2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)
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Keynote Speakers

Prof. Ruguo Fan

Wuhan University, China


Speech Title: Public Management Research: Reform based on Big Data and Complexity Science Theory


Abstract: Public management enters the data eraBig Data deeply changes social structure、social style、thinking mode behavior way. It has been applied to public management research and practice. It becomes a new turn on public management research using Big Data thinking. As a complex system, Public management issues are becoming increasingly complex, public management need multidisciplinary integration research. Complex public management requires the adoption of social computing thinkingPublic management has get a complete program from scientific theory to science and technology by using complex theory and social computation method.


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Prof. Jerry Chun-Wei Lin

Department of Computer science, Electrical engineering and Mathematical sciences

Western Norway University of Applied Sciences, Norway

IET Fellow, IEEE Senior Member, ACM Distinguished Member


Brief Introduction: Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published 500+ research articles in refereed journals (with 50+ IEE/ACM Journals, e.g., IEEE TKDE, IEEE TFS, IEEE TNNLS, IEEE TCYB, IEEE TII, IEEE TITS, IEEE TNSE, IEEE TETCI, IEEE SysJ, IEEE SensJ, IEEE IOTJ, ACM TKDD, ACM TDS, ACM TMIS, ACM TOIT, ACM TIST, ACM TOS, ACM TALLIP) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 16 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining, soft computing, optimization, artificial intelligence/machine learning, and privacy preserving and security technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Associate Editor of 11 SCI journals including IEEE TNNLS, IEEE TCYB, Information Sciences, etc, and Guest Editor for several IEEE/ACM journals such as IEEE TFS, IEEE TITS, IEEE TII, IEEE SysJ, ACM TMIS, ACM TALLIP, ACM TOIT and ACM JDIQ. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, and 2020 by Scopus/Elsevier. He is the Fellow of IET (FIET), ACM Distinguished Member and IEEE Senior Member.


Speech Title: Intelligent Utility-Driven Data Analysis


Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns about customer buying behavior that can then be used for decision making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which has been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits by using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed.



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Prof. Hongbo Li

 School of Management, Shanghai University


Brief Introduction: Hongbo Li is currently Associate Professor of Information Systems and Management Science in the School of Management at Shanghai University, Shanghai, China. He obtained his PhD degree in Management Science in July 2014 from Beihang University, Beijing, China. He was a visiting PhD student at Research Center for Operations Management, KU Leuven, Belgium from 2012 to 2013. His research interests include artificial intelligence and project scheduling. He has published in a variety of refereed journals, such as Journal of Scheduling, International Journal of Production Research, and Decision Support Systems.


Speech Title:  Uncertain Public R&D Project Portfolio Selection Considering Sectoral Balancing and Project Failure


Abstract:  In order to promote scientific and technological innovation and sustainable development, public funding agencies select and fund a large number of R&D projects every year. To guarantee the performance of the resulting project portfolio and the government’s investment benefits, the decision maker needs to select appropriate projects and determine a reasonable funding amount for each selected project. In the process of project selection, it is necessary to consider the balance of funding allocated to different scientific sectors as well as the failure probability of the projects in future execution, so that the expected performance of the project portfolio is maximized as much as possible. In view of this, we propose and study the uncertain public R&D project portfolio selection problem considering sectoral balancing and project failure. We formulate a stochastic programming model for the problem to support the portfolio decisions of the funding agencies. We also transform the model into an equivalent deterministic second-order cone programming model that can be directly solved by exact solvers. We generate datasets reflecting different scenarios through simulation and perform computational experiments to validate our model. The impacts of various factors (i.e., the number of project proposals, project failure probability, the upper limit of the budget allocated to each project, and the decision maker’s tolerance for project failure) on the project portfolio performance are analyzed.



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Siau Keng Leng

Department of Information Systems

City University of Hong Kong, China


Brief Introduction: Professor Siau is the Head of the Department of Information Systems and Chair Professor of Information Systems at the City University of Hong Kong (June 2021-present). He is also a Chair Professor (Affiliate) of the School of Data Science, City University of Hong Kong. Professor Siau received his Ph.D. in Business Administration from the University of British Columbia (Canada) in 1996. His M.S. and B.S. (honors) degrees are in Computer and Information Sciences from the National University of Singapore. His research areas are: Digital Transformation and Digital Society, Business Analytics and Data Science, Technological Innovation and Entrepreneurship, Smart Health and FinTech, AI, Robotics and Machine Learning: Future of Work and Future of Humanity Human-Centered AI, Human-AI Interaction, Web3, Metaverse


Speech Title: Artificial Intelligence, Chat GPT, Metaverse: Intelligent Society, Future of Work, and Future of Humanity.


Abstract: The rapid advancement of technologies, such as artificial intelligence, machine learning, Chat GPT, and metaverse, is transforming our society into an intelligent society. The way we work, the way we play, the way we socialize, and the way we live our lives are transformed by these technologies. Digital transformation, however, affects the future of work and the future of humanity. This talk will look at these technological developments and discuss their impact on our future.