Prof. KWONG Sam Tak Wu

Lingnan University, Hong Kong, China

IEEE Fellow, US National Academy of Innovators Fellow
Hong Kong Academy of Engineering and Sciences Fellow

Biography: Professor KWONG Sam Tak Wu is the Chair Professor of Computational Intelligence, and concurrently as Associate Vice-President (Strategic Research) of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as one of the top 2% of the world’s most cited scientists, according to the Stanford University report. He was listed as one of the most highly cited scientists by Clarivate in 2022 and 2023. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-23. Professor Kwong has a prolific publication record with over 350 journal articles, and 160 conference papers with an h-index of 83 based on Google Scholar. He is currently the associate editor of many leading IEEE transaction journals. He is a fellow of the US National Academy of Innovators. and the Hong Kong Academy of Engineering and Sciences.

Speech Title: Creating a Better Future: Harnessing AI for Social and Environmental Responsibility

Abstract: In this talk, I will explore the potential of artificial intelligence (AI) to address some of the most pressing social and environmental challenges facing our world today. With its ability to analyze vast amounts of data, identify patterns, and make predictions, AI has the potential to revolutionize fields such as healthcare, education, and climate science.
However, as AI becomes more powerful and ubiquitous, it is also raising important ethical and social questions. How can we ensure that AI is used for the greater good, rather than contributing to inequality and injustice? How can we ensure that the benefits of AI are shared fairly across society, rather than concentrated among a small group of wealthy individuals and corporations?
In this talk, the speaker will delve into various questions related to AI applications and their positive impact on society and the environment. The talk will draw on examples of specific AI applications that are already making a difference. For instance, the underwater instance segmentation, which is the process of detecting and segmenting objects in underwater images. This technology has the potential to improve underwater exploration, marine conservation, and disaster response efforts.
Another example is image reconstruction based on compressive sensing. This technique allows for the reconstruction of high-quality images from a limited amount of data, which can be particularly useful in applications such as medical imaging or remote sensing. The third topic is the low night image enhancement, which is a technology that enhances images taken in low-light conditions. This can improve the accuracy and effectiveness of applications such as surveillance, transportation safety, and security.
By exploring these and other examples of AI applications, the talk aims to demonstrate the potential of AI to make a positive impact on society and the environment, and to inspire further innovation in
Ultimately, this talk will aim to inspire and empower attendees to think critically about the role of AI in shaping our future, and to explore ways in which they can harness this powerful technology to create a more just, equitable, and sustainable world.

Prof. Naoyuki Kubota

Tokyo Metropolitan University, Japan

Director of Community-centric Systems Research Center, Tokyo Metropolitan University, Japan
Representative Director of the Tokyo Biomarker Innovation Research Association, Japan

Biography: Naoyuki Kubota is currently a professor in the Department of Mechanical Systems Engineering, the Graduate School of Systems Design, and director of Community-centric Systems Research Center, Tokyo Metropolitan University, Japan. He is the representative director of the Tokyo Biomarker Innovation Research Association, Japan. He received a doctoral degree from Nagoya University, Japan, in 1997. He was a Visiting Professor at University of Portsmouth, UK, Seoul National University, and others. His current interests are in the areas of topological intelligence, coevolutionary computation, spiking neural networks, robot partners, and informationally structured space. He has published more than 600 peer-reviewed journal and conference papers in the above research areas. He received the Best Paper Award of IEEE IECON 1996, IEEE CIRA 1997, and so on. He was an associate editor of the IEEE Transactions on Fuzzy Systems from 1999 to 2010, Editorial Board Member of Advanced Robotics from 2004 to 2007, Editorial Board Member of Journal of Advanced Computational Intelligence and Intelligent Informatics since 2004, the IEEE CIS Intelligent Systems Applications Technical Committee, Robotics Task Force Chair from 2007 to 2014, Editor of ROBOMECH Journal since 2012, IEEE Systems, Man, and Cybernetics Society, Japan Chapter Chair from 2018 to 2021, IEEE Transactions on Affective Computing Steering Committee Member since 2019, and others.

Speech Title: Topological Intelligence and Coevolutionary Computation

Abstract: Recently, artificial intelligence for digital twin has been discussed in various research fields. We often need topological features and structures from given or measured big data, when we perform multiscale and multi-physics simulations on real-world phenomena in the cyber world based on digital twin. Therefore, we proposed the concepts of topological twin and topological intelligence. The goal of topological twin is to (1) extract topological structures implicitly hidden in the real world, (2) hidden reproduce them in the cyber world, and (3) simulate and analyze phenomena of the real world in the cyber world. Digital twin and Topological twin are used complementarily, not exclusively. While we need to deal with the physical dynamics at the microscopic level, we need to deal with spatiotemporal qualitative relationships between objects, people, culture, experience, and knowledge at the macroscopic level. Furthermore, we need the mesoscopic integration method that connects microscopic and macroscopic topological features and structures. Thus, the topological twin plays the essential role in extracting and connecting the structures hidden in the real world from the multiscopic point of view. Especially, the coevolution between multiscopic topological features is very important to deal with the learning and searching in topological twin. Therefore, topological intelligence is used for inference, learning, search, and prediction based on topological and graphical data. In this talk, I first introduce the concepts of topological twin and topological intelligence. Then, I explain various types of topological clustering methods and graph-based methods related with topological intelligence. Furthermore, I discuss the role of coevolutionary computation in topological intelligence. Next, I show several experimental results of topological intelligence for trailer living laboratory, robot partners, multi-legged robots, and mobility support robots. Finally, I discuss the applicability and future directions of topological intelligence.

Prof. Amir H. Gandomi

University of Technology Sydney, Australia

ARC DECRA Fellow, Highly Cited Researcher award (top 1% publications and 0.1% researchers)

Biography: Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University at Budapest as a Distingushed Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the Stevens Institute of Technology and a distinguished research fellow at BEACON center, Michigan State University. Prof. Gandomi has published 400+ journal papers and 14 books which collectively have been cited 50,000+ times (H-index = 100+). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six years. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof Amir H Gandomi is ranked as the 28th most impactful researcher in the AI and Image Processing subfield in 2022! He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are data analytics and global optimisation (big) in real-world problems in particular.

Speech Title: Evolutionary and Swarm Intelligence for Real-World Problems

Abstract: Artificial Intelligence has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. Evolutionary and swarm Intelligence (ESI) techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EI comes from biological systems or nature in general. The efficiency of ESI is due to its significant ability to imitate the best features of nature, which have evolved through natural selection over millions of years. The central theme of this presentation is ESI techniques and their application to complex geomechanical problems. On this basis, first, I will talk about an automated learning approach called genetic programming. Applied evolutionary learning will be presented, and then their new advances will be mentioned. Here, some of my studies on big data analytics and modelling using ESI and genetic programming, in particular, will be presented. Second, ESI will be presented, including key applications in the optimisation of complex and nonlinear systems. It will also explain how such algorithms have been adopted for geotechnical and mechanical engineering and how their advantages over the classical optimisation problems are used in action. Optimisation results of large-scale and many objective problems will be presented to show the applicability of ESI. Finally, heuristics that are adaptable to ESI will be explained and can significantly improve the optimisation results.

Invited Speakers

Prof. Andres Iglesias

University of Cantabria, Spain

Level-A (exceptional) accredited Full Professor, leader of the "Computer Graphics & Artificial Intelligence" research group

Biography: Andres Iglesias is a level-A (exceptional) accredited Full Professor of Computer Science and Artificial Intelligence at the University of Cantabria, Santander, Spain, where he leads the "Computer Graphics & Artificial Intelligence" research group, and was Head of Department (2008-2012), and Postgraduate Studies Coordinator (2005-2012). Since 2013, he has also been Invited/Guest Professor at Toho University, Funabashi, Japan. He is the current chair of the Technical Committee 5 - Information Technology Applications, Workgroup 5.10 - Computer Graphics and Virtual Worlds at IFIP (International Federation for Information Processing), the UN-recognized UNESCO-established international organization comprising more than 50 national societies and academies in the field of computer science. He was also Chair of the Steering Board of ICMS, the society responsible for the regular ICMS event, part of the ICM (International Congress of Mathematicians, the world's largest event in Mathematics).
His research is highly inter/multidisciplinary, having published journal papers in 33 different categories of Web of Science Journal Citation Reports, including most categories of Computer Science, Mathematics, Physics and Engineering. His publication profile includes more than 320 international scientific papers, 22 books (20 in English, 2 in Spanish; published by Elsevier, Springer, IEEE, and Thomson Publishers). He also holds 4 patents/IPR and 36 research projects (mostly public-funded), totalling 5.86 million Euros. He has also edited 15 special issues of journals, 13 of them JCR-indexed. He has been included in the Stanford-Elsevier Ranking of Authors' Career among the 2% most-cited researchers in the field of "Artificial Intelligence and Image Processing".
Prof. Iglesias has been chairman/organizer of 70 international conferences and workshops, including several top (CORE-A and CORE-B) conferences, such as ICCS, Cyberworlds or ICMS, Steering/Advisory committee member of 38 international conferences, program committee member of +300 international conferences, reviewer of +250 papers in JCR journals and +850 papers of international conferences. He is also associate editor and editorial board member of several international scientific journals.
He has been a project expert evaluator for the European Union (FP7, Horizon 2020, Horizon Europe) and for several public research agencies in USA (NSF), Germany (DFG), United Kingdom (UKRI), Canada (ORF), Spain (ANEP, ANECA), Cyprus (RPF), Iceland (IRF), The Netherlands (NWO), and others, for several calls granting more than 380 Million Euros in research funding.
During the last years, he has pioneered the worldwide research on the application of metaheuristic techniques to the problem of curve and surface reconstruction. His research has been applied to challenging problems in industrial design and manufacturing, swarm robotics, medical sciences (non-invasive medical imaging, melanoma detection from macroscopic/dermoscopic images), fractal imaging generation, dynamical systems, and other fields.

Speech Title: An Overview of Swarm Intelligence for Shape Reconstruction with Applications

Abstract: Shape reconstruction plays a crucial role in various domains, including computer graphics and animation, computer vision, and image processing. It involves the capture or recovery of the shape and appearance of real-world objects from diverse inputs, which can be geometric (such as a cloud of scanned data points), visual (a single image or multiple images from different viewpoints), or a combination of both. In all cases, this process is widely recognized as very challenging and computationally expensive.
A promising avenue for shape reconstruction consists of the application of powerful artificial intelligence (AI) methods, which have gained significant attention in recent years due to remarkable developments in the field of AI, including deep learning, generative AI, real-time object detection, biometric recognition, and more, which are reshaping the current landscape of today’s digital world in ways that were unimaginable just a few years ago.
One of the most remarkable AI-based approaches is swarm intelligence, a cutting-edge AI subfield with applications spanning academic and industrial fields. Swarm intelligence systems consist of simple agents that interact locally with each other or their environment, exhibiting basic behavioral patterns and operating autonomously in a decentralized and self-organized manner. Despite the simplicity of individual agents, their local interactions give rise to a collective intelligence, enabling the swarm to perform sophisticated tasks and develop complex behavioral patterns unattainable to the individual agents.
This invited talk will explore some of the most recent advances regarding the application of swarm intelligence methods to shape reconstruction. The discussion will encompass diverse academic, professional, and industrial domains, including computer-assisted design and manufacturing, computer animation, computer vision, medical imaging, and swarm robotics. Several real-world examples will be used to illustrate the enormous potential of swarm intelligence to solve challenging problems in various academic and industrial fields.

Dr. Shahin Jalili

Imperial College London, UK

Departmental Fellow at Imperial College London

Biography: Shahin is a Departmental Fellow at Imperial College London, specialising in the decarbonisation of key sectors, including buildings, transportation, offshore wind energy, and oil and gas. He has several years of academic research experience at the University of Exeter, the University of Aberdeen, and the National Decommissioning Centre, where he collaborated with international energy companies and a diverse range of stakeholders. He is also a Chartered Engineer with the Institution of Mechanical Engineers (IMechE).

His research focuses on addressing challenges at the intersection of engineering, computer science, and mathematics, with particular emphasis on optimisation methods and numerical algorithms. He has developed a variety of numerical algorithms to address optimisation problems in multiple areas of engineering, ranging from the optimal design of skeletal and composite structures to the performance optimisation of traffic transportation networks. He has also worked on the mathematical aspects of finite element analysis, formulating new approaches based on polynomial-type extrapolation methods to enhance computational efficiency in structural optimisation. Shahin’s work also involves developing decision-support tools to facilitate the transition to net-zero. He has worked on creating multi-criteria decision-making tools for offshore energy decarbonisation projects that assess the economic, environmental, safety, technical, and societal impacts. These tools are designed to provide stakeholders in the North Sea region with comprehensive insights, supporting sustainable development and informed decision-making.

Speech Title: Structural Optimisation via Hyper-Cultural Algorithm

Abstract: Cultural Algorithms (CAs) are meta-heuristics inspired by the bio-cultural evolution theory, which have some features that make them unique comparing to the other evolutionary algorithms. Over the past years, they have been effectively employed for structural optimisation problems. However, the performance of CAs can be significantly affected by the influence functions with different types of constraints and objective functions. This study proposes a Hyper-Cultural Algorithm (HCA), which employs a High-Level Heuristic (HLH), called Choice Function (CF), to decide what type of influence function needs to be employed by a given individual within the population space during the optimisation process. To validate the efficiency of the HCA, optimum design of a 942-bar steel tower structure is performed. The comparison results reveal the capabilities of HCA in providing higher-quality designs for large-scale structures.

Dr. Zichao Li

Canoakbit Alliance / University of Waterloo, Canada

Biography: My name is Zichao Li, and I am currently a lecturer and researcher at University of Waterloo. I have a PhD in engineering. I am conducting research on machine learning optimization algorithms. I leverage on traditional optimization techniques used in transportation model to improve deep learning's knowledge graph structures. The main application area of my research is in financial market fraud and sentiment detection. My research interests are Pattern recognition for medical engineering; Big data and deep learning; Graph Convolutional Network; Neural Networks; Optimization Algorithms for big data; Reinforced Learning and Adversarial Learning; Self Supervised and Unsupervised Learning; Bayesian Optimization; RNN, LSTM, GNN; Knowlege-Graph Embedding; Explainable AI; Distributed Statistical Model.

Speech Title: Cryptocurrency Forecasting Using Metaheuristics, Swarm Intelligence, and Alternative Data: A Hybrid Approach

Abstract: Cryptocurrencies are highly volatile assets influenced by a wide range of factors, including market sentiment, blockchain activity, and macroeconomic trends. Traditional forecasting models often struggle to capture these dynamics due to their complexity and reliance on unstructured data. This presentation explores a hybrid framework that integrates Metaheuristic Algorithms , Swarm Intelligence , and alternative data sources (e.g., Google Trends, Twitter sentiment, Reddit sentiment, and blockchain metrics) to predict cryptocurrency price movements. The proposed approach leverages the optimization capabilities of metaheuristics (e.g., Genetic Algorithms, Particle Swarm Optimization) and the collective intelligence principles of swarm algorithms to enhance forecasting accuracy. We demonstrate the effectiveness of this framework using real-world datasets and discuss its potential applications in algorithmic trading, risk management, and portfolio optimization.