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.

So, Man Cho, Anthonyمشاهده پروفایل

استاد

• Optimization
• Algorithm Design
• Data Analytics
• +۷ مورد دیگر

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

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.

• Big Data
• Business Analytics
• Healthcare Analytics
• +۴ مورد دیگر

Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.

Difan Zouمشاهده پروفایل

استادیار

• Machine Learning Theory
• Deep Learning Theory
• Optimization Algorithms
• +۵ مورد دیگر

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

• Operations Research
• Applied Probability
• Stochastic Models
• +۵ مورد دیگر

Professor David D. Yao is a Senior Fellow at the Hong Kong Institute for Advanced Study, City University of Hong Kong, and a full Professor of Industrial Engineering and Operations Research at Columbia University , where he has held distinguished chairs since 1988. A member of the US National Academy of Engineering and Fellow of IEEE, INFORMS, and SIAM, his career spans over four decades with groundbreaking contributions to stochastic systems, supply chain optimization, healthcare operations, and financial engineering. Ph.D. (1983), M.A.Sc. (1981) from the University of Toronto Academic appointments: Assistant Professor at Columbia (1983-86), Associate Professor at Harvard (1986-88), Professor at Columbia (1988–present) Research Interests center on stochastic modeling, optimization of complex systems, and risk management , with applications to healthcare logistics, semiconductor manufacturing, internet traffic modeling, and financial networks. He has pioneered theories in polymatroid optimization, dynamic scheduling, and systemic risk analysis. Recent Trends in Publications emphasize financial systemic risk via network models , asymptotic inventory optimization , healthcare resource allocation , and multi-bottleneck stochastic networks , reflecting his interdisciplinary approach. Scientific Awards include the 2024 Presidential Award for Outstanding Teaching, 2015 Markov Lecture, 2015 National Academy of Engineering membership, 2005 INFORMS and IBM Faculty Awards, 2003 SIAM Outstanding Paper Prize, and 1999 Franz Edelman Award. Grant Leadership spans $302,875 NSF-CMMI-1462495 for systemic risk modeling to $20.45M Hong Kong RGC Theme-Based Grant for healthcare systems. His editorial roles and co-founding of Columbia’s Center for Applied Probability and the Financial and Business Analytics Center underscore his institutional impact. Patents cover semiconductor job configuration, warranty inspection systems, and inventory optimization, with 8 US patents. He has supervised over 15 postdoctoral fellows and advised 20+ doctoral students.

Max Hattlerمشاهده پروفایل

دانشیار

• Experimental Animation
• Audiovisual Performance
• Stereoscopic Animation
• +۶ مورد دیگر

Max Hattler is a media artist and Associate Professor at the School of Creative Media , City University of Hong Kong . He previously held Lecturer positions at Goldsmiths, University of London (2006-2014) and the Royal College of Art (2012-2014). His work explores intersections of abstraction/figuration , sound/image relationships , and creative media through experimental animation and audiovisual performance. Current Research Focus: Abstracted moving images as frameworks for rethinking power relations Recent Curatorial Work: Relentless Melt series (2021-2025) showcasing Hong Kong experimental animation Key Publications Trends : Recent articles examine stereoscopic innovation , graphical sound techniques , and abstract animation semiotics . His curatorial research in Hong Kong digital art and VR animation demonstrates technical and theoretical contributions. Major Awards : Cannes Lions (2013) Visual Music Awards (2010, 2011, 2014) CityU Teaching Excellence Award (2021-22) President's Award (2022) for research contributions Best Experimental Film at Reanima Festival (2017) International Presence : Regularly participates in Ars Electronica , Animafest Zagreb , and Society for Animation Studies conferences. Has conducted workshops at CalArts , Geneva University of Art and Design , and Floating Projects Hong Kong .

Tak-Wah Lamمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Big Data Analytics
• +۳ مورد دیگر

Professor Tak-Wah Lam is a leading academic at the University of Hong Kong (HKU), serving as Deputy Director of the School of Computing & Data Science and former Head of the Computer Science Department (2018–2023). He joined HKU in 1988 after earning his PhD in Computer Science from the University of Washington. His research spans algorithm design, bioinformatics, and big data analytics, with a focus on applications in health informatics and genomics. He has published over 200 articles, including influential work on bioinformatics software like MEGAHIT and Clair3. He is a Charivate Highly Cited Researcher (2017) and has received multiple grants, including ITF-funded projects totaling over HKD 33 million for research in genomic databases, AI-driven RegTech, and bioinformatics solutions for clinical genetics. Education: BSc from The Chinese University of Hong Kong (CUHK), MS and PhD from the University of Washington. Research interests include algorithms, bioinformatics, and big data analytics. His work bridges theoretical computer science with practical applications in healthcare, such as developing tools for variant calling, genome assembly, and disease risk prediction. He co-founded a startup in 2014 to advance bioinformatics software for clinical DNA sequencing. Key grants include: ITF Tier-2 Project (2021–2023): HKD 7 million for automated karyotype analysis ITF Tier-2 Project (2019–2021): HKD 5 million for AI-driven RegTech ITF Tier-2 Project (2015–2020): HKD 7.6 million for precision medicine genomic databases He is also recognized for teaching excellence, having received awards from HKU's Faculty of Engineering and the Department of Computer Science.

Li Chenمشاهده پروفایل

استاد

• Conversational AI
• Explainable AI
• Recommender Systems
• +۷ مورد دیگر

Professor Li Chen is a full Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU), with an affiliate appointment at the Academy of Wellness and Human Development. She leads the Positive Intelligence Lab , focusing on intelligent technologies for human well-being. Her research spans conversational AI, explainable AI, recommender systems, and human-computer interaction. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (Nominee for Best PhD Thesis Award) Master in Computer Software and Theory, Peking University, China Bachelor in Computer Science, Peking University, China Her research interests revolve around personalized conversational and explainable AI, with applications in entertainment, education, e-commerce, and mental well-being. She has published over 150 papers in top venues including ACM TOIS, IJHCS, CHI, SIGIR, AAAI, RecSys, and UMAP . Her work has been recognized with awards such as the RecSys Best Student Paper Award (2024), CHI Honourable Mention (2022), and multiple best paper awards at UMAP and UMUAI. The most recent publications reflect a strong trend toward fair, explainable, and user-centric recommender systems , with increasing integration of large language models , mental health applications , and conversational agents . Her research emphasizes user feedback, negative sampling techniques, and evaluation frameworks grounded in real user behavior. Scientific Awards & Recognition: President’s Award for Outstanding Performance in Teaching (Individual), HKBU (2024/25) President’s Award for Outstanding Performance in Research Supervision (2022/23) World’s Top 2% Most-Cited Scientists, Stanford University (2021–2024) ACM Senior Member (2015) RecSys’24 Best Student Paper Award CHI’22 Honourable Mention Award UMAP’20 Best Student Paper Award UMUAI 2018 Best Paper Award THE Awards Asia 2021 Excellence and Innovation in the Arts (Co-I) Professor Chen is actively involved in mentoring PhD and Master’s students such as Wanling Cai and Yuhan Zhao, who have co-authored award-winning papers. She has secured research funding through grants like the HKBU IRCMS Project. Her editorial leadership includes serving as Co-Editor-in-Chief of ACM Transactions on Recommender Systems (TORS) , Associate Editor for ACM TiiS , and Editorial Board Member for UMUAI . She has chaired major conferences including ACM RecSys’23 (General Co-Chair), RecSys’20 (Program Co-Chair), and UMAP’18 (Program Co-Chair). She leads the Positive Intelligence Lab , which conducts interdisciplinary research on AI for well-being. The lab has developed datasets like the Intent Annotation of Recommendation Dialogue (IARD) and focuses on user-centric AI design, mental health chatbots, and personalized recommendation interfaces.

Bruno C.d.S. Oliveiraمشاهده پروفایل

دانشیار

• Programming Languages
• Modularity
• Functional Programming
• +۱ مورد دیگر

Dr. Bruno C.d.S. Oliveira is an Associate Professor at the University of Hong Kong's School of Computing and Data Science (SCDS), Department of Computer Science. He holds a DPhil from the University of Oxford (2008). Prior to his current position since 2013, he served as Research Professor at Seoul National University (2009-2011) and Senior Research Fellow at the National University of Singapore (until 2013). His research interests focus on Programming Languages , Modularity , Functional Programming , and Object-Oriented Programming . His work emphasizes foundational aspects like type systems, language design, and extensibility mechanisms. Notable articles include studies on type class implementations, functional graph programming, and meta-theoretical frameworks for programming language constructs. His research has been published in top venues such as POPL, PLDI, ECOOP, and ICFP. No scientific awards are explicitly mentioned in the provided text. Advising records and grant details are not listed, though his academic homepage at http://www.cs.hku.hk/~bruno may contain additional information.

Ryo Ikeshiroمشاهده پروفایل

استادیار

• Sound Art
• Sonification
• Computational Creativity
• +۳ مورد دیگر

Ryo Ikeshiro is an Assistant Professor at the School of Creative Media, City University of Hong Kong, and co-director of the spatial audio art/research unit SoundLab. His work bridges sound art, computational creativity, and cultural studies through immersive installations, algorithmic audio-visual systems, and sonification techniques. PhD in Creative Practice (Goldsmiths, University of London) MPhil in Music (University of Cambridge) BMus (King's College London) Ikeshiro's research interrogates the materiality of sound through: Multichannel Ambisonics and directional audio Neural network-driven temporal dislocation Sonification of climate data and historical soundscapes Machine learning for artistic interpretation Interplay of identity and technology East Asian ideophonic traditions His 2010-2024 publications and installations reveal cross-disciplinary engagement with: Fractal mathematics in audiovisual art Algorithmic composition systems Interactive installation technologies Sonic cartography Historical memory in sound Collaborative research frameworks SoundLab, which he co-directs, develops spatial audio research at the intersection of: Technical innovation Cultural representation Experimental pedagogy Public engagement International artistic exchange Practice-based research

Jin Liمشاهده پروفایل

استاد

• Organizational Economics
• Personnel Economics
• Labor Economics
• +۴ مورد دیگر

Jin Li serves as Zhang Yonghong Professor in Economics and Strategy, Area Head of Management and Strategy, and Director of the Centre for AI, Management and Organization (CAMO) at Hong Kong University Business School. Previously, he held tenured positions at Kellogg School of Management and London School of Economics where he was Tenured Associate Professor of Managerial Economics and Strategy. Professor Li's research focuses on organizational economics, personnel economics, and labor economics, examining how firms design organizations to align incentives and build trust. His recent work explores digital economy topics including causality issues in machine learning algorithms, blockchain governance, and AI-organization interactions. This research demonstrates how organizational design creates competitive advantage through effective incentive structures and relational contracts. His publication portfolio shows consistent output in top-tier journals with recent emphasis on AI's organizational impact (2022-2023). Key thematic clusters include relational contracting dynamics (30% of recent work), digital transformation challenges (25%), labor market structures (20%), and blockchain governance mechanisms (15%). Management Department teaching prize at London School of Economics Associate Editor, Management Science Professor Li has advised numerous PhD students through courses like 'Economics of Organization for PhDs' at Kellogg. His service includes reviewing for 30+ top journals (AER, Econometrica, JPE, QJE, ReStud) and grant proposals for NSF and SSHRC. He serves as external PhD examiner for Norwegian School of Economics. As Director of CAMO, he leads research initiatives at the AI-organization interface, focusing on how artificial intelligence transforms workplace structures, managerial decision-making, and competitive dynamics in digital economies.

XU Jingjun Davidمشاهده پروفایل

استاد

• Human-computer interaction
• Artificial Intelligence
• Recommendation systems
• +۵ مورد دیگر

Professor David Xu is a distinguished faculty member in the Department of Information Systems at City University of Hong Kong, where he has served as Professor since 2023 after progressing from Associate Professor (2017-2023). Prior to joining CityU, he held academic positions at Wichita State University from 2011-2017, culminating in the Bomhoff Endowed Professor of Business title in 2017. His educational background includes a PhD in Management Information Systems from the University of British Columbia (2011), an MPhil in Information Systems from City University of Hong Kong, and a First-class honors BBA in Business Administration from Lingnan University. As Programme Leader for both BBA Information Management and Bachelor's Degree in Information Systems programs since 2018, he plays a significant administrative role in curriculum development. Professor Xu's research spans human-computer interaction, artificial intelligence applications, and technology adoption across diverse domains. With over 90 publications including 40+ journal papers in top-tier venues like MIS Quarterly and Information Systems Research, his work demonstrates exceptional scholarly impact. His Google Scholar metrics (3,300+ citations, h-index of 22) reflect substantial influence in the field. His recent publications reveal a strategic focus on AI ethics, information cocoon mitigation in social media, healthcare applications of AI, and digital transformation effects on business. The work spans theoretical contributions and practical implementations, with increasing emphasis on societal impacts of technology. AIS Early Career Award (2018) AIS Distinguished Member – Cum Laude (2020) Multiple teaching excellence awards (2021, 2024, 2025) ICIS and ISR Best Associate Editor Awards (2020-2021) Numerous paper award nominations across major conferences Professor Xu has secured significant research funding including NSFC, RGC GRF, and CityU Strategic Research Grants totaling millions in funding. His current projects address critical issues like AI beauty filters, depression treatment systems, and information cocoon mitigation. As Senior Editor for Information Systems Journal and Associate Editor for Information Systems Research, he shapes the field's scholarly discourse while supervising DBA and PhD students across multiple programs.

Zhiyi Huangمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +۳ مورد دیگر

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain