جست‌وجوی استاد و پژوهشگر

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.

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

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.

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.

Zhou Zhibinمشاهده پروفایل

عضو هیئت علمی

• Human-AI Interaction
• Design Methodology for AI-enhanced UX
• Human-Computer Interaction
• +۶ مورد دیگر

Dr. Zhou Zhibin is a Research Assistant Professor in the School of Design at The Hong Kong Polytechnic University . He is also a member of the Research Centre for Deep Tech Translation (RCDTT) and the PolyU Academy for Interdisciplinary Research . His expertise lies in Human-AI Interaction and Design Methodology for AI-enhanced UX , with a strong foundation in human-computer interaction and artificial intelligence. Education: He earned his PhD in Design Science from Zhejiang University School of Computer Science and Technology . He was also a visiting PhD candidate at Politecnico di Milano , where he worked on an AI-powered platform for package designers. Research Interests: Dr. Zhou's research focuses on Human-AI Interaction , Design Methodology for AI-enhanced UX , Machine Learning , Generative AI , Virtual Reality , and User Experience Design . His work explores the intersection of AI and design, aiming to create transparent and user-friendly AI systems. Research Trends: His recent publications (2021-2025) emphasize transparent AI agents , ML lifecycle tools , generative AI in UX design , and virtual reality applications . These works highlight his commitment to advancing AI-driven design methodologies and improving user experiences through innovative technologies. Consultancy: In 2023, he led a consultancy project on Interaction Design for the Digital Twin for Autonomous Tower Crane . Labs & Teams: He is affiliated with the Research Centre for Deep Tech Translation (RCDTT) and the PolyU Academy for Interdisciplinary Research , fostering interdisciplinary collaboration in AI and design.

Guodong Lyuمشاهده پروفایل

استادیار

• Operations Management
• Supply Chain Management
• Smart City Operations
• +۵ مورد دیگر

Guodong Lyu serves as an Assistant Professor in the Department of Information Systems, Business Statistics and Operations Management at HKUST Business School. His research transforms urban living through operations innovation, emphasizing sustainability and social responsibility in complex systems. Specializing in Operations Management, Professor Lyu pioneers multi-objective stochastic optimization for ride-sourcing markets (e.g., Uber), enhancing real-time supply-demand matching and customer satisfaction. His Locker Alliance Network research revolutionizes last-mile delivery by reducing carbon footprints and operational costs, while capacity allocation studies in resource pooling systems optimize production networks. In e-commerce, his analysis of Amazon's fulfillment models provides risk-adjusted logistics strategies. Professor Lyu bridges academia and industry through high-impact collaborations: developing real-time metro passenger flow policies to alleviate urban congestion and designing machine learning-driven cash transfer rules for equitable poverty alleviation. His holistic approach creates blueprints for smarter, greener, and more connected urban environments, aligning with HKUST's global research leadership.

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).

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +۷ مورد دیگر

Hubert Tsz-Hong Chan is an Associate Professor in the Department of Computer Science, School of Computing and Data Science at The University of Hong Kong. He earned his PhD in 2007 from Carnegie Mellon University under the supervision of Anupam Gupta, followed by post-doctoral research at the Max-Planck-Institut für Informatik (2007-2009). Education PhD in Computer Science, Carnegie Mellon University, 2007 Research Interests Dr Chan's research lies at the intersection of algorithms , combinatorial optimisation , discrete metric spaces , and security & privacy . A recurring theme is the design of provably efficient approximation algorithms for geometric and graph-theoretic problems under realistic or adversarial settings. Representative contributions include polynomial-time approximation schemes (PTAS) for TSP and Steiner Forest in doubling metrics, spectral analysis of hypergraph Laplacians, and foundational work on differential obliviousness and oblivious RAM. Publications & Trends With more than 80 peer-reviewed papers in premier venues such as JACM , SIAM Journal on Computing , Algorithmica , FOCS , SODA , EUROCRYPT , ASIACRYPT , CCS , and WWW , his recent output (2018-2021) demonstrates a shift toward privacy-preserving algorithms, differential obliviousness, and socially-aware optimisation models, often combining rigorous theory with practical datasets like Netflix and Twitter. Scientific Awards IPDPS 2019 Best Paper Award WWW 2018 Honorable Mention Students & Mentoring Dr Chan has successfully graduated 17 PhD and MPhil students and currently mentors 7 PhD candidates and 1 MPhil student. His graduates have secured academic and industry positions worldwide, and their theses frequently build on his funded projects. Research Funding & Labs Since 2012 he has been Principal Investigator on 12 competitive grants from the Hong Kong Research Grants Council (RGC), totalling more than HK$8 million, spanning topics from privacy-preserving aggregation to Byzantine-resilient federated learning. While no dedicated laboratory name is advertised, his group operates within the Security & Privacy and Algorithms Labs in the Department of Computer Science.

Cai Zhenguangمشاهده پروفایل

استاد

• Psycholinguistics
• Cognitive Psychology of Language
• Computational Linguistics
• +۳ مورد دیگر

Prof. Cai Zhenguang is a Professor in the Department of Linguistics and Modern Languages at The Chinese University of Hong Kong (CUHK), where he leads the Language Processing Lab affiliated with the Brain and Mind Institute. His research integrates behavioral, neuroscientific, and computational approaches to investigate language processing mechanisms. His educational background includes a PhD in Psychology from the University of Edinburgh. Prior to joining CUHK, he served as a lecturer at the University of East Anglia and held an ESRC Future Research Leader fellowship at University College London. Prof. Cai's research centers on psycholinguistics and cognitive neuroscience of language, with specific focus on language comprehension, production, and learning mechanisms. His lab investigates how interlocutors influence language processing, Chinese handwriting literacy including character amnesia, and whether large language models exhibit human-like language behaviors. Additional interests include psychophysics of magnitude perception where physical dimensions interact statistically. Recent publications (2024-2025) reveal strong trends in neural correlates of speaker-contextualized comprehension, computational modeling of language adaptation, and AI-human language comparisons - particularly in Chinese language phenomena. Key methodological approaches combine ERP/fNIRS neuroimaging with Bayesian modeling and deep learning techniques. Scientific recognition includes: ESRC Future Research Leader fellowship He has successfully advised multiple PhD students including Zebo Xu, Hanlin Wu, and Xufeng Duan, along with MA students like Tianyi and Yufeng Wu. His research is supported by significant grants: two General Research Fund projects as Principal Investigator (on character amnesia and interlocutor modeling), an Academic Equipment Grant for TMS-EEG systems, and a teaching grant for virtual behavioral research methodologies. The Language Processing Lab maintains active collaborations across disciplines, having organized major conferences including AMLaP Asia and CogSci Hong Kong Meetup, while recruiting researchers with expertise in fMRI, Bayesian modeling, and machine learning for ongoing projects.

Jiannong Caoمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain
• Wireless Sensing and Networking
• +۴ مورد دیگر

Jiannong Cao is the Otto Poon Charitable Foundation Professor in Data Science and Chair Professor of Distributed and Mobile Computing at the Department of Computing, The Hong Kong Polytechnic University (PolyU). He currently serves as Dean of the Graduate School and directs multiple research entities including the Research Institute for Artificial Intelligence of Things (RIAIoT), Internet and Mobile Computing Lab (IMCL), and PolyU's University Research Facility in Big Data Analytics (UBDA). Previously, he was the Head of the Department of Computing from 2011 to 2017. Education : BSc from Nanjing University, MSc and PhD from Washington State University Prof. Cao's research focuses on distributed systems , blockchain , wireless sensing , big data analytics , machine learning , mobile cloud , and edge computing . His work includes over 800 publications, 34 patents, and leadership in 130+ research projects with HK$187 million in grants. Recent research trends highlight his contributions to blockchain interoperability , edge AI optimization , secure GPU execution , federated learning frameworks , and IoT-based control systems . These align with his expertise in distributed computing, blockchain, and data-driven intelligent systems. Scientific Awards : PolyU President Award for Excellent Performance/Achievement in Research Ministry of Education Higher Education Outstanding Scientific Research Output Awards – Natural Science (Second Class) China Computer Federation (CCF) Overseas Outstanding Contribution Award IEEE TCCLD Research Innovation Award Silver Medal at Geneva International Exhibition of Inventions Best Paper Awards across multiple IEEE conferences (2011-2021) Prof. Cao has secured over HK$187 million in grants from agencies like NSFC, MOST, RGC, ITC, and industry partners (Alibaba, IBM, Nokia). He has delivered 50+ keynote/invited talks and serves on editorial boards of top journals including IEEE TC, IEEE TPDS, and ACM ToSN. He leads research labs such as RIAIoT and IMCL, focusing on artificial intelligence of things, blockchain integration, and edge computing systems. Current projects include collaborative edge computing, Web3 policies, and secure autonomous cooperation in edge nodes.

Yupeng LIمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Network Science
• +۹ مورد دیگر

Dr. Yupeng LI serves as Assistant Professor in the Department of Interactive Media at Hong Kong Baptist University, conducting interdisciplinary research at the intersection of algorithmic systems and societal applications. His work bridges theoretical foundations with real-world implementations in networked environments. His academic journey includes: Ph.D. in Computer Science from The University of Hong Kong Postdoctoral research at the University of Toronto Research focuses on robust algorithmic frameworks for complex systems, with core emphases: Machine learning algorithms for data classification and decision-making Network science applications in social and information networks Ethical AI development within system health and smart society contexts Theoretical foundations of algorithmic fairness in economic systems Key recognitions include: Rising Star in Social Computing Award by CAAI IEEE INFOCOM Technical Program Committee Distinguished Membership (2022) Dr. LI actively contributes to top-tier computer science venues (IEEE INFOCOM, ACM MobiHoc) while teaching specialized courses including Algorithmic Culture and Social Media Systems. His work maintains strong ties with ACM and IEEE professional communities through technical committee service and interdisciplinary collaboration.

Ting Kei Pongمشاهده پروفایل

استاد

• Continuous optimization
• Convex relaxations
• First-order methods
• +۳ مورد دیگر

Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.

Akintomide Afolayan Akinsanolaمشاهده پروفایل

استادیار

• Climate Dynamics
• Climate Modeling
• Regional Climate
• +۵ مورد دیگر

Akintomide Afolayan Akinsanola is an Assistant Professor at the University of Illinois Chicago in the Department of Earth and Environmental Sciences. His research focuses on climate dynamics, particularly tropical and mid-latitude precipitation systems, monsoon variability, and climate change impacts. PhD in Climate Science (2019), City University of Hong Kong MSc in Meteorology (2015), Federal University of Technology BSc in Meteorology (2011), Federal University of Technology His work spans climate modeling , extreme weather analysis , and land-atmosphere interactions . Key areas include West African monsoon systems, heatwaves, precipitation erosivity, and aerosol impacts on regional climates. Recent publications (2024-2025) highlight intensified winter precipitation in the US , accelerated African heatwaves , and land surface temperature dynamics in West Africa. His research employs CMIP models, convection-permitting simulations, and machine learning techniques. 2018 : Outstanding Academic Performance Award 2018 : Chow Yei Ching Scholarship 2017 : Research Tuition Scholarship 2017 : UGC-funded Research Award Collaborations include institutions in Norway (University of Bergen), China (CityUHK), and Nigeria (Federal University of Technology). He actively contributes to advancements in high-resolution climate modeling and precipitation simulation through projects like CORDEX and CMIP6.

Qingyan CHENمشاهده پروفایل

استاد

• Computer Simulations and Experiment Measurements of Built Environments
• Energy-efficient Buildings
• Healthy Buildings
• +۶ مورد دیگر

Professor Qingyan CHEN serves as Chair Professor of Building Thermal Science and Director of the PolyU Academy for Interdisciplinary Research (PAIR) at The Hong Kong Polytechnic University, where he leads the Department of Building Environment and Energy Engineering. As Director of PAIR, he oversees 11 research institutes and 6 research centers with over 500 senior researchers and manages more than HK$600 million in annual research funding. His educational background includes a B.Eng. from Tsinghua University (1983), and M.Eng. and Ph.D. from Delft University of Technology (1985, 1988). Prior to his current position, he held academic appointments at Swiss Federal Institute of Technology (ETH-Zurich), TNO in the Netherlands, MIT, and Purdue University where he is now Professor Emeritus. Professor CHEN is a world leader in developing healthy and comfortable built environments. His research spans computational fluid dynamics, indoor air quality, thermal comfort, and energy efficiency in buildings. He has made significant contributions to low-order turbulence models for CFD, including the indoor zero-equation model and fast fluid dynamics. During the COVID-19 pandemic, his research on infectious disease transmission in airplanes and cruise ships gained international attention and was reported by over 80 media outlets worldwide. His recent publications demonstrate a continued focus on sustainable building technologies, infection control in confined spaces, and advanced computational methods for airflow and contaminant dispersion. The research output shows strong emphasis on practical applications of building science to address climate change and public health challenges. Fellow of the Hong Kong Academy of Engineering Sciences Fellow of the International Society of Indoor Air Quality and Climate Willis J. Whitfield Award (2007) John Rydberg Gold Medal (2011) Distinguished Achievement Award from IBPSA (2013) Global STEM Professor (2021) Professor CHEN has secured over US$39 million in research funding throughout his career. He has co-supervised PhD students from multiple international institutions including Eindhoven University of Technology, University of Cagliari, University of Padua, and Queensland University of Technology. As Editor-in-Chief of the journal 'Building and Environment' from 2008 to 2024, he helped grow the journal to publish over 1,000 papers annually. Through PAIR, Professor CHEN directs interdisciplinary research efforts across sustainable cities, health and well-being, and advanced technologies. His work has established significant collaborations with institutions worldwide and has been featured by major media outlets including CNN, BBC, The New York Times, and CCTV.

Pok Yin Victor Leungمشاهده پروفایل

استادیار

• Robotic Fabrication
• Computational Design
• Digital Fabrication
• +۵ مورد دیگر

Pok Yin Victor Leung is an Assistant Professor at the School of Creative Media, where he teaches interactive machines, computational design, and digital fabrication. His work bridges art, technology, and architecture through robotic systems and creative automation. Doctor of Science (ETH Zurich) Master of Science in Architectural Studies (MIT) Bachelor of Arts in Architectural Studies (University of Hong Kong) His research focuses on robotic fabrication, computational design, and custom machine development. Key themes include enhancing human potential through automation, non-repetitive robotic assembly, and timber construction automation. Victor has taught at ETH Zurich, MIT, University of Hong Kong, and Singapore University of Technology and Design. He emphasizes hands-on, curiosity-driven learning. Projects include the 5-axis custom hotwire foam cutter (using Makeblock components), the HyparHut Pavilion (distributed robotic screwdriver assembly), and a CoreXY vertical drawing robot for glass canvases. Scientific awards: Best Paper Award at CAADRIA 2021

• Computer Graphics
• 3D Computer Vision
• User Interaction
• +۵ مورد دیگر

Chi-Wing FU, Philip is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK). He holds dual roles in research and education, including Associate Editor-in-Chief of IEEE Computer Graphics and Applications. His research focuses on computer graphics, 3D vision, and human-computer interaction, with over 100 publications in top venues like SIGGRAPH, CVPR, and IEEE Visualization. Education: B.Sc. (1st Hons), Computer Science & Engineering, CUHK M.Phil., Computer Science & Engineering, CUHK PhD, Indiana University, Bloomington Research Interests: Dr. Fu's work spans 3D shape generation, computational LEGO design, AR visualization, and robotic interaction. He has pioneered projects like Make-A-Shape (large-scale 3D modeling) and DreamStone (text-driven 3D creation). His team also develops tools for medical data visualization and hand-object pose estimation. Recent Trends in Articles: Recent work emphasizes AI-driven creativity (e.g., LEGO art, text-to-3D systems) and real-time AR applications. His publications often bridge theory (e.g., generative models) with practical systems (e.g., user interfaces for design). Awards: Postgraduate Research Output Award (2023) MSRA Fellowship Nomination (2022) Best Associate Editor (IEEE CG&A) Outstanding Reviewer (ICCV 2021, CCF CAD/CG 2023) Advising & Grants: Supervised over 40 PhD/Master students and postdocs. Active in securing grants for projects like computational LEGO design (with Autodesk), medical AR visualization, and 3D generative AI. Collaborates with industry partners like Adobe and Huawei. Labs & Teams: Leads the Computational Design and Visualization Lab, focusing on 3D systems, robotics, and creative AI. Key projects include the LEGO Sketch Art toolchain and the HandShadowPoser AR system.