Keynote Speakers

Prof. Nikola K Kasabov
Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK
Auckland University of Technology, New Zealand

Professor Nikola K Kasabov is a Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He has Doctor Honoris Causa from Obuda University, Budapest. He is the Founding Director of KEDRI and Professor Emeritus at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. He is also Visiting Professor at the Institute for Information and Communication Technologies of the Bulgarian Academy of Sciences and Dalian University, China. Kasabov is Director of Knowledgeengineering.ai and member of the advisory board of Conscium.com. He is Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS). Kasabov holds MSc in computer engineering and PhD in mathematics from TU Sofia. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, neuroinformatics, spiking neural networks. He has published more than 750 publications, highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia. Kasabov has received a number of awards, among them: INNS Ada Lovelace Meritorious Service Award; NN journal Best Paper Award; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Medal “Bacho Kiro” and Honorary Citizen of Pavlikeni, Bulgaria; Honorary Member of the Bulgarian-, the Greek- and the Scottish Societies for Computer Science. More information of Prof. Kasabov can be found on: https://academics.aut.ac.nz/nkasabov and on https://knowledgeengineering.ai.

Speech Title: Generative-, Predictive-, Agentic AI and AGI: All They Need is Brain-inspired Neural Networks

Abstract: Significant advances in AI, including generative-, predictive and agentic AI, have been achieved due to the use neural networks. Most recent advances are based on a class of neural networks – brain-inspired spiking neural networks (SNN). SNN and their neuromorphic hardware platforms, have proved its efficiency not only in their minimal power consumption and massive parallelism, but in adaptive and predictive modelling, due to their spike-based/event-based information processing. The talk presents how these techniques can be used now to build more efficient Generative, Predictive and Agentic AI. Generative AI, such as LLM, generate new information based on pretrained neural network models. The use of SNN makes them more efficient. Predictive AI predict events in a future time and SNN have been used due to their predictive coding feature. Agentic AI designs AI agents that are autonomous entities, able to evolve itself from data, make decisions, take actions, adapt to the environment, communicate with other agents. SNN are fit for this task too. The talk presents current methods, systems, their applications, along with current EU projects and future directions.

 

 

Prof. Kaoru Ota
AAIA Fellow, Fellow of EAJ
Tohoku University, Japan & Muroran Institute of Technology, Japan

Kaoru Ota received her B.S. and Ph.D. degrees from the University of Aizu, Japan, in 2006 and 2012, respectively, and her M.S. degree from Oklahoma State University, USA, in 2008. She is a Distinguished Professor at the Graduate School of Information Sciences, Tohoku University, Japan, and a Professor at the Center for Computer Science (CCS), Muroran Institute of Technology, Japan, where she served as the founding director. She has been recognized as a Highly Cited Researcher by Clarivate Analytics in 2019, 2021, and 2022, a Fellow of the Engineering Academy of Japan (EAJ) in 2022, and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) in 2025.

 

 

Prof. Masaki Aida
Tokyo Metropolitan University, Japan

Masaki Aida received the B.S. degree in Physics and the M.S. degree in Atomic Physics from St. Paul's University, Tokyo, Japan, in 1987 and 1989, respectively, and the Ph.D. degree in telecommunications engineering from the University of Tokyo, Japan, in 1999. He joined NTT Laboratories in 1989 and has been with Tokyo Metropolitan University since 2005, where he has been a Professor with the Graduate School of Systems Design since 2007. His research interests include communication traffic theory, decentralized control of computer communication networks, and analysis of online user dynamics. He received the Best Tutorial Paper Award and the Best Paper Award of IEICE Communications Society in 2013 and 2016, respectively, and the IEICE 100-Year Memorial Paper Award in 2017. He is a Fellow of IEICE, a Senior Member of IEEE, and a member of ACM and ORSJ.

Speech Title: Before the News Breaks: Detecting Significant Events from Collective Online Behavior

Abstract: Can we detect a significant event before the news breaks? This keynote addresses that question through a mathematical and empirical study of collective online behavior. Rather than mining the content of social media posts or reacting only after information becomes public, we focus on subtle but measurable precursors that emerge in aggregate user activity at an earlier stage. I first introduce a theoretical framework for online user dynamics based on locality and causality, which predicts that overheating phenomena—such as rapid collective attention shifts and online flaming—are preceded by low-frequency beat patterns. I then show how frequency-spectrum analysis of time-series data, including search trends, can verify this prediction and turn it into a practical indicator for early warning detection. Finally, I discuss extensions to the early detection of company-related news and investment simulation, and outline broader opportunities in trend forecasting, crime prevention, and information security. The central message is that hidden precursors in collective behavior can make significant events detectable before they are explicitly reported, enabling timely and explainable decision support.

 

 

Prof. Jiun-In Guo
National Yang Ming Chiao Tung University & Founder and CTO, eNeural Technologies, Inc.

Prof. Jiun-In Guo received the B.S. and Ph.D. degrees in Electronics Engineering from National Chiao Tung University (NCTU), Hsinchu, Taiwan, in 1989 and 1993, respectively. He is currently a Distinguished Professor of the Institute of Electronics, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan, and the Founder and CTO of his start-up, eNeural Technologies, Inc, founded in March 2022. His research interests include images, multimedia, and digital signal processing, VLSI algorithm/architecture design, digital SIP design, SOC design, and intelligent vision processing applications including ADAS/Self-driving vehicles.
Prof. Guo received the outstanding electrical engineering professor award from the Chinese Institute of Electrical Engineering in 2010, the outstanding engineering professor award from the Chinese Institute of Engineers in 2014, the outstanding research award of Minister of Science (MOST) in 2017, as well as the outstanding technology transferring award of MOST in 2018 and 2020 with the topic of deep learning ADAS systems. Prof. Guo was also selected as the Elsevier 1960-2020, 1960-2021, 1960-2022, 1960-2023 top 2% Scientist in Life-long Impact by Stanford University in consecutive four years.
Prof. Guo is the author of 273 technical papers on the research areas and has served as the PI/Co-PI of 117 research projects, 133 industrial projects, and 65 industrial technology transfer projects. Prof. Guo is also the inventor of 73 invention patents and the receiptent of 129 awards in the research areas. With all the cumulated research outcome, Prof. Guo starts up a company called eNeural Technologies, Inc. since March 2022, where eNeural Technologies Inc. is an embedded AI design service house in the area of automotive and AIOT applications to help customers to solve the pain points in developing quality light weight AI models on embedded computing platforms. For more information about eNeural Technologies Inc., please visit the official website below: https://www.eneural.ai/.

Speech Title: Building Next Generation Edge Vision System through Automatic AI Model Compression and Self-Learning

Abstract: This talk addresses the key factors in building the next generation edge vision system through a systematic design approach incorporating automatic AI model compression and self-learning methodology. To compress an AI model in a systematic way, what we proposed include the AI model pruning tool (P-Craft) and AI quantization tool (Q-Craft), that allow users to compress the AI models via user-defined setting to achieve the goal of fitting in the provided processing power via the selected AI SoC to achieve the accuracy of the target applications. Some examples for the AI model compression via the proposed P-Craft and Q-Craft will be discussed, which covers the applications of smart transportation, smart manufacturing, smart healthcare, etc. In addition to AI model compression, some important tools to assist the training of compressed AI models will also be introduced, including self-training tool (eSL-Craft) for AI model training and fine tuning and the corner case dataset generation tool (GenAI-Craft) to provide more diverse datasets used for AI model training in a click. With all these AI model compression and self-learning tools, users can quickly design the slim fit AI model inferenced on the target AI SoC for applications in an efficient way.