جستجوی استاد راهنمای دانشگاهی

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.

Xin Huangمشاهده پروفایل

دانشیار

• Graph Data Management
• Big Graph Mining & Visualization
• Social Network Analysis
• +۴ مورد دیگر

Xin Huang is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University (HKBU), Faculty of Science. He serves as Coordinator of Research Postgraduate Programme (PhD&MPhil) and Coordinator of Programming Contest Teams. His educational background includes: Ph.D. in Systems Engineering and Engineering Management from The Chinese University of Hong Kong (2014) B.Eng. in Computer Science from Xiamen University (2010) Huang's research focuses on innovative technologies for large-scale graph data management and analysis. His work spans graph data management including keyword search and graph indexing, graph mining techniques for summarization and decomposition, community search algorithms, and graph learning approaches including graph neural networks. His research integrates theoretical foundations with practical applications in social network analysis and privacy-aware computing. His publication record shows consistent output in top-tier venues with a clear evolution from foundational graph algorithms to more complex applications involving dynamic and uncertain graphs, reflecting the growing importance of graph analytics in real-world systems. Notable awards include: Best Student Paper Award at ACM/IEEE IWLS 2023 Best Faculty Article Award from Chinese Communication Association (2022) HKBU President's Award for Outstanding Performance as Young Researcher (2021) RGC Early Career Award (2020) Best Paper Award at WISE 2019 Huang actively mentors PhD students and postdoctoral fellows, currently supervising five PhD candidates and four postdocs. His research has been supported by competitive grants including the RGC Early Career Award. He serves as Associate Editor for Data Science and Engineering and World Wide Web Journal, and participates extensively in program committees for major conferences including VLDB, ICDE, and WWW. As part of HKBU's Database Research Group, Huang contributes to a vibrant research environment that has secured over $10 million in research funding and published more than 200 papers in top venues.

Juanru Fangمشاهده پروفایل

دانشگاهی

• Data Privacy and Security
• Database Algorithms
• Sampling
• +۳ مورد دیگر

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.

Shenghua Gaoمشاهده پروفایل

دانشیار

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +۴ مورد دیگر

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.

LI Yuanxi, Kristenمشاهده پروفایل

مدرس

• Semantic Multimedia Indexing and Retrieval
• Adaptive Search Engine Design
• Link Prediction Based on Multimedia Big Data
• +۳ مورد دیگر

Dr. LI Yuanxi, Kristen is a Lecturer in the Department of Computer Science at Hong Kong Baptist University. She holds a Ph.D. in Computer Science and an M.Sc. in IT Management from the same institution. Her research focuses on semantic multimedia indexing, adaptive search engines, link prediction using big data, infectious disease modeling, inclusive education, and web usability design. Education: Ph.D. in Computer Science, Hong Kong Baptist University M.Sc. in IT Management, Hong Kong Baptist University Her research explores innovative methods for multimedia retrieval and search engine design, with applications in social media analysis and public health. She actively engages in pedagogical research to enhance teaching methodologies. Recent Workshops: Organized the 2017 Joint Design Workshop on 'Design for Sustainability (D4S)', available on YouTube. Her publications emphasize semantic technologies and ontology-driven systems to improve information retrieval effectiveness.

Ken M. L. Yiuمشاهده پروفایل

استاد

• Databases
• Query Processing
• Spatiotemporal Data
• +۷ مورد دیگر

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.

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.

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.

Siu Ming Yiuمشاهده پروفایل

استاد

• Computer Security
• Cryptography
• Bioinformatics
• +۳ مورد دیگر

Professor Siu Ming Yiu is a faculty member at the University of Hong Kong's School of Computing and Data Science (CDS), holding the position of Professor and Associate Director (TPg) of CDS. He joined the Department of Computer Science after working as an Analyst Programmer. His research focuses on computer security, cryptography, and bioinformatics with notable contributions to secure cloud applications, smart grid privacy, and vehicular network security. Education: BSc in Computer Science (Chinese University of Hong Kong), MS in Computer and Information Science (Temple University), PhD in Computer Science (University of Hong Kong). Research Interests: Professor Yiu specializes in cryptographic protocols, data privacy preservation, and secure system design. He has pioneered work on identity-based encryption for cloud security and privacy-preserving techniques in smart grids. His research bridges theoretical cryptography with practical industrial applications, collaborating with organizations on projects involving secure query systems and forensic data integrity. Awards: He has received multiple accolades including the Outstanding Teaching Award (2009), repeated Teaching Excellence Awards (2001-2010), and the Best Teacher Award (2005, 2009). His publications include impactful work recognized with the ESORICS 2014 Best Paper Award. Grants & Advising: While specific grants are not listed, his involvement in industrial projects indicates active research funding. He has advised on projects involving secure databases (SDB), smart grid authentication (PRGA), and vehicular networks (VSPN). Labs & Teams: Engaged with CDS research groups focusing on cryptography and security, contributing to the School's innovation projects in data science and AI integration with security frameworks.