Keynote Speaker

Prof. Marios Polycarpou, KIOS CoE Director【IEEE Fellow, IFAC Fellow】

University of Cyprus, Cyprus

Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Founder of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He is also a Member of the Cyprus Academy of Sciences, Letters, and Arts, an Honorary Professor of Imperial College London, and a Member of Academia Europaea (The Academy of Europe).  He received the B.A degree in Computer Science and the B.Sc. in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and learning control systems, fault diagnosis, machine learning, and critical infrastructure systems. Prof. Polycarpou is the recipient of the 2023 IEEE Frank Rosenblatt Technical Field Award and the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC. He served as the President of the IEEE Computational Intelligence Society (2012-2013), as the President of the European Control Association (2017-2019), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). Prof. Polycarpou currently serves on the Editorial Boards of the Proceedings of the IEEE and the Annual Reviews in Control. His research work has been funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant, the ERC Synergy Grant and the EU-Widening Teaming program. 

Title: Enhancing Resilience of Cyber-Physical Systems 

Abstract: The development of cyber-physical systems with multiple sensor/actuator components and feedback loops has given rise to advanced automation applications, including energy and power, intelligent transportation, water systems, manufacturing, etc. Traditionally, feedback control has focused on enhancing the tracking and robustness performance of the closed-loop system; however, as cyber-physical systems become more complex and interconnected and more interdependent, there is a need to refocus our attention not only on performance but also on the resilience of cyber-physical systems. In situations of unexpected events and faults, artificial intelligence and machine learning can play a key role in improving the fault tolerance of cyber-physical systems and preventing serious degradation or a catastrophic system failure. The goal of this presentation is to provide insight into the design and analysis of intelligent monitoring methods for cyber-physical systems, which will ultimately lead to more resilient societies.

Prof. Cesare Alippi【IEEE Fellow】

Politecnico di Milano, Italy

CESARE ALIPPI is Professor with the Università della Svizzera italiana (Switzerland) and Professor with the Politecnico di Milano (Italy); he is visiting Professor at the Guandong University of Technology (China) and Consultant Professor at the Northwestern Polytechnic of Xi’An (China). He has been a visiting researcher/professor at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN), U.Kobe (Japan). Alippi is an IEEE Fellow, ELLIS Fellow, INNS Fellow and AAIA Fellow, Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society (CIS), Past Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, Associate Editor of Proceedings of IEEE and other journals, Past Associate editor of the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, IEEE-Transactions on Neural Networks, IEEE-Transactions on Emerging Topics in Computational Intelligence and member and chair of many IEEE committees.

In 2024 he received the IEEE CIS Enrique Ruspini Meritorious Service Award, the 2018 IEEE CIS Outstanding Computational Intelligence Magazine paper award, the 2016 Gabor Award from the International Neural Networks Society and the Outstanding Transactions on Neural Networks and Learning Systems Paper Award from the IEEE Computational Intelligence Society; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses graph-based learning, adaptation and learning in non-stationary environments and Intelligence for embedded, cyber-physical systems and IoT. For the graph-based learning research please refer to http://gmlg.ch. He holds 8 patents, has published one monograph book (translated in Chinese), 7 edited books and about 250 papers in international journals and conference proceedings.

Title: Graph Deep Learning for Irregular Spatiotemporal Data 

Abstract: Irregular spatiotemporal data consist of observations collected asynchronously across different times and spatial locations. Unlike regular data, their inherent irregularity makes traditional methods designed for discrete-time sequences and Euclidean spaces partly ineffective. In this talk, we explore how graph deep learning leverages the existence of relational dependencies to tackle both interpolation (imputation) and extrapolation challenges. We will cover techniques for reconstructing missing data from a limited set of observations by enforcing spatiotemporal consistency, as well as forecasting methods for predicting future values from sparse inputs.

Prof. Tingwen Huang【IEEE Fellow, AAIA Fellow】

Shenzhen University of Advanced Technology, China

Tingwen Huang is a Professor at Texas A&M University at Qatar. He received his B.S. degree from Southwest University, China, 1990, his M.S. degree from Sichuan University, China, 1993, and his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then he joined Texas A&M University at Qatar as an Assistant Professor in August 2003, then he was promoted to Professor in 2013. Dr. Huang’s research areas include neural networks, chaotic dynamical systems, complex networks, optimization and control, smart grid. He a Fellow of IAPR, IEEE and TWAS (The World Academy of Sciences) and Member of The European Academy of Sciences and Arts.

Title: AI-Driven Motion Imitation in Dinosaur Models via Neural and Modal Analysis 

Abstract: In this talk, we present an AI-based framework for motion imitation between distinct 3D dinosaur models using modal analysis and neural networks. By decomposing complex animations into modal coordinates via eigenmode analysis of finite element models, we obtain interpretable and compact motion representations. Specifically, we extract modal coefficients over time from a reference dinosaur (e.g., a T-Rex) and aim to transfer the motion to a target dinosaur (e.g., a Brachiosaurus) with different morphology. To learn this cross-modal mapping, we construct a synthetic dataset of modal coefficient sequences and employ Long Short-Term Memory (LSTM) networks to predict the target dinosaur’s modal dynamics from those of the source. We further explore the integration of Physics-Informed Neural Networks (PINNs) to enforce structural consistency during inference. Our experiments demonstrate that the LSTM can successfully learn to approximate complex motion behaviors across morphologically distinct models, and the addition of physics constraints further improves stability and realism. This work bridges physical modeling and AI, offering a promising methodology for animating biomechanical systems with minimal manual rigging or retargeting.

Prof. Zhongsheng Hou【IEEE Fellow, CAA Fellow】

Qingdao University, China

Zhongsheng Hou received his Ph. D. degree from Northeastern University in 1994, postdoc from Harbin Institute of Technology in 1997, and visiting scholar from Yale University in 2002-2003. He was formerly the director and second-level professor of the Department of Automatic Control of Beijing Jiaotong University, and was selected as a "Leading talent" of the Outstanding 100 People Program of Beijing Jiaotong University. He is currently the chief professor of Qingdao University and the Dean of the Institute of Systems Science.

IEEE Fellow; Chinese Association of Automation (CAA Fellow); Member, IFAC Technical Committee "Adaptive and Learning Systems"; Member, IFAC Technical Committee "Transportation Systems". Founding Director of the "Data-Driven Control, Learning and Optimization" Professional Committee of the Chinese Society of Automation; He founded the IEEE Data Driven Control and Learning Systems Conference and served as the general Chairman of the conference. Former or current editorial board member of "Acta Automatica Sinica", "Control Theory and Applications", "Control and Decision", "Systems Science and Mathematics"; He was a guest editor of the IEEE Neuronal Networks and Learning Systems Journal on "Data-Based Control, Decision, Scheduling and Fault Diagnosis". Guest editor of the IEEE Industrial Electronics Conference special issue "Data-Driven Control and Learning Systems".

Title: Dynamic Linearization Data Model and Its Prospects 

Abstract: This talk includes four parts. First parts will focus on the State Space Models (SSM) and the SSM based Control Theory (SBC) with theirs advantages and drawbacks. The second is going to talk the Dynamic Linearization Data Model (DLDM) and DLDM based MFAC Control Theory (DMFAC) with brief comparisons between the SBC and DMFAC in theory and application, especially in the aspects of fundamental theoretical issues. The third part will present the future possible framework on data driven control. The last is the Conclusion.