Speakers 2024

Keynote Speaker Ⅰ

Prof. Martin Nowak

Harvard University, USA

Bio: Martin Nowak was born in Vienna in 1965. He went to the Albertus Magnus School. He studied Biochemistry and Mathematics at the University of Vienna. He worked on his diploma thesis with Peter Schuster and on his PhD thesis with Karl Sigmund. He graduated sub auspiciis praesidentis rei publicae. Martin wrote more that 500 papers and 4 books. He is one of the most cited researchers in the areas of mathematical biology, theoretical biology and evolutionary biology. He helped to create the fields of virus dynamics, mathematical oncology, evolutionary graph theory, adaptive dynamics and indirect reciprocity. He proposed that cooperation is the third fundamental principle of evolution beside mutation and selection.

Speech Title: Cooperation

Keynote Speaker Ⅱ

Prof. Yun Li

IEEE Fellow

Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, China

Bio: Professor Yun Li FIEEE obtained his PhD from University of Strathclyde, Glasgow, UK. He taught and researched into “AI for Engineering” at University of Glasgow for 28 years, where he was recognized by the University library as the second Top Author and served as the Founding Director of University of Glasgow Singapore and Founding Director of Joint Education with University of Electronic Science and Technology of China (UESTC).  Since mid-2021, he has been Director of Industrial Artificial Intelligence Centre at Shenzhen Institute for Advanced Study, UESTC.  Prof Li has published over 300 papers and books, one of which with the assistance of AI resolved a PID control issue puzzling practising engineers for over 50 years and has been the most popular paper in IEEE Transactions on Control Systems Technology almost every month still today since its publication in 2005.  He holds 30 patents in China, Europe, United States and Japan.  He has been funded over 30 million pounds to lead or co-lead 40 research projects in the UK, EU, Singapore and China.  Currently, he leads two National Natural Science Foundation of China major projects and one National Key R&D Program of China project concerning the third-generation and compute-in-memory AI.  

Speech Title: Grey-Box State-Space Model for the Large World Model of Artificial Intelligence

Abstract: The explosive growth in data volume and computational power has enabled the successful application of artificial intelligence (AI) to engineering science ("AI for Engineering"), potentially elevating industrial innovation beyond manual capabilities in exploration, imagination, and creativity. This talk introduces, in conjunction with large language models (LLMs) based on the Artificial Neural Network (ANN) and the Kolmogorov-Arnold Network (KAN), the next generation of artificial intelligence that is explainable (XAI), driven by both data and knowledge.  In particular, a grey-box state-space model (SSM) for the “large world model (LWM)” will be discussed for the third-generation AI with explainability, modularity and extendibility.  I will then illustrate its role in the transformation of "Computer-Aided Design" (CAD) in the third paradigm of science to "Computer-Automated Design" (CAutoD) in the fourth, to break through limitations of human designers, whereby enlightening original creativity, enhancing design performance, and shortening development gearing.  Finally, the talk will briefly cover pilot applications to dynamic system modelling, electronic design automation, and engineering design.

Keynote Speaker Ⅲ

Prof. Julio Gonzalo

Universidad Nacional de Educación a Distancia, Spain

Bio: Julio Gonzalo is director of the UNED Research Center in Natural Language Processing (NLP) and Information Retrieval (IR) in Madrid. Along his career he has worked on topics such as online reputation monitoring, toxicity and misinformation in Social Media, interactive cross-language search, computational creativity and semantic similarity. He has also worked extensively in the design and assessment of evaluation metrics for a wide range of Artificial Intelligence problems, which led to a Google Faculty Research Award (together with Enrique Amigó and Stefano Mizzaro). He has recently been general co-chair of ACM SIGIR 2022 and of IberLEF 2019-2022, the annual evaluation campaign for NLP systems in Spanish and other Iberian languages. He is currently leading ODESIA (odesia.es), a Spanish government initiative to measure the state of the art of language technologies in Spanish.

Speech Title: Experiments and Thoughts on Large Language Models and Autonomous Fiction Writing

Abstract: One of the most remarkable aspects of Large Language Models is their ease to writing creative texts. Under specific conditions, they have been shown to match and even improve average human writing skills. But is their writing truly creative, or just a repetition of the clichés they have been pre-trained with? Do they have a distinctive creative writing style? What is the role of (human) prompting in the creation process? 

In the talk, I will discuss the creative writing potential of LLMs and their intrinsic limitations, paying special attention to the experiments carried out at UNED. These include a contest between GPT-4 and one of the best contemporary novelists in Spanish, Patricio Pron. The contest is inspired by past AI duels (such as DeepBlue vs Kasparov and AlphaGo vs Lee Sidol), and was designed to test whether LLMs can already challenge a top (rather than an average) fiction writer. 

Keynote Speaker Ⅳ

Prof. Jie Qi

Donghua University, China

Bio: Jie Qi is a Professor in the College of Information Science and Technology at Donghua University, China. She has been a visiting researcher at the Cymer Center for Control Systems and Dynamics at the University of California, San Diego, from March 2013 to February 2014 and again from June to September 2015, as well as an academic visitor at the Mathematical Institute, University of Oxford, from September 2023 to September 2024. Professor Qi currently serves as an Associate Editor for the journal of Systems & Control Letters. She is also a member of the Teaching Committee of the Chinese Association of Automation (CAA) and an executive council member of the Shanghai Association of Automation. Her achievements have been recognized with several honors, including the Baosteel Excellent Teacher Award and the Shanghai Young Talents Award. Her research focuses on control and estimation of distributed parameter systems, control of delayed systems, and their applications in multi-agent systems and chemical production processes.

Speech Title: Large Scale Multi-agent Control via PDE Approach

Abstract: The control of multi-agent systems (MASs) at large scales has attracted significant attention. Partial Differential Equations (PDEs) have demonstrated their strength in modeling large-scale MASs, capturing the inter-agent interactions through compact macroscopic equations. Using the Lagrangian continuous description, individual agents are mapped onto a continuous spatial domain, where interactions with neighboring agents are modeled via the Laplacian operator, forming a PDE-based representation of collective MAS dynamics. We apply the PDE backstepping method to design controllers for achieving leader-driven, distributed control. This presentation further explores our recent work in MAS control across diverse topologies and in varying dimensional space, and then outlines potential directions for future research.