Prof. Kiyoshi Kiyokawa
Nara Institute of Science and Technology, Japan
 

 

Biography: Kiyoshi Kiyokawa is a Professor at the Nara Institute of Science and Technology (NAIST), where he leads the Cybernetics and Reality Engineering (CARE) Laboratory. He is a distinguished researcher in virtual reality (VR), augmented reality (AR), and human augmentation. Professor Kiyokawa received his M.S. and Ph.D. degrees from NAIST in 1996 and 1998, respectively. His career includes positions as an Associate Professor at Osaka University, a researcher at the Communications Research Laboratory (now NICT), and a visiting scholar at the University of Washington’s Human Interface Technology Laboratory. His significant contributions have been recognized with numerous accolades, including the 2022 IEEE VGTC Virtual Reality Technical Achievement Award, the inaugural 2022 IEEE VGTC Virtual Reality Service Award, and the title of Fellow from the Virtual Reality Society of Japan (VRSJ).

Professor Kiyokawa's research has resulted in several pioneering technical achievements. He is known for developing advanced head-mounted display (HMD) systems, including ELMO, the first occlusion-capable optical see-through HMD in 1999. His foundational work also includes VLEGO, one of the first collaborative immersive modelers, and SeamlessDesign, which featured the first transitional interface for switching between VR and AR. His research extends to vision augmentation and assistive interfaces, collaborative virtual and augmented reality, and innovative multimodal interfaces.

Beyond his research, Professor Kiyokawa has demonstrated a profound dedication to the academic community through extensive service and leadership. He has served on the Steering Committees for top-tier conferences, including IEEE VR, IEEE ISMAR, and IEEE 3DUI. His leadership roles are numerous, having served as General Co-Chair for IEEE VR 2019 in Osaka, which was the largest in-person conference in its history at the time. Additionally, he is on the Editorial Board of IEEE Transactions on Visualization and Computer Graphics (TVCG) and has frequently been a Board Member of the VRSJ.

 

 

 

Prof. Shin'ya Nishida
Kyoto University, Japan
 

Speech Title: Toward Digital Twins of Human Visual Perception

Abstract: Virtual and mixed reality technologies seek to reproduce the sensory experiences of the real world. Yet, faithfully simulating every aspect of human sensory input remains computationally infeasible. Practical systems must therefore simplify or omit information—but ideally in ways that users never notice. Achieving this goal requires not only advances in engineering but also a deep understanding of human perception. In other words, effective VR/MR systems should exploit the characteristics and limitations of the human visual system, allowing them to “fool the brain” without degrading the perceived experience.

Traditionally, the development of immersive systems has relied heavily on user studies. While indispensable, human experiments are inherently limited by practical and ethical constraints, making it difficult to exhaustively explore the vast design space of VR/MR systems. This motivates a new paradigm: replacing the human component of the conventional framework with a digital twin of human perception. Such a perceptual digital twin would enable rapid evaluation and optimization of system designs while explicitly accounting for human perceptual characteristics.

Recent advances in computer vision have made computational models of vision far more powerful than ever before, in some cases surpassing human performance on visual recognition tasks. However, high performance alone does not guarantee that these models perceive the world as humans do. To serve as perceptual digital twins, computational models must reproduce not only human-level performance but also human perceptual behavior, including its strengths, limitations, and systematic biases. Achieving this goal requires both biologically and psychologically informed machine models and large-scale human perceptual datasets that allow direct comparisons between model predictions and human behavior.

In this keynote, I will discuss our recent efforts toward building digital twins of human visual motion perception, focusing on computational models and benchmark datasets that capture human characteristics.

Biography: Shin’ya Nishida is Professor at the Graduate School of Informatics, Kyoto University, and former Senior Distinguished Scientist at NTT Communication Science Laboratories, Japan.

His research focuses on human sensory information processing, including visual motion perception, time perception, material perception, tactile perception, and multisensory integration. Although originally educated in psychology at Kyoto University, he pursued a broad spectrum of research ranging from fundamental perceptual science to engineering-oriented studies during his long career at NTT laboratories. His work combines psychophysics, cognitive neuroscience, computational modeling, and engineering approaches to understand human perceptual intelligence. His recent interests include the use of machine vision systems to better understand human visual intelligence. He is widely recognized as one of Japan’s leading vision scientists and has served on the editorial boards of major journals in the field, including Journal of Vision, Vision Research, and Annual Review of Vision Science.

He has also played leading roles in large-scale interdisciplinary research initiatives, including the Japanese national projects “Innovative Shitsukan Science and Technology” (2015–2020) and “Deep Shitsukan” (2020–2025), both focusing on the science of material and sensory perception. He currently serves as Sub-program Director of JST Moonshot Goal 9.

He has received numerous honors, including the Japan Society for the Promotion of Science Prize (2006), the MEXT Prize for Science and Technology (2015), the Special Prize of the Japanese Psychological Association International Award (2023), and the Medal with Purple Ribbon from the Japanese government (2024).

 

 

 

Prof. Mayuri Mehta
Sarvajanik College of Engineering and Technology, India
 

 

Speech Title: Transforming Healthcare with AI: Emerging Trends, Applications, and Future Research Directions

Abstract: Artificial Intelligence (AI) is driving the transformation of next-generation healthcare by enabling intelligent, data-driven, and patient-centric solutions. The rapid growth of electronic health records, medical imaging, genomics, wearable sensors, and real-time monitoring systems has generated vast volumes of heterogeneous healthcare data. Advanced AI techniques are increasingly being leveraged to extract meaningful insights from this data, thereby improving clinical decision-making, diagnosis, and treatment planning.

Recent advancements, including Generative AI, Explainable AI (XAI), Multimodal AI, Self-Supervised Learning (SSL), Federated Learning, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Agentic AI, are reshaping healthcare applications. These technologies support diverse areas such as disease diagnosis, medical image analysis, robot-assisted surgeries, biomedical wearables, personalized medicine, drug discovery, bioinformatics, telemedicine, and healthcare analytics. AI-driven approaches enable pattern discovery in complex medical datasets, facilitating predictive modeling and evidence-based care.

Despite these advancements, critical challenges remain, including data privacy, model interpretability, bias, regulatory compliance, and ethical deployment. Addressing these concerns is essential for building trustworthy and scalable healthcare systems.

This session provides a comprehensive overview of emerging AI trends and their applications in healthcare, use cases, associated challenges, and future research directions in modern healthcare. It offers an interdisciplinary perspective, equipping participants from academia, industry, and healthcare domains with insights into the evolving AI-driven healthcare ecosystem.

Biography: Dr. Mayuri Mehta is a Professor of Computer Engineering at Sarvajanik College of Engineering and Technology, India, with over 25 years of academic, research and leadership experience. She acts as the institute’s International Relations and External Affairs Officer and leads the AI Task Force at Sarvajanik University.

Her research work focuses on Applied AI and Data Science, Medical Image Analysis, Health Informatics, and Computer Vision, with particular interest in AI for healthcare and societal impact.. She has delivered more than 150 invited talks, keynote lectures, and technical sessions at international conferences, universities, and professional forums across the world. Her talks have been hosted by institutions including Imperial College London, Coventry University & Ulster University in UK, University of Rhode Island in USA, and Pwani University in Kenya along with numerous IEEE International conferences & IEEE international sections.

She owns 18 patents, 6 published books and 60+ research papers, and has secured multiple research grants. Her contributions to engineering education and research have been recognized through multiple honors, including the ‘Best Paper Awards’, the ‘Nation Builder Award (Rotary District 3060)’, ‘Best Teacher Award’ by 112 years old philanthropic Sarvajanik Education Society, and ‘Researcher of the Year Award (Engineering – Female)’. She was also featured in the “Women in AI” initiative by INDIAai (INDIAai.gov.in), recognizing her contributions to Artificial Intelligence research and education (Women in AI on INDIAai).

 Dr. Mehta is a Senior Member of IEEE and an active member of IEEE societies including Women in Engineering, EMBS, and SPS. She is also a Lifetime Member of professional bodies such as ISTE and CSI.

 

 

 

Prof. Wei-Chang Yeh
ASPEED, NTHU, and CYCU Chair Professor, National TsingHua University, Taiwan
 

 

Biography: Dr. Wei-Chang Yeh is the ASPEED Chair Professor and Chair Professor in the Department of Industrial Engineering and Engineering Management at National Tsing Hua University (NTHU), Taiwan, and also serves as Chair Professor at Chung Yuan Christian University. He received his M.S. and Ph.D. degrees in Industrial Engineering from the University of Texas at Arlington. His research focuses on algorithm design, exact solution methods, soft computing, network reliability, and NP-hard optimization problems.

Dr. Yeh has published more than 300 SCI-indexed journal papers and holds more than 70 patents. He has been listed among Stanford/Elsevier’s Top 2% Scientists worldwide for both career-long and singleyear impact since 2020. His honors include two Outstanding Research Awards, one Distinguished Scholars Research Project Award, and two Overseas Research Fellowships from Taiwan’s MOST/NSTC.

He serves as an Associate Editor for IEEE Transactions on Reliability, IEEE Access, and Reliability Engineering & System Safety. He is the proposer of Simplified Swarm Optimization (SSO) and the BinaryAddition-Tree (BAT)framework. Dr. Yeh is also an NVIDIA University Ambassador for the Deep Learning Institute and has received NVIDIA research grant support.

 

 

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