Keynote & Invited Talk

Abstract

    Artificial Intelligence (AI) has become a very familiar household term in our rapidly changing world, yet its intricacies are often shrouded in mystery and technical jargon. In recent years, the mainstream media has paid full attention to this subject; it is often considered a magical box capable of solving any problem. To integrate AI into a Community-centric System, it is expected that robots or intelligent systems are integrated seamlessly into human environments, enhance the quality of life, and address the specific needs of communities. From understanding the basics of Machine Learning algorithms to exploring real-world AI applications, this talk will serve as a primer for individuals interested in grasping the transformative potential of robotics in the age of generative AI.

Biography

    Ahmad Lotfi is currently a Professor of Computational Intelligence and Head of the Department of Computer Science at Nottingham Trent University, Nottingham, where he is also leading the Computational Intelligence and Applications (CIA) research group. Areas of his research interest include computational intelligence, ambient intelligence, assistive robotics, and smart homes. His research has been recognised internationally for significant contributions to the application of computational intelligence techniques in control systems and intelligent environments. He has supervised many research fellows and over 20 PhD research students to successful completion.  He has worked in collaboration with many healthcare commercial organisations and end-users. He has authored and co-authored over 250 scientific papers in the area of computational intelligence, anomaly detection and machine learning in highly prestigious journals and international conferences. He has been invited as an Expert Evaluator and Panel Member for many EU Framework Research Programmes. More details are available from: http://www.lotfi.uk

Abstract

    In today’s rapidly evolving technological landscape, collaboration between academia and industry is more critical than ever. These partnerships not only drive innovation but also fuel economic growth by transforming cutting-edge research into practical, real-world applications. This keynote will explore strategies to optimize these collaborations for mutual benefit, ensuring that universities and industries work in synergy rather than in isolation.

    By fostering a symbiotic relationship, universities can leverage industry expertise, resources, and challenges to enhance both research and education. In return, industries gain access to state-of-the-art developments and a highly skilled talent pipeline that is well-prepared for emerging technological demands.

    An example of such an ecosystem is the Department of Artificial Intelligence at Eötvös Loránd University, Hungary. This talk will highlight how the University 3.0 model seamlessly integrates academic research with industry-driven innovation. We will examine the pivotal role of doctoral students as bridges between fundamental research and real-world applications, ensuring that knowledge transfer occurs effectively.

    To illustrate these principles in action, we will present several case studies that demonstrate the power of AI-driven collaboration, including:

• Automated unpacking robots for industrial logistics, streamlining supply chain processes.

• Evolutionary algorithm-based portfolio optimization for PCB production, improving efficiency and cost-effectiveness.

• Anomaly detection in serial production and product development, leveraging AI to enhance quality control.

• Semantic segmentation for welding line detection, ensuring precision in automated manufacturing.

    Throughout the discussion, we will emphasize the role of advanced AI techniques, such as neural architecture search and spiking neural networks, in solving complex industrial challenges. By bridging the gap between theoretical AI research and practical implementation, these collaborations pave the way for groundbreaking advancements with tangible impact.

Biography

    Janos Botzheim’s degrees earned: Budapest University of Technology and Economics: M.Sc. in Technical Informatics (2001), Ph.D. in Computer Engineering (2008). Visiting positions: long-term and short-term visits and scholarships as Ph.D. student at the following Universities: Czech Technical University, Prague, Czech Republic (2002, 1 month); Johannes Kepler University, Linz, Austria (4 months in 2003, 2 months in 2004, 4 months in 2005); Cracow University of Technology, Cracow, Poland (2003, 1 month); The Australian National University, Department of Computer Science, Canberra, Australia (8 months, 2005-2006). He joined the Department of Automation at the Szechenyi Istvan University, Gyor, Hungary in 2007 as a senior lecturer, in 2008 as an assistant professor, and in 2009 as an associate professor. He was a visiting researcher in the Graduate School of System Design at the Tokyo Metropolitan University from September 2010 to March 2011 and from September 2011 to February 2012. He was an associate professor in the Graduate School of System Design at the Tokyo Metropolitan University from April 2012 to March 2017. He was an associate professor in the Department of Mechatronics, Optics and Mechanical Engineering Informatics at Budapest University of Technology and Economics from February 2018 to August 2021. He is the Head of the Department of Artificial Intelligence at Eötvös Loránd University, Faculty of Informatics, Budapest, Hungary, since September 2021. Research interests: computational intelligence, automatic identification of fuzzy rule-based models and some neural networks models, bacterial evolutionary algorithm, memetic algorithms, applications of computational intelligence in robotics, cognitive robotics. Membership in scientific societies: John von Neumann Computer Society, Hungarian Academy of Engineering, Hungarian Fuzzy Association, IEEE, IEEE Computational Intelligence Society, IEEE Robotics and Automation Society, IEEE Systems, Man, and Cybernetics Society, IEEE Computer Society. He has published about 200 papers in journals and conference proceedings. The number of his known independent references is about 700, with a cumulative impact factor of more than 100. He has been an invited reviewer for numerous scientific journals and conferences.

Abstract

    Streetscape, which represents the characteristics of a city, serves as a canvas that reflects the cultural essence and the emotional connection of its residents. Among its elements, street trees, in particular, illustrate the cyclical rhythm of nature and time through their seasonal changing appearances. In this paper, we propose a location aware system which enables users to experience the seasonal growth stages of street trees within the images of Google Street View. Our system integrates deep learning, computer graphics, and web data mining technologies. Tree images captured from Google MAP are dynamically transformed based on their GPS information and our database, which has web-crawled daily YouTube live camera data since 2021. The experimental results shown that our method can generate vivid, engaging, and dynamic images of street trees that align with the local natural and environmental conditions, therefore successfully enhancing users' experience and perception of seasonal changes in the streetscape.

Biography

     Lieu-Hen Chen (陳履恒) is currently an Associate Professor of the Department of Computer Science and Information Engineering at National Chi Nan University, Taiwan. His research interests include Computer Graphics, Information Visualization, Virtual Reality, and Artificial Intelligence. He received a BS in Computer Science and Information Engineering from National Taiwan University in 1989, and his MS and PhD degrees in Electrical and Electronic Engineering from Tokyo University, Japan in 1994 and 1998, respectively.

Abstract

Recent research in Artificial Intelligence, termed also as the 'AI revolution', shows that AI is changing our life in many fields. Most of us are using Large Language Models based GenAIs, intelligent Agents and ChatGPT more and more in his profession and in daily life. We are more productive and faster, but more and more of us are also scared not to be needed anymore or one day to lose our job? Superintelligence can be the end of humans or helping us to solve most complex problems on earth.

In this talk, Prof. Rätsch will introduce the research of his working group 'Visual Systems for Intelligent Robots' (ViSiR) in collaboration with the Tokyo Metropolitan University. We will see how Chatbots can be even emphatic and improve our self-efficacy, wellbeing or mental health.

All is illustrated on agents and robots of his RT-Lions team, wining world championships in RoboCup. Practical examples are shown from collaborations with industrial or research partners and on current projects with clinics, retirement homes, and the TMU.

Biography

    Prof. Matthias Rätsch is a professor at the Reutlingen University for Artificial Intelligence, Image Understanding, and Interactive Mobile Robotics since 2013. In 2008, he received his Ph.D. degree in the Graphics and Vision Research Group (GraVis) at the University of Basel, Switzerland in 3DMM Face Analysis. Until 2013 he was with the world leading company Cognitec Systems for face recognition (e.g. see the eGates at each European airport for pass control). His research interests are in the fields of Artificial Intelligence, Image Understanding, Autonomous Driving, Bionics, Service and Humanoid Robots, Human Robot Collaboration, Natural Language Processing, LLMs, Chatbots, and AI-Ethics.

    He is the head of the working group ‘Visual Systems for Intelligent Robots’ (ViSiR, www.visir.org) and the RoboCup team ‘RT-Lions’ (www.rt-lions.de) obtaining several awards: World Champion in Graz 2009, German Master ‘09, and still acting Vice World Champion, and after changing to the RoboCup@Home League: 1st Prize at Portuguese Open ’16, 4th Prize German Open, 8th at World Championship in Nagoya, Japan ‘17, 1st Prize SICK Robot Day ‘18, 5th at World Championship in Sydney, Australia ’19 and recently 3rd Prize at World Championship Worldwide Virtual 2021. Prof. Rätsch has been a member of the program committee and a session chair for several international conferences and was invited for several speeches including keynote, seminal and training speeches.

   Prof. Rätsch has published more than 70 international academic research papers and journals, like at the IEEE Transactions on Image Processing journal or at the SIGGRAPH conference. The publications of his group were honored with several awards, like the Otto-Johansen-Price or the Robert Bosch Price. He led large research and industrial founding projects in collaborations with strong industrial partners, like BMW, Mercedes Benz Daimler, BOSCH, FESTO or KUKA.

Abstract

The convergence of the Internet of Things (IoT) and multi-agent systems (MAS) is transforming decision-making in diverse sectors. The highly dynamic nature of IoT environments demands adaptive and decentralized frameworks capable of responding to evolving conditions in real time. By enabling real-time data sharing and collaborative problem-solving among autonomous agents, IoT-MAS frameworks enhance operational efficiency and scalability. This talk explores how IoT-enabled MAS facilitate intelligent, decentralized, and adaptive decision-making, addressing critical challenges such as scalability, interoperability, and real-time data processing. Through real-world applications, we will demonstrate how MAS, powered by IoT, optimizes resource allocation, enhances coordination, and enables autonomous decision-making in complex environments. However, despite the potential benefits, IoT-enabled MAS face significant challenges, including managing uncertainties within IoT infrastructures and developing context-aware decision-making frameworks. This talk will also address the implications of these challenges for future research and development to enhance decision-making capabilities in complex IoT ecosystems.

Biography

Kurnianingsih is an Associate Professor in the Department of Electrical Engineering and the Head of the Center for Research and Community Service, Politeknik Negeri Semarang, Indonesia. She received a Ph.D. in Electrical Engineering and Information Technology from Universitas Gadjah Mada, Indonesia, in 2018. Her research focuses on sensor networks, anomaly detection, multi-agent systems, and recommender systems. She has been an executive committee member in IEEE Region 10 (Asia-Pacific) since 2018, was appointed as the Vice Chair of Professional Activities for 2025-2026, the Past Treasurer for 2023-2024, and the Past Chair of the Information Management Committee for 2018-2022. She currently serves as the 2025 Chair of the IEEE Indonesia Section, the 2025 IEEE MGA IT Coordination and Oversight Committee in the U.S., and the 2025 IEEE MGA AdHoc Committee on Reaching New Audiences in the U.S. She has received several prestigious awards, including the IEEE Region 10 Directors' Discretionary Award in 2024, the IEEE MGA Achievement Award in 2020, and the IEEE Region 10 Young Professionals Award in 2018. 

Abstract

    Brain function is a prime manifestation of an emergent phenomenon, arising from multi-level neural activities and their sophisticated interactions. These levels span from nonlinear dynamics at the neuronal scale to interactions within local neural circuits via synaptic connections, and further extend to brain-wide networks between regions, as revealed by connectomics studies. Efforts to elucidate the mechanisms underlying such emergence have evolved over decades. These efforts include in vitro and in vivo electrophysiological experiments, structural analyses of neural networks using diffusion tensor imaging (DTI), and functional network studies employing high-temporal-resolution modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). More recently, advances in data-sharing frameworks have enabled access to large-scale neuroscience datasets, accelerating data-driven research approaches. These advancements have also provided new insights into neurological disorders, particularly psychiatric conditions characterized by abnormal neural dynamics. This talk will present the latest developments in data-driven analyses for understanding the emergence of brain function. Additionally, it will highlight our recent collaborative research with Indonesian researchers utilizing EEG data. 

Biography

    Sou Nobukawa graduated from the Department of Physics and Earth Sciences, University of the Ryukyus, in 2006, and earned his Ph.D. in 2013 from the University of Hyogo. He is currently a professor in the Department of Computer Science at Chiba Institute of Technology. His research focuses on neural dynamics and cognitive functions, employing mathematical modeling and neuroimaging data analysis from EEG, MEG, and pupilometry. Prof. Nobukawa has published his work in leading journals, including NeuroImage and IEEE Transactions on Neural Networks and Learning Systems. 

    He is a Senior Member of the International Neural Network Society (INNS) and IEEE,  as well as an active member of JNNS, IEICE, SICE, ISCIE, and other academic societies. His contributions have been recognized through numerous awards, such as the SICE Encouragement Prize (2016), the Young Researcher Award from the IEEE Computational Intelligence Society Japan Chapter (2019), Best Paper Awards at the 29th FAN Symposium (2019), SSI (2021), and FIT (2022), as well as the Excellent Research Award from the Japanese Neural Network Society (2021). 

Abstract

    IoT technologies in mobility such as connected, autonomous, shared, and electric (CASE) cars have changed mobility from simple personal/public binary matters to more complex systems called Mobility-as-a-Service (MaaS). In response to this trend, Toyota Motor Corporation and Kyoto University have started a joint project to develop fundamental technologies for MaaS from the mathematical scientific viewpoint, recruiting applied mathematicians, computer scientists, and engineers from both institutions as well as some others. The speaker is involved in the project as the leader of the data science and machine learning group. In this talk, I will introduce research topics to solve the mobility problem in a shrinking/aging city.

Biography

    Kazushi Ikeda got his B.E., M.E., and Ph.D. in Mathematical Engineering from University of Tokyo in 1989, 1991, and 1994. He joined Kanazawa University as an assistant professor in 1994 and became a junior/senior associate professor of Kyoto University in 1998 and 2003, respectively. He has been a full professor of NAIST since 2008. His research interests include machine learning theory and its applications in biomedical engineering, mathematical biology, and data science.

Abstract

The neural basis of higher brain dysfunction has not been extensively studied because it does not involve significant perceptual or motor deficits, and there are no clear criteria for its definition. Cognitive flexibility, which refers to the ability to switch perspectives, thoughts, and strategies, has recently become an important concept in the study of higher brain functions. Currently, the Wisconsin Card Sorting Test (WCST) plays a central role in the assessment of cognitive flexibility. The WCST in volves recognition of shapes, colors, and numbers. It is too time-consuming to train in monkeys and completely impossible to implement in rodents. Therefore, it is difficult to use extensively in comprehensive studies that take advantage of the characteristics of humans and a variety of other animals. Here we discuss the two-target search task that we used in our physiological experiment with monkeys as an alternative to the WCST. In the task, two out of four adjacent targets were alternately correct, and the valid target pair was switched after consecutive correct trials in the exploitation phase. The agent had to find a new pair during the exploration phase. Performance on this task allows the assessment of three subcomponents of cognitive flexibility: cognitive set maintenance ability based on the maintenance of correct answers during the exploitation phase, cognitive set shifting ability based on the persistence of old valid pairs during the exploration phase, and reasoning ability based on search strategies. Moreover, this task is not only feasible regardless of the subject's intelligence, but also has the advantage of facilitating model studies based on reinforcement learning.

Biography

    Kazuhiro Sakamoto was born in 1968. He received his B.S. and M.S. from the University of Tokyo in 1991 and 1993, respectively. He became a research associate and assistant professor at Tohoku University in 1993 and 2007, respectively. He received his Ph.D from Tohoku University in 2009. He became an associate professor at Tohoku Medical and Pharmaceutical University in 2016. His research interests include neurophysiology, complex systems theory and neural networks. He is a member of the JNSS, PSJ, IPSJ and SfN, and a governing board member of JNNS.