Talk Title:

Recent Advances in AI-Enabled Neurodegenerative Diseases

Abstract

Recent advances in artificial intelligence (AI) have transformed neuro-oncology domain by enabling automated, accurate, and interpretable brain tumor recognition from medical imaging data. This keynote explores the state-of-the-art in AI-enabled brain tumor analysis, covering brain tissue and tumor classification, automated segmentation, survival time prediction, treatment response estimation, and the emerging role of brain digital twins. The keynote discusses deep learning architectures, multimodal data fusion of MRI modalities and clinical data, and optimization-driven model enhancements for improved diagnostic performance. An emphasis is placed on explainable AI (XAI) techniques that address the “black-box” challenge, building trust and transparency in clinical decision-making. The keynote also highlights the integration of AI with privacy-preserving and security-aware workflows, ensuring compliance with healthcare standards while safeguarding sensitive patient data. By bridging innovation in AI, interpretability, and secure medical data handling, these advances pave the way toward reliable, patient-centered, and clinically adoptable brain tumor recognition systems.

Bio

Robertas Damaševičius is a professor at the Department of Software Engineering at Kaunas University of Technology and at the Department of Applied Informatics at Vytautas Magnus University, Lithuania, and an adjunct at the Faculty of Applied Mathematics, Silesian University of Technology, Poland. With extensive expertise in artificial intelligence, medical imaging, and digital health, he is a recognized thought leader in his fields. Prof. Damaševičius has a strong background in doctoral supervision and project evaluation, serving as an editor and associate editor for numerous international journals such as Information Technology and Control, and International Journal of Imaging Systems and Technology (IMA). He has delivered keynote speeches at prestigious conferences worldwide, sharing his insights on topics ranging from AI in product development to sustainable health systems and digital twins. His recent research interests include explainable AI, computational intelligence, and digital health twins. He has authored over 400 publications, which have been cited more than 21,000 times. His h-index of 77 reflects the reach and impact of his contributions to the field. Beyond academia, he actively collaborates on international projects and engages in knowledge transfer initiatives to bridge the gap between research, education and practical applications.

Talk Title:

Federated Learning for Data Privacy

Abstract

In recent years, mobile devices can be equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications. Traditionally, any cloud-based Machine Learning (ML) approach requires that data be centralized on a cloud-based server/data center. However, this can result in critical issues related to unacceptable latency and communication inefficiency as well as major security and privacy concerns. However, conventional ML technologies still require personal data to be shared. Recently, in light of increasing security and privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. In a large, complex mobile edge networks, FL still faces implementation challenges with regard to communicational costs, resource allocation, security, and privacy. In this talk, we begin with an introduction to the background and fundamentals of FL. We then discuss how FL can work to try and preserve privacy while maintaining security of data. Finally, we discuss some open research areas and specific open problems where attendees may be able to make an impact.

Bio

Gautam Srivastava was awarded his B.Sc. degree from Briar Cliff University in U.S.A. in the year 2004, followed by his M.Sc. and Ph.D. degrees from the University of Victoria in Victoria, British Columbia, Canada in the years 2006 and 2012, respectively. He then taught for 3 years at the University of Victoria in the Department of Computer Science, where he was regarded as one of the top undergraduate professors in the Computer Science Course Instruction at the University. From there in the year 2014, he joined a tenure-track position at Brandon University in Brandon, Manitoba, Canada, where he currently is active in various professional and scholarly activities. He was promoted to Professor in January 2023. Dr. G, as he is popularly known, is active in research in the fields of security, privacy and the Internet of Things. In his 10-year academic career, he has published a total of 500 papers in high-impact conferences in many countries and in high-status journals (SCI, SCIE) and has also delivered invited guest lectures on Big Data, Cloud Computing, Internet of Things, and Cryptography at many international universities. He is an Editor of several international scientific research journals including IEEE Transactions on Industrial Informatics, IEEE IoT Journal, IEEE Transactions on Services Computing, IEEE Transactions on Computational Social Systems, IEEE Transactions on Cybernetics, and Information Sciences. His research program is funded by the National Sciences and Engineering Research Council of Canada, MITACS, and the National Cybersecurity Consortium.

Talk Title:

Next-Generation Cybersecurity: Emerging Threats, Intelligent Defenses, and Strategic Research Frontiers

Abstract

The cybersecurity landscape is undergoing a profound transformation as emerging technologies reshape both the nature of digital systems and the threats targeting them. AI-driven, adaptive, and polymorphic attacks are increasingly undermining traditional security models, while the rapid convergence of IoT, 5G/6G, cloud-edge ecosystems, autonomous system, and cyber-physical infrastructure continues to expand the global attack surface. This interconnectivity, often built on insecure-by-design components, creates complex, systemic, and cascading vulnerabilities across industries, governments, and critical infrastructure. At the same time, adversaries are accelerating their use of advanced AI capabilities, including large language models (LLMs), generative AI (GenAI), autonomous agents, and automated exploitation frameworks. These tools enable unprecedented speed, scale, and deception in cyberattacks, reinforcing the urgency for next-generation defense strategies. This keynote will explore the evolving threat landscape through the lens of emerging technologies and highlight cutting-edge, research-driven innovations designed to secure future intelligent systems and services, including smart mobility. Drawing on indigenous cybersecurity advancements, the talk will outline novel methodologies, resilient architectures, and strategic research directions critical for safeguarding an increasingly interconnected digital ecosystem. Attendees will gain a forward-looking perspective on the technologies, tools, and scientific breakthroughs shaping the next era of cybersecurity resilience.

Bio