Keynote Speaker | 主讲嘉宾

Prof. Benny C. F. Cheung

The Hong Kong Polytechnic University, Hong Kong

Ir Prof. Benny Cheung is Chair Professor in the Department of Industrial and Systems Engineering of The Hong Kong Polytechnic University. He is an elected Fellow of International Academy of Engineering and Technology (AET Fellow) and the International Academy for Production Engineering (CIRP Fellow). His research interest in Knowledge and Technology Management includes Artificial Intelligence, Knowledge-based Systems, Knowledge Auditing, Intellectual Capital Management, Intellectual Property Management, Technology Forecasting and Roadmapping, Knowledge Mining and Knowledge Systems Technology. His research in KTM and enterprise systems encompasses broad-based research of methods and tools built on the basis of Information Processing and Artificial Intelligence Technologies for supporting the management of knowledge and technology for enterprises from various industries such as Manufacturing, Public Utilities, Social Services, etc. Up to present, he has authored and co-authored more 300 Science Citation Indexed (SCI)/Social Science Citation Indexed (SSCI) refereed journal papers. Prof. Cheung has received many research prizes and awards such as the 2008 ASAIHL-Scopus Young Scientist Awards–First Runner Up Prize in the category of “Engineering and Technology”, Joseph Whitworth Prize 2010 and A M Strickland Prize 2017 by the Manufacturing Industrial Division of The Institution of Mechanical Engineers (IMechE), UK in 2011, Winner of the IET Innovation Award for Manufacturing Technology in 2017, by Manufacturing Industries Division of IMechE, UK, Bank of China Hong Kong (BOCHK) Science and Technology Innovation Prize 2023–Advanced Manufacturing, etc.

Title: Enhancing Technology Adoption and Knowledge Transfer: A Gamification Approach

The increasing technological complexity of products and services significantly encourages people to adopt new technologies. Gamification makes use of game mechanics which provides an important means to motivate and engage end users to adopt new technology and transfer knowledge. In this presentation, the speaker presents a gamification approach to enhance technology adoption based on the extended technology adoption model with gamification effectiveness. A purpose-built gamification system has been developed based on a game-based learning model and game mechanic model, to increase the rate of adoption intention and enhance knowledge transfer of technology. Pre-test and post-test interviews have been conducted to validate the performance of the gamification approach in terms of perceived ease of use, perceived usefulness, perceived effectiveness, perceived attitude, and perceived enjoyment. The results show that the gamification approach is not only able to help users to explore the technology, but also helps the end user to acquire knowledge of the technology's features which facilitates technology adoption and knowledge transfer.


Prof. Mohsen Razzaghi

Mississippi State University, USA

Professor Mohsen Razzaghi received his Bachelor's in Mathematics from the University of Sussex in England, his Master's in Applied Mathematics from the University of Waterloo in Canada, and his doctorate from the University of Sussex. He is currently a professor in the Department of Mathematics and Statistics at Mississippi State University (MSU) in the USA and served as Head of the Department from 2006 to 2025.
Dr. Razzaghi offered two prestigious Fulbright fellowships for the academic years 2011-2012 and 2015-2016. During each period, he spent 9 months at the Technical University of Civil Engineering in Bucharest, Romania, one of Europe's most prestigious universities. In addition, he received two Fulbright Specialist Awards: one at the University of Bucharest in Romania in 2019 and the other at the Prince of Songkla University in Thailand in 2024. Dr. Razzaghi was named a W.L. Giles Distinguished Professor at MSU in 2019. The highest award the University can bestow upon a faculty member. He also received the Robert Wolverton Legacy Award in 2024, the highest honour the College of Arts & Sciences bestows on those who have made sustained contributions to the goals of the liberal arts & sciences.
His research area is Applied and Computational Mathematics, encompassing the development, analysis, and implementation of mathematical models and numerical methods in the sciences and engineering. He is particularly interested in optimal control, fractional calculus, orthogonal functions, and wavelets. According to Scopus, he has over 270 refereed publications in high-quality international journals across applied mathematics, mathematical physics, and engineering. In addition, he has over 9859 citations, and one of his papers, coauthored with one of his Ph.D. students at MSU, has been cited over 900 times.

Title: Hybrid Functions for Management Engineering and Fractional Differential Equations

An efficient mathematical model offers the best promise to assist in the development of an effective means for management engineering. Furthermore, the application of mathematical research methodologies, such as optimization techniques and control theory, is crucial to the development of management engineering. Orthogonal functions may be widely classified into three families. The first consists of sets of orthogonal polynomials, the second consists of sine-cosine functions in the Fourier series, and the third consists of sets of piecewise constant basis functions (PCBFs). While orthogonal polynomials and sine-cosine functions together form a class of continuous basis functions, PCBFs have discontinuities or jumps. For some problems in management engineering, images often have properties that vary continuously in some regions and discontinuously in others. Therefore, neither continuous basis functions nor PCBFs alone can accurately model these spatially varying properties. In these cases, hybrid functions consisting of the combination of PCBFs with orthogonal polynomials proved to be powerful tools in management engineering. Fractional differential equations (FDEs) are generalizations of ordinary differential equations to an arbitrary (non-integer) order. FDEs have attracted increasing attention and interest due to their ability to model complex phenomena. Generally speaking, most of the FDEs do not have exact analytic solutions. Therefore, finding numerical solutions to these equations is significant. This talk first provides an introduction to hybrid functions. Then, an efficient numerical method based on hybrid functions for solving the FDEs is presented. The numerical solutions are compared with available exact or approximate solutions to assess the accuracy of the proposed method.

Prof. Reynold C.K. Cheng

The University of Hong Kong (HKU), Hong Kong, China

Professor Reynold Cheng is currently the Division Head and Professor (AI & Data Science), at the School of Computing and Data Science, in the University of Hong Kong (HKU). He is a Steering Committee Member of the HKU Musketeers Foundation Institute of Data Science. He is an academic advisor to the College of Professional and Continuing Education of HKPU. He was an Associate Dean of Engineering in 2022-24. His research interests are in data science, big graph analytics and uncertain data management.
Prof Cheng was named the AI 2000 Most Influential Scholar Honorable Mention in Database in 2023 to 2025. He received the ACM Distinguished Membership Award and the HKU Outstanding Research Student Supervisor Award in 2023. He was listed as the World’s Top 2% Scientists by Stanford University in 2022. He received the SIGMOD Research Highlights Reward 2020, HKICT Awards (2021, 2023), HKU Knowledge Exchange Award (2024), HKU Engineering Knowledge Exchange Award (2024, 2021), and HKU Engineering Best Teaching Award (2023, 2024), and HKU Outstanding Young Researcher Award 2011-12. He received the Universitas 21 Fellowship in 2011, and two HKPU Computing Performance Awards in 2006 and 2007. He was a PC co-chair of IEEE ICDE 2021. He is on the editorial board of PVLDB, ACM TSAS, IS, DAPD, and DSEJ.

Title: AI for Social Goods: STAR Lab’s Experience”

Many metropolitan cities face a severe shortage of manpower in social care. In Hong Kong, for instance, elderly care homes report a 70% shortfall in staff. To address these challenges, there has been growing interest in leveraging AI for social good—using technology to enhance service quality and streamline administrative tasks for social workers.
In this talk, Prof Cheng will share how the HKU STAR (Social Technology And Research) Lab applies AI and data science to support elderly and family care services. He will introduceHINCare, a software platform designed to foster volunteering and a culture of mutual help within communities. Powered by AI on a Heterogeneous Information Network (HIN), HINCare recommends suitable helpers to elders and other service recipients and currently supports 14 NGOs and 7,000 users.
Prof Cheng will also discuss a collaboration with the Hong Kong Jockey Club Charities Trust to develop an innovative case management and AI-enabled data analysis system for 40% of family care centres in Hong Kong. Over the past five years, the STAR Lab has earned numerous accolades, including the HKICT Awards, Asia Smart App Awards, and HKU Knowledge Exchange Awards.

Invited Speaker | 特邀演讲嘉宾

Assoc Prof. Yu Zhao

Tokyo University of Science, Japan

Dr. Yu Zhao is currently a Junior Associate Professor at the School of Management, Tokyo University of Science. He also serves as a visiting lecturer at the Institute of Statistical Mathematics, Japan. He obtained his Ph.D. in Information Science and Technology from Osaka University. His research primarily focuses on both the theoretical and practical aspects of statistical learning theory, operations research, and management science. His analytical approaches include machine learning and algorithmic learning methods, statistical inference and modeling, and mathematical programming, among others. His work has been published in journals such as Omega – The International Journal of Management Science, The European Journal of Operational Research, Expert Systems with Applications, and other reputable journals.

Title: Mixed-Copula Mixture Models for Robust Clustering of Mixed-Type Data

Clustering mixed-type datasets with missing values is a significant challenge. Traditional distance-based methods suffer from arbitrary variable weighting and inadequate handling of missing data. We propose the Mixed-Copula Mixture Model (MCMM), a probabilistic generative model founded on Sklar’s theorem. This approach separates the modeling of marginal distributions from the dependence structure, allowing for flexible marginal assumptions (e.g., Student’s t-distribution for robustness, cumulative logit for ordinal data) and the capture of complex, cluster-specific correlations. The framework provides a principled method for handling missing data under the MAR assumption without imputation.We validate the proposed method through extensive numerical experiments across nine diverse scenarios. The results demonstrate that MCMM achieves superior clustering accuracy (ARI) and, critically, correct model selection via BIC, significantly outperforming baseline methods like kmeans and k-prototypes. We also present multiple estimation modes, including a pairwise composite likelihood, to balance statistical efficiency and computational cost.


Dr. Kuangzhe Xu

Cyberspace Security Insititute of China (preparation)

Dr. Kuangzhe Xu currently serves as a Lecturer at the General Education Department of the Wuhan University of Cyber Security (under preparation). Previously, he was an Assistant Professor in the Institute for Promotion of Higher Education at Hirosaki University, Japan. Dr. Xu obtained his Ph.D. in Mathematical Information Science from Chiba University, Japan. His main research interests include cognitive science, behavioral science, and data science, with a particular focus on modeling and analyzing human gaze behavior and cognitive mechanisms using Bayesian statistical models. His research methodologies cover a broad spectrum, ranging from traditional statistical modeling to cutting-edge machine learning techniques, and from behavioral experiments to big data analysis. His work spans multiple interdisciplinary fields, including cognitive science, psychology, education, medicine, and language engineering. His research findings have been published in several high-impact international journals such as Scientific Reports and Journal of Eye Movement Research, and one of his papers was awarded by the Hokuriku Branch of the Japan Society for Oriental Medicine.

Title: Personality-Aware Facial Reaction Modeling: Enhancing User Experience in Intelligent Service Systems with DeepFace and Bayesian Lasso

While prior studies have focused on self-reported emotional responses, the dynamic relationship between per-sonality traits and real-time facial expressions during emotion perception remains underexplored. This aspect of research is precisely the key to enhancing intelligent service systems and user experience. To address this gap, this study combined Deep-Face, a deep-learning-based facial analysis tool, with Bayesian hierarchical Lasso modeling to objectively quantify the influence of stable personality traits on transient facial expressions. These expressions were further modeled using Bayesian hierarchical Lasso regression, incorporating personality traits assessed via the Big Five model. Results revealed significant associations between high agreeableness/neuroticism and reduced emotional expressions. Specifically, anger and disgust expressions were sup-pressed in these individuals. The results indicate that personality traits play a stronger role than momentary facial expressions in emotion judgment tasks. These findings offer novel insights for the software engineering of adaptive affective computing systems. This work provides a foundational framework for designing next-generation intelligent service systems capable of delivering more personalized and empathetic user interactions.


Prof. Witold Pedrycz

University of Alberta, Canada

Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, Granular Computing, and Machine Learning.
Professor Pedrycz serves as an Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley).

Title: Informed Machine Learning: Emerging Opportunities

Machine Learning (ML) and Artificial Intelligence (AI) have enjoyed a lot of interest and led to numerous success stories including those in areas of high criticality. With the passage of time, some limitations of the ML technology have become visible and raised concerns about the deployment of the ML constructs (including LLMs) and their exclusive reliance on data. Indeed, data are a lifeblood of design methodologies and drive current commonly encountered development practices. At the center of the ML methodology lies a default assumption that the data fully represent the problem to be solved (e.g., classification or prediction). We look at the problem and produce a solution through the lens of data; in many cases, this may lead to the data blinding effect. We advocate that a holistic knowledge-data development perspective is urgently needed.
An Informed ML (IML) has emerged as a new direction of research addressing these needs. In brief, IML is sought as a methodology in which data and knowledge are used in unison to design ML systems. From the design perspective encountered in the ML learning environment, data and knowledge are radically different. Data are numeric and precise. Knowledge is general and usually expressed at the higher level of abstraction (generality). Knowledge and data emerge at different levels of information granularity.
In this talk, we deliver a comprehensive taxonomy of main pursuits of IML and link them with the main ways the knowledge is represented. A historical perspective is offered by studying the symbolic and subsymbolic processing encountered in successive decades of AI.
The two general categories of physics-oriented and neuro-symbolic constructs associated with the ways in which knowledge and data are explored together. We elaborate on the design process being guided by a prudently augmented additive loss function whose corresponding parts minimize distances between the developed ML model and numeric target values and deliver adherence of the model to information granules reflecting available knowledge. A general taxonomy of neuro-symbolic systems involving learning-for-reasoning, reasoning-for-learning, reasoning-learning is discussed.

Dr. Muhammad Jawad Sajid

China University of Mining and Technology, China

Dr. Muhammad Jawad Sajid is an assistant professor at the School of Economics and Management, China University of Mining and Technology, holding a Ph.D. in Management Science and Engineering from the same institution. Dr. Sajid specializes in environmental management, with deep expertise in industrial ecology, sustainability, carbon emissions, carbon footprints, and carbon policy. He is also at the forefront of developing innovative technologies that combine renewable energy sources, including wind, solar, and hydropower.
Dr. Sajid has published extensively in respected journals indexed by SCIE, SSCI, EI, and ESCI. As the first inventor, he holds two invention patents, six utility model patents, and five design patents. He has also authored/edited books and chapters on relevant topics. Dr. Sajid is a regular contributor to international conferences and workshops, having served as co-chair for MITM2020: Modern Informatics and its Teaching Methods, session chair at the International Conference on Intelligent Transportation Engineering (ICITE 2023), and as a keynote and invited speaker at major events such as EEGT 2021, EES 2022, and IWEG 2022. Furthermore, Dr. Sajid has contributed as a technical committee member and peer reviewer for numerous academic conferences and journals.
A senior member of the Hong Kong Chemical, Biological & Environmental Engineering Society (HKCBEES), Dr. Sajid is recognized internationally for his/her research and leadership in the field of environmental management and sustainable technology.

Title: Building Knowledge and Organizational Capacity as the Predominant Barrier to Climate Technology Transfer: Evidence from Three Decades of Research

This presentation reviews more than three decades of research (1990–2023) on climate technology transfer. It is based on an analysis of 285 barriers identified in books, academic articles, policy reports, and expert discussions. The study groups these barriers into six main categories that affect how climate technologies move from developed to developing countries. The key finding is clear: problems related to knowledge and organizational capacity are the most frequently identified barrier. These include gaps in technical skills, weak institutions, limited access to information, and a lack of trained personnel. They emerge as the most frequently cited obstacle, with a frequency score of 108 out of 285. This surpasses even government and law barriers (69) and investment and finance constraints (46). Based on the speakers published findings, the speech argues that, compared to the intuitive barriers of finance and policy, human factors related to organizing and developing knowledge are supported by a larger consensus as key obstacles to technology transfer. Which shows, technology transfer is not simply about delivering equipment. It requires long-term learning, strong institutions, skilled professionals, and ongoing cooperation. Without these foundations, even well-funded projects often struggle to succeed. The goal of policymakers should be to change the way they think about technology transfer from a problem of getting the physical (hard) part of technology to the people to an opportunity to build people's skills (soft technology). The physical aspect of technology may accompany or follow soft technology development through any suitable means, including purchasing, attracting FDIs from developed countries, or, where permitted, copying (imitating).