳ مورد دیگر

Dr. Alan William Dougherty is a Lecturer at the School of Computing and Data Science, University of Hong Kong. He holds an MEng in Electronic Engineering from King’s College London (2013) and a PhD from Hong Kong Polytechnic University (2019), where he received the prestigious Hong Kong PhD Fellowship. His professional background includes engineering roles in compiler design for edge AI on FPGAs, autonomous robotic systems, and computer vision applications such as human emotion classification and 3D mesh generation from single images. Affiliations: School of Computing and Data Science (HKU) Research Interests: Computer Vision, Machine Learning, Medical Robotics, Edge AI, Autonomous Systems His research spans interdisciplinary areas including immersive online learning systems, human pose estimation using transformers, and computational material science for energy applications. Notable contributions include the SAILS platform for young learners and advanced catalyst discovery methodologies. He integrates practical engineering challenges with theoretical research to address real-world problems. Dr. Dougherty has been recognized for his work with awards such as the Hong Kong PhD Fellowship. His teaching philosophy emphasizes bridging theory and practice, fostering critical thinking in emerging tech fields.

Francis C.M. Lauمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Wireless Networks
• Operating Systems
• +۶ مورد دیگر

Professor Francis C.M. Lau is a Professor at the University of Hong Kong's School of Computing and Data Science, within the Department of Computer Science. He holds an honorary professorship and has contributed extensively to academia through editorial roles and international society involvement, including pioneering the IEEE Computer Society's Distinguished Visitors Program for Asia/Pacific in 1993. His educational background includes a BSc from Acadia University and MMath/PhD from the University of Waterloo. Professor Lau's research focuses on parallel/distributed computing, wireless networks, operating systems, and computer music, with notable publications in journals like Theoretical Computer Science and Ad Hoc Networks . He has led major research grants, including a $7.5M HKU UGC Special Equipment Grant for interdisciplinary systems research. His awards include the IEEE Golden Core Recognition (1998) and the IEEE Third Millennium Medal (2000). He has served on key committees such as the Research Grant Council's engineering panel and Hong Kong Institution of Engineers' accreditation committee. His work bridges theoretical computer science with practical applications in networking, cloud computing, and cultural heritage through computational approaches to art.

Anthony T.C. Tamمشاهده پروفایل

مدرس

• Cluster Computing
• Parallel Computing
• High-speed Communication
• +۲ مورد دیگر

Dr. Anthony T.C. Tam is a Lecturer and Academic Advisor at the School of Computing and Data Science, University of Hong Kong. He holds a BSc (First Class Honors) from Asia International Open University (formerly EAOI), MAppSc from Queensland University of Technology (Australia), and a PhD in Computer Science from HKU (2002). His research focuses on cluster computing, parallel computing, high-speed communication systems, and operating system performance analysis. Over his career, he has been recognized as an outstanding tutor for three consecutive years. His research emphasizes optimizing communication protocols and scheduling algorithms in distributed systems. Recent work includes studies on contention-aware scheduling, gigabit Ethernet-based supercomputing, and realistic communication modeling for clusters. He has contributed to both academic journals and international conferences on parallel and distributed computing systems. Dr. Tam actively advises students in computer science programs and participates in curriculum development. His technical expertise is showcased through his homepage (https://www.cs.hku.hk/~atctam) and publications accessible via institutional repositories. No scientific awards are explicitly mentioned in the provided information.

Heming Cuiمشاهده پروفایل

دانشیار

• Distributed Systems
• Operating Systems
• Cloud Computing
• +۷ مورد دیگر

Dr. Heming Cui is an Associate Professor at the Department of Computer Science, University of Hong Kong, affiliated with the School of Computing and Data Science. He joined HKU in 2015 after completing his PhD at Columbia University, following bachelor's and master's degrees from Tsinghua University. His research focuses on distributed systems, operating systems, and high-performance computing, with a strong emphasis on reliability and security. Dr. Cui leads projects in distributed AI training systems, blockchain frameworks, and secure execution environments, collaborating closely with industries like Huawei. He has received notable awards including the Croucher Innovation Award (2016), HK$5 million RGC Research Impact Fund (2023), and best paper awards at ICSE and ACSAC. His work has led to commercialized systems such as Huawei's MindSpore integration of Fold3D and TICS' UTEE component derived from his secure systems research. Dr. Cui actively recruits PhD students specializing in systems security and database systems, prioritizing candidates with strong systems-building backgrounds. Key grants include leadership in projects totaling HK$30 million, including flagship collaborations with Huawei and RGC grants targeting transaction/analytical processing in edge computing and cloud security. His research spans over 50 publications in top venues like SOSP, NSDI, and IEEE journals, emphasizing reproducibility and industrial impact.

Paolo Mengoniمشاهده پروفایل

مدرس

• Artificial Intelligence
• Complex Network Analysis
• Natural Language Processing
• +۳ مورد دیگر

Dr. Paolo Mengoni is a Lecturer I at Hong Kong Baptist University (HKBU), specializing in Artificial Intelligence and Complex Network Analysis. Originally from Italy, he brings over 20 years of experience as an IT consultant and researcher. His work focuses on AI applications in education, natural language processing, emotion recognition, and autonomous agents. He holds a Ph.D. in Computer Science from the University of Florence and MSc/BSc degrees from the University of Perugia (Italy). Teaching responsibilities include courses like AI and Digital Communication , Basic Programming for Data Science , and Recommender Systems for Digital Media . His research explores topics such as learning analytics, sentiment analysis in social networks, and community detection in collaborative environments. He actively contributes to academic activities, including organizing international workshops like the 2020 Global Virtual Hack and Design Challenge and the HKBU-University of Perugia exchange program. His publications span journals and conferences including IEEE/WIC/ACM Web Intelligence, Future Generation Computer Systems, and IEEE International Symposia. He serves as a reviewer for venues such as IEEE Congress on Evolutionary Computation and the International Conference on Computational Science and Its Applications.

Yang Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۸ مورد دیگر

Dr. Yang Liu is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science. He also serves as the Associate Director of the Health Informatics Center. His academic career spans prestigious institutions including Yale University and Carnegie Mellon University, demonstrating his expertise in both theoretical and applied aspects of computer science. Dr. Liu received his B.Eng. and M.Eng. degrees in Automation from National University of Defense Technology in 2004 and 2007, respectively. He earned his Ph.D. in Computing from The Hong Kong Polytechnic University in 2011. His academic journey included a Visiting Scholar position at Carnegie Mellon University's Robotics Institute (Feb.-Aug. 2010) and a Postdoctoral Research Associate position in the Department of Statistics at Yale University (2011-2012). Dr. Liu's research spans the intersection of artificial intelligence, machine learning, and practical applications in health and complex systems. His work focuses on artificial intelligence , machine learning , pattern recognition , dimensionality reduction , and subspace learning , with particular emphasis on multi-way/multi-view/multi-label/multi-task learning approaches. His research extends to modeling complex dynamical systems with applications in computational epidemiology and infectious disease modeling , addressing critical public health challenges through data-driven approaches. Analysis of Dr. Liu's recent publications reveals a strong focus on applying machine learning techniques to epidemiological challenges, particularly in modeling infectious disease transmission patterns. His work bridges theoretical advances in graph neural networks, subspace learning, and multi-view analysis with practical applications in public health. A significant portion of his research addresses the challenges of high-dimensional and heterogeneous data analytics, with applications ranging from malaria transmission modeling in Cambodia to uncovering COVID-19 transmission patterns in Hong Kong. Dr. Liu is an IEEE Senior Member and ACM Member, reflecting recognition of his contributions to the field. His paper "What are the underlying transmission patterns of COVID-19 outbreak? – An age-specific social contact characterization" was recognized as one of the most cited articles in EClinicalMedicine, Lancet Discovery Science, during 2020-2021, highlighting the impact of his work on pandemic response research. Dr. Liu actively mentors research students and regularly has research student and RA positions available. His professional service includes serving on editorial boards for SPJ Health Data Science and as a journal guest editor for special issues on cross-media analysis. He has extensive experience as a journal reviewer for top publications including IEEE TNNLS/TNN, IEEE T-Cyber/TSMC-B, IEEE TAC, IEEE TKDE, IEEE TMM, IEEE TCSVT, ACM TIST, ACM TOMM, and others, and serves on program committees for major conferences including WWW, IJCAI, and AAAI. Dr. Liu is affiliated with the Centre for Health Informatics and the Artificial Intelligence and Machine Learning Laboratory (AIML) at Hong Kong Baptist University. These research centers provide the infrastructure and collaborative environment necessary for his work in health informatics and machine learning applications. His role as Associate Director of the Health Informatics Center positions him at the forefront of interdisciplinary research connecting computing with public health challenges.

Hubert T.H. Chanمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +۴ مورد دیگر

Professor Hubert T.H. Chan is an Associate Professor at the Department of Computer Science, University of Hong Kong, and serves as Programme Director for the BEng(CompSc) programme. He holds a PhD from Carnegie Mellon University (2007) and previously worked as a postdoc at Max-Planck-Institut für Informatik in Germany. His research focuses on algorithms, combinatorial optimization, discrete metric spaces, and security & privacy, with applications in graph theory, network analysis, and privacy-preserving mechanisms. Education: PhD in Computer Science, Carnegie Mellon University, 2007 BEng (Computer Science), details not specified in text Research interests emphasize algorithmic design for dynamic systems, privacy in data aggregation, and efficient optimization techniques. His work bridges theoretical computer science with practical applications in network security and distributed systems. Key grants include Hong Kong RGC-funded projects on oblivious data structures, spectral hypergraph analysis, and dynamic metric problems (2012–2018). His publications span top conferences like SODA, FOCS, WWW, and ICML, addressing challenges in clustering, sorting, privacy, and graph decomposition. He oversees research groups exploring hypergraph learning, secure computation, and algorithmic privacy. His lab's work often intersects with real-world systems, emphasizing both theoretical rigor and practical relevance.