جست‌وجوی استاد و پژوهشگر

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.

Edmund Yin Mun LAMمشاهده پروفایل

استاد

• Computational Optical Imaging
• Digital Holography
• Neuromorphic Imaging
• +۳ مورد دیگر

Edmund Yin Mun LAM is a Professor of Electrical and Electronic Engineering at the University of Hong Kong (HKU), with a courtesy appointment in Computer Science. He also serves as Associate Dean (Innovation and Career Development) of HKU's Graduate School. His academic journey includes a B.S., M.S., and Ph.D. from Stanford University. Key roles include Director of the Computer Engineering program and founding Director of the Imaging Systems Laboratory. He has held a Visiting Associate Professorship at MIT. His research focuses on computational optics and imaging, spanning algorithm design to applications in semiconductor manufacturing and biomedicine. Notable contributions include pioneering AI-driven computational imaging and deep learning for holographic microscopy. Awards include the IBM Faculty Award and multiple HKU accolades. He is a Fellow of IEEE, Optica, SPIE, IS&T, IOP, and HKIE, and a founding member of the Hong Kong Young Academy of Sciences. Education: B.S., M.S., and Ph.D. in Electrical Engineering from Stanford University (1995–2000). Research Interests: Computational optical imaging, digital holography, neuromorphic imaging, biomedical imaging, AI integration in computational optics. His work bridges algorithmic innovation with practical applications, emphasizing cross-disciplinary solutions for imaging challenges in healthcare and engineering. Awards and Recognition: Over 400 publications, 30+ advised students, IBM Faculty Award, HKU Outstanding Researcher Award (2019), and multiple teaching awards. His research impacts fields like semiconductor lithography and medical diagnostics. Labs and Teams: Imaging Systems Laboratory (founded), Computer Engineering Program leadership, involvement in SPIE conference organization, and editorial roles in top journals like IEEE Transactions on Biomedical Circuits and Systems.

• Quantum simulations
• Quantum computing
• Quantum resource theories
• +۳ مورد دیگر

Dr. Qi Zhao is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU). He specializes in quantum information science, focusing on quantum simulation, quantum computing, and entanglement detection. His research bridges theoretical foundations and practical applications, contributing to advancements in quantum algorithms and device-independent protocols. Education: B.Sc. and Ph.D. from Tsinghua University (2014, 2018). Postdoctoral positions at University of Science and Technology of China (2019) and Hartree Fellow at University of Maryland (2019–2022). Research Interests: Quantum simulation (especially Hamiltonian simulation), quantum resource theories, self-testing quantum information, and entanglement detection. His work emphasizes practical applications of quantum computers and fundamental quantum mechanics. Recent Achievements: Published in Nature Physics (2025), Communications in Mathematical Physics (2025), and Physical Review Letters . Awarded grants including the 2030 National Science and Technology Major Project (2025) and named MIT Innovators Under 35 Asia Pacific (2024). Awards: MIT Technology Review Innovators Under 35 Asia Pacific 2024, 2030 National Science and Technology Major Project Youth Grant, RGC Early Career Scheme, and NSFC Young Scientists Fund. Advising & Grants: Supervises PhD/MPhil students and leads projects on quantum algorithms, entanglement detection, and quantum advantage. Collaborates with HKUST and HKBU on quantum finance initiatives. Labs & Teams: Co-founder of the HKU-Oxford Joint Lab in Quantum Information, leading a multidisciplinary team in quantum computing and information theory.

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.

• Quantum simulations
• Quantum computing
• Quantum resource theories
• +۴ مورد دیگر

Dr. Qi Zhao is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU). His research focuses on quantum information science, including quantum simulations, quantum computing, quantum resource theories, and self-testing quantum information. He holds a PhD from Tsinghua University (2018) and postdoctoral experience at the University of Science and Technology of China and the University of Maryland's QuICS as a Hartree Fellow. His work bridges theoretical advancements and experimental applications, with contributions to quantum algorithms, entanglement detection, and device-independent protocols. Key research interests include Hamiltonian simulation optimizations, quantum resource theory frameworks, and robust self-testing methodologies. Notable achievements include the 'Innovators Under 35 Asia Pacific 2024' recognition by MIT Technology Review and significant grants such as the 2030 National Science and Technology Major Project of China. His team explores quantum differential equation solvers, entanglement acceleration in simulations, and practical quantum algorithm implementations. Dr. Zhao advises a growing team of PhD and MPhil students, focusing on quantum computing and information theory. His research has led to publications in top journals like Nature Physics , Physical Review Letters , and Communications in Mathematical Physics . Current projects include developing efficient quantum algorithms for scientific computation and exploring the theoretical limits of quantum simulation techniques.

Changwen Chenمشاهده پروفایل

استاد

• Multimedia Communication
• Multimedia Systems
• Mobile Video Streaming
• +۶ مورد دیگر

Professor Changwen Chen is the Chair Professor of Visual Computing at The Hong Kong Polytechnic University and serves as the Interim Dean of the Faculty of Computer and Mathematical Sciences. He is a distinguished member of the Academia Europaea and a Fellow of IEEE and SPIE. Chair Professor of Visual Computing, The Hong Kong Polytechnic University Interim Dean, Faculty of Computer and Mathematical Sciences Member of Academia Europaea (2021) IEEE Fellow (2005), SPIE Fellow (2007) His research spans Multimedia Communication , Computer Vision , and Deep Learning , with a focus on applications in Internet of Video Things (IoVT) , Mobile Video Streaming , and Immersive Video Systems . Current work includes Intelligent Reflecting Surfaces (IRS) for wireless optimization and Hybrid Graph Reasoning for visual dialog systems. Recent publications highlight trends in Multimedia Signal Processing , leveraging Ensemble Learning and Self-supervised Frameworks for tasks like Skeleton-based Exercise Assessment and Motion Estimation . These studies integrate Graph Neural Networks and Visual Dialog Systems to bridge deep learning with real-world applications. Notable scientific awards include: Sigma Xi Excellence in Graduate Research Mentoring Award (2003) Alexander von Humboldt Research Award (2009) SUNY Chancellor’s Award for Excellence in Scholarship (2016) SIGMM Outstanding Technical Achievement Award (2024) Silver Medal at Geneva International Exhibitions (2023) As a prolific editor, he has led IEEE Transactions on Multimedia (2014–2016) and IEEE Transactions on Circuits and Systems for Video Technology (2006–2009), alongside roles in the Proceedings of IEEE and IEEE Journal of Selected Areas in Communications .

YUNG Kai-leungمشاهده پروفایل

استاد

• Precision Engineering
• Space Instrument Design
• Deep Space Explorations
• +۴ مورد دیگر

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.

PerMagnus Lindborgمشاهده پروفایل

دانشیار

• Sound Art
• Composition
• Sonification
• +۱۲ مورد دیگر

PerMagnus Lindborg is a composer, sound artist, and researcher in sound perception with over 150 scholarly publications, compositions, and media artworks. Currently serving as Associate Professor at City University of Hong Kong's School of Creative Media, he teaches sound, music, research skills, and perception while coordinating research degrees. PhD: KTH Stockholm (2015) DEA: Paris (2003) IRCAM: Paris (1999) BMus: Oslo His research focuses on sound perception , sonification , spatial audio , multi-sensory environments , and data-driven art . He pioneered low-cost spatial audio solutions through the Open Ambisonics Toolkit and explores sensory heritage documentation via soundscapes and smellscape analysis in Hong Kong. Recent publications demonstrate trends in climate data sonification , neural audio processing , and immersive sound design . His work intersects computer music , psychoacoustics , and environmental art . Scientific Awards : Best New Director (World Film Carnival, Cannes Short Film Festival, ISA Awards - 2020) First Prize (Stavanger Symphony Orchestra Prize - 2002) Fellowships: The Arctic Circle (2023), SCM Team Research (2020-25), TBA The Current (2016) As principal investigator for Multi-Modal Hong Kong (GRF 2023-25) and Data Art for Climate Action (2020-23), he supervises PhD students in areas like multi-sensory cultural heritage , deep learning audio processing , and virtual reality sound design . He co-founded the SoundLab and DACA Conference .

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.

Dong Xuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Multimedia
• +۴ مورد دیگر

Professor Dong Xu is a faculty member at the University of Hong Kong's School of Computing and Data Science, Department of Computer Science. He holds a PhD from the University of Science and Technology of China (USTC). His research focuses on computer vision, multimedia, and machine learning, with applications in autonomous driving, AR/VR, medical image analysis, and video surveillance. He has published over 150 papers in top venues like CVPR, ICCV, and IEEE Transactions. He has received prestigious awards including IEEE and IAPR Fellowships, and holds editorial roles at ACM Computing Surveys and multiple IEEE Transactions journals. Education: B.Eng. and PhD from USTC (2001, 2005). Postdoctoral work at Columbia University, tenure-track roles at Nanyang Technological University and the University of Sydney. Current research includes developing machine learning methods for vision and multimedia systems. His publications emphasize cross-domain adaptation, neural networks for video analysis, and medical imaging. Recent work explores spatial-temporal modeling, adversarial learning, and generative AI in HCI. Leadership roles include Program Coordinator for ACM Multimedia 2024, steering committee member for ICME, and program co-chair for multiple conferences. Awards highlight his editorial contributions and research impact in pattern analysis. Advising: Supervised students whose work earned Best Student Paper (CVPR 2010) and IEEE Prize Paper (2014). Active in organizing international conferences and training future researchers.

Ruibang Luoمشاهده پروفایل

دانشیار

• Bioinformatics
• Clinical Informatics
• Genomics
• +۲ مورد دیگر

Ruibang Luo is an Associate Professor in the Department of Computer Science at the School of Computing and Data Science, University of Hong Kong, and serves as Assistant Director (Learning Experience & Student Enrichment). His research spans bioinformatics algorithms and clinical informatics, with over 80 publications including ten highly cited papers. \n His educational background includes: \n \n PhD in Bioinformatics from the University of Hong Kong (2010-2015) under Dr. Tak-Wah Lam \n Postdoctoral training at Johns Hopkins University (2016-2017) with Drs. Steven Salzberg and Michael Schatz \n \n Luo's research centers on bioinformatics and clinical informatics , developing algorithms for genomic data analysis. Key areas include variant calling, metagenomics assembly, pan-genome construction, and AI applications in precision medicine. His work bridges computational methods with clinical solutions for genomic surveillance and disease detection. \n Analysis of his recent publications (2021-2022) reveals strong focus on population genomics (Hong Kong Cantonese exomes, Chinese pan-genome), nanopore sequencing for epigenomics/transcriptomics, and AI-driven gene-disease relation extraction. Earlier work established foundational tools like MEGAHIT for metagenome assembly and SOAPdenovo for genome alignment. \n Luo has received significant recognition: \n \n Top 1% Scholars Worldwide by Clarivate Analytics (2019-2023) \n Baidu Research: Worldwide Top 150 Chinese Young Scholars in AI \n MIT Technology Review: Top 10 Innovators Under 35 Asia Pacific (2019) \n Forbes: 30 Under 30 Asia in Healthcare and Science (2017) \n \n As principal investigator, Luo secured grants including RGC GRF for cancer mutation detection using single-molecule sequencing and industrial collaboration with Oxford Nanopore Technologies. He contributed as Co-PI to cardiovascular risk prediction and automated karyotype analysis projects. His group actively translates bioinformatics algorithms into clinical applications through partnerships with medical institutions. \n Luo leads a research team within HKU's Department of Computer Science focused on next-generation bioinformatics tools for clinical genomics, with ongoing work expanding Chinese pan-genome resources and AI diagnostic systems.

Yifan (Evan) Pengمشاهده پروفایل

استادیار

• Computational Optics
• Imager and Sensor Technology
• Holography
• +۵ مورد دیگر

Yifan (Evan) Peng is an Assistant Professor at the University of Hong Kong, jointly affiliated with the Departments of Electrical & Electronic Engineering and Computer Science. He leads the WeLight Lab, focusing on interdisciplinary research at the intersection of Optics, Graphics, Vision, and Artificial Intelligence. His work emphasizes computational imaging systems, holography, and human-centered visual technologies. Education: PhD in Computer Science from the Imager Lab, University of British Columbia Postdoctoral Research Scholar at Stanford University's Computational Imaging Lab Visiting Student Researcher at KAUST's Visual Computing Center and Stanford MS & BS in Optical Science and Engineering from Zhejiang University Research Interests: His research explores computational optics, holographic displays, VR/AR/MR systems, and low-level vision techniques. Recent efforts include developing snapshot hyperspectral imaging systems, lighting-robust machine vision, and neural rendering frameworks for dynamic scenes. He investigates hardware-software co-design in imaging systems and explores applications in medical imaging and mixed reality. Publications: Recent work focuses on advancing holographic display technologies, neural rendering, and hybrid optical-computational imaging systems. Key contributions include metasurface-based AR displays, speckle reduction techniques, and learned optical systems for hyperspectral imaging. His research bridges physical optics with digital algorithms to achieve high-quality imaging and display solutions. Grants & Advising: Hosts visiting scholars and collaborates with industry partners like Ford, Sony, and Intel. Openings exist for PhD students, postdocs, and research assistants in computational imaging and optics. Labs & Teams: Leads the WeLight Lab at HKU, collaborating with global institutions on projects like neural holography and diffractive optics. Active in conferences like SIGGRAPH, CVPR, and ISMAR as program committee member.

Xiaojuan Qiمشاهده پروفایل

استادیار

• 3D Vision
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Xiaojuan Qi is an Assistant Professor in the Department of Electrical and Electronic Engineering at The University of Hong Kong. Her research focuses on 3D vision, deep learning, and AI applications in medical imaging and science. She holds memberships in IEEE and CCF. Education: PhD, The Chinese University of Hong Kong (2018) BEng, Shanghai Jiao Tong University (2014) Postdoctoral Researcher, University of Oxford (prior to HKU) Research Interests: Dr. Qi develops scalable deep learning algorithms for medical and natural image analysis, 3D scene understanding, and neural network behavior in out-of-distribution scenarios. Her work emphasizes label efficiency, geometric reasoning, and cross-modal integration. Lab Contributions: As head of the Computer Vision and Machine Intelligence Lab (CVMI Lab), she explores open-world intelligence, 3D reconstruction, and AI applications in robotics, autonomous systems, and healthcare. Recent lab highlights include 10+ ICCV/CVPR/NeurIPS papers in 2023-2025. Awards: ImageNet Semantic Parsing Challenge (1st Place) Outstanding Reviewer Awards (ICCV 2017/2019) CVPR Doctoral Consortium Travel Award Advising & Grants: Supervises ~20 PhD students (listed in detail). Active in securing grants for embodied AI, medical imaging, and edge computing hardware. Lab Collaborations: Focuses on interdisciplinary projects with Intel, Oxford, and Toronto groups, emphasizing 3D vision, neuromorphic computing, and AI-driven medical solutions.

Weiming Fengمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Discrete Probability
• Sampling Algorithms
• +۳ مورد دیگر

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.

Marius Hofertمشاهده پروفایل

دانشیار

• Dependence Modeling
• Computational Statistics
• Quantitative Risk Management
• +۳ مورد دیگر

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).