Fugee Tsung
Professor Fugee Tsung is a globally recognized expert in industrial analytics and quality engineering, listed among the top 2% of most influential scientists worldwide by Stanford-Elsevier Mendeley Data 2023. As a Chair Professor at HKUST and HKUST(GZ), he directs the Industrial and Intelligence Institute (Triple-I Institute) and the Quality and Data Analytics Lab (QLab). He has held prominent positions such as Editor-in-Chief for the Journal of Quality Technology (JQT), Head of the Department of Industrial Engineering and Decision Analytics, and founding acting Dean of the Information Hub at HKUST(GZ). A fellow of esteemed organizations like ASA, ASQ, IISE, IAQ, and HKIE, Prof. Tsung has an extensive publication record and has mentored many successful doctoral students. He holds a Ph.D. and an MSc from the University of Michigan, Ann Arbor.
Keynote Title
“Harnessing Industrial Informatics and Intelligence in Service Science: A Pathway to Future Innovations”
Keynote Abstract
This keynote presentation delves into the transformative role of industrial informatics and intelligence in driving digital transformation across service industries within the framework of Industry 4.0. It highlights how the integration of artificial intelligence, particularly through AI-generated content (AIGC) and large language models (LLMs), is reshaping interactions between technology and human expertise, emphasizing the cultivation of innovation as a critical skill. Insights will be drawn from recent advancements at the Industrial Informatics and Intelligence Institute (Triple-I Institute) and the Quality and Data Analytics Lab. Additionally, the session will feature the HKUST 2.0 initiative, illustrating the integration of technology, arts, and education in innovating service science. The discussion aims to spark dialogue on leveraging interdisciplinary approaches to enhance service sectors, showcasing technology’s transformative potential in education and industry.
Qiu Guanghua
Dr. Qiu is a worldwide pioneer leading the development of Service Science (an interdisciplinary field of AI, Data Analytics, Big Data, Management Science, Social Science, and Computing Science). Dr. Qiu has actively promoted service science education and research internationally, aimed at developing the needed knowledge and skills required in today and the future’s service-led global economy. In addition to initiating international conferences in the area of Service Science, Dr. Qiu worked with many international scholars to have founded the Service Science Section of INFORMS (in 2006) and the Logistics and Services Technical Committee in the IEEE Intelligent Transportation Systems Society (in 2005). Service Science, a fully refereed journal, was launched in 2008 and became an official INFORMS journal in 2011 under his leadership and vision. He has been working diligently with many other pioneers in this emerging research, education, and application field to make Service Science an INFORMS flagship journal, facilitating the development of Service Science to better serve academics and practitioners in this field worldwide. Dr. Qiu served as the editor-in-chief of INFORMS Service Science. He was an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and an associate editor of IEEE Transactions on Industrial Informatics. Dr. Qiu is the Editor-in-chief of Digital Transformation and Society published Emerald Publishing and SpringerBriefs in Service Science by Springer. He has had more than 180 publications, including 3 books.
Keynote Title
“Feeding Right Data to Large Language Models”
Keynote Abstract
The quick advances and powerful capabilities of large language model (LLM) have recently stimulated the emergence of a lot of LLM applications worldwide. Due to the flocking-in effects of capitalism or marketing propaganda, many of those LLM applications were developed and deployed for the purpose of entertaining or show-off. In the field of machine learning, “garbage in, garbage out” is a well-known norm. Because of limited or sometime bad data, AI policymakers and educators are deeply concerned with the negativity of the outcomes derived from LLM applications, including but not limited to serious nonsense, disinformation, bias, and ethical implications. This talk presents an effective and efficient approach by focusing on collecting massive trusted data across networked platforms to train an LLM and aggregating quality data to finetune the LLM, aimed at deploying problem-solving LLM applications in the field, in particular in the field, such as healthcare and manufacturing, that current, accurate, and precise knowledge is the key to all stakeholders. The explored framework for developing LLM applications can help avoid the potential of generating a serious nonsense as an answer to an end user’s question, ensuring that developed LLM applications are of high-quality and readily acceptable in practice.
Two essential data-oriented technologies will be explored. First, blockchains enabling distributed data will be applied for collecting massive trusted data across distributed and networked platforms. Secondly, graph-based knowledge bases aggregating quality data in a logic manner will be utilized to generate the input to finetune LLMs, resulting in high-quality and readily acceptable domain-specific LLM applications. As an example, an LLM application in an automated automotive body welding line will be presented.
Janny Leung
Janny Leung is a Professor at the University of Macau affiliated with the State Key Lab of Internet of Things for Smart City and the Faculty of Business Administration. She holds an S.B. degree in Applied Mathematics from Harvard University, an M.A. in Mathematics from Oxford University and a Ph.D. in Operations Research from the Massachusetts Institute of Technology. Her main research interests are combinatorial optimization and transportation logistics. Her research has been well supported by the National Science Foundation of USA and the Hong Kong Research Grants Council. She is a Fellow of the Chartered Institute of Logistics and Transport, a Fellow of the Hong Kong Institution of Engineers and a Fellow of The Institute for Operations Research and the Management Sciences (INFORMS). She is currently the President of the International Federation of Operational Research Societies (IFORS).
Keynote Title
”Public Transport for Smart Cities”
Keynote Abstract
The idea of a smart city is one that utilizes IoT technologies and data analytics to optimize the efficiency of city operations and services, so as to provide a high quality of life for its citizens. Due to reduced public funding, many public transportation systems are already facing challenges to maintain their services. For a smart city, the goal of public transportation is not simply the movement of people, but to provide mobility for living. In order to provide sufficient coverage/frequency, an integrated co-ordinated multi-modal public transportation system is needed, leading to substantial increase in operational complexity. Environmental concerns and the recent pandemic may also have changed work and commuting patterns in the future. For smart cities, public transport must offer ubiquitous access, real-time response to demand, convenience and quality service, and energy-efficient operations. This talk will discuss the challenges in network design, operations planning, scheduling and management of smart public transport systems.
Lu Xiong
Lu Xiong is a Professor at the School of Automotive Studies at Tongji University and the recipient of the National Science Fund for Distinguished Young Scholars. He currently serves as the Associate Dean of the School of Automotive Studies at Tongji University, Deputy Director of the National Engineering Research Center for New Energy Vehicles and Power Systems, and Director of the Shanghai Electric Vehicle Engineering Technology Center.
He has long been engaged in research related to autonomous driving, automotive chassis control, and dynamic control of distributed drive electric vehicles. He has published over 300 papers, holds more than 100 invention patents, has authored three monographs, co-authored two English books, and led the development of three national standards. He has received the First Prize for Technological Invention from the China Automotive Industry, the First Prize for Scientific and Technological Progress from Shanghai, and has been involved in several provincial and ministerial scientific and technological awards. He has been recognized as a Shanghai Technical Leader and has won the China Industry-University-Research Cooperation Innovation Award.
He is the co-chair of the SAE International Connected and Automated Vehicle Technology Committee, chairman of the Intelligent Transportation Branch of the China Society of Automotive Engineers (SAE-China), vice-chairman of the Intelligent Chassis Branch of SAE-China, and a member of the Vehicle Control and Intelligence Committee of the Chinese Association of Automation.
Keynote Title
”New Generation Vehicle Chassis: Key Technologies, Innovative Practices, Development Outlook”
Keynote Abstract
In the context of the major trends towards electrification and intelligence in automobiles, the chassis system, which bears the core functions of vehicle driving, is undergoing a profound transformation from traditional to x-by-wire chassis. The x-by-wire chassis, which uses electric power to operate and controls braking, driving, steering, and other functions through electrical signals, has great potential for improving various aspects of automotive design, dynamics control, and intelligent driving. It has become a popular topic of research both domestically and internationally. This talk will conduct a thorough analysis of the global development status of the x-by-wire chassis, systematically organizing the latest advancements and key technologies in its main subsystems, structures, and control areas. Additionally, it will share the innovative work and practical applications of the presenter and the team in the field of x-by-wire chassis design and control. Finally, we will forecast future trends in x-by-wire chassis technology and offer suggestions for its advancement based on the current shortcomings and challenges in China’s x-by-wire chassis technology and industry.