Hironori Washizaki is a Professor and Associate Dean of the Research Promotion Division at Waseda University, and serves as a Visiting Professor at the National Institute of Informatics. He has served as a director and advisor to companies such as eXmotion and SI&C, effectively bridging academia and industry in computing, with a focus on software and AI engineering. Hironori is the Immediate Past President of the IEEE Computer Society and currently co-chairs both the IEEE TAB Ad Hoc Committee to Refine Budgeting Process and the CS Ad Hoc Committee on AI Strategy. His extensive technical leadership and IEEE governance experience includes roles as TAB Budget Process WG Chair and Editor of the IEEE-CS Guide to the Software Engineering Body of Knowledge (SWEBOK Guide). He has received numerous honors, including the Computer Best Paper Award and the Spirit of the CS Award. Beyond his work with IEEE, Hironori contributes to international initiatives such as ISO/IEC. He has led many academia-industry joint research projects and large-scale funded projects in AI software engineering, and heads the SmartSE professional IoT/AI/DX education project. More information: http://www.washi.cs.waseda.ac.jp/washizaki/.
This keynote begins with the IEEE Megatrends 2030 Report, which forecasts breakthrough technologies highlighting the transformative role of AI as general-purpose infrastructure and trust, governance, and human–AI interaction as key enablers, with energy availability and efficiency as important constraints, and the IEEE Computer Society's Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) 4.0a, which reflects contemporary software engineering paradigms and emerging areas, including Agile and DevOps, software architecture, security, and AI. Building on these developments, the talk explores how AI can support the engineering of trustworthy systems while addressing diverse stakeholder concerns, particularly those related to trust and sustainability. It presents a comprehensive overview of generative and agentic AI approaches to requirements engineering, based on a systematic literature review. The review identifies reproducibility, hallucinations, and interpretability as interconnected challenges that can undermine the trustworthiness and consistency of AI-supported requirements engineering. The talk then presents recent advances in AI-empowered requirements engineering, including requirements negotiation and argumentation using multiple LLM agents. It also introduces LLM-based topic modeling and gap analysis to identify and analyze diverse stakeholder concerns, illustrated through a VR application case study. These approaches demonstrate how generative and agentic AI can move beyond assisting individual requirements activities toward supporting the analysis and negotiation of diverse stakeholder concerns. The talk concludes by discussing the implications of these developments for engineering trustworthy and sustainable consumer products and services, emphasizing the increasingly important role of requirements engineering in translating and reconciling technological capabilities, stakeholder needs, and broader societal concerns.
Albert Liu, Kneron’s founder and CEO. After graduating from the Taiwan National Cheng Kung University, he got the scholarships from Raytheon and the University of California to join the UC Berkeley/UCLA/UCSD research programs, and then got his PhD in Electrical Engineering from the University of California Los Angeles (UCLA). Before establishing Kneron in San Diego in 2015, He has worked in R&D and management positions in Qualcomm, Samsung Electronics R&D centre, MStar and Wireless Information. Albert has been invited to give lectures of computer vision technology and artificial intelligence at the University of California, as well as to be a technical reviewer for many internationally renowned academic journals. In addition, Albert owned more than 30 international patents in the areas of artificial intelligence, computer vision and image processing. He has published more than 70 papers in major international journals. In 2020, published Deep Learning – Hardware design. In 2021, published Introduction to Artificial Intelligence 4th Industrial Revolution.Moreover, Albert has received numerous awards such as 2021 IEEE TCAS Darlington award, 2022 Pan Wen-Yuan Foundation IoT Innovation Award, 2022 IEEE CTSoc Awards Corporate Innovation Leadership Award.
As AI computing outpaces Moore’s Law, unsustainable data center energy consumption demands a shift to the Edge. This presentation explores the evolution of NPU-driven architectures, demonstrating how decentralized edge processing achieves exceptional energy efficiency, offline reliability, and privacy protection, ultimately shaping the next wave of global AI infrastructure.
Pola Goldberg Oppenheimer is a Professor in Micro-Engineering and Bio-Nanotechnology at the University of Birmingham’s School of Chemical Engineering and Healthcare Technologies Institute. She leads the Advanced Nano-Materials & Surface Architectures (ANMSA) Group, collaborating with clinical teams at Queen Elizabeth Hospital and Birmingham Enterprise. Her research focuses on micro-engineering and hybrid nanomaterials for miniaturized healthcare devices. Featured in top journals, BBC News, and radio, her work has earned funding from the Royal Academy of Engineering, EPSRC, EU Consolidator Grant, and Wellcome Trust. Professor Oppenheimer has published in Nature Biomedical Engineering, Science Advances, and Advanced Materials, and received the Carl-Zeiss Award. She has presented at major conferences, authored a book, and contributed to science policy as an envoy to the House of Commons Science & Technology Committee and a member of the Parliamentary Engineering Group at House of Lords.
Many diseases continue to cause significant morbidity and mortality worldwide, with rising incidences leading to long-term disabilities. Rapid and accurate point-of-care (PoC) diagnostics are critical for effective management but remain challenging due to non-specific symptoms and delayed or incorrect treatments, contributing to preventable cognitive, emotional or physical morbidity. This underscores the urgent need for portable PoC technologies to enable timely intervention. This work presents the development of a suite of innovative miniaturized micro-engineered devices for the rapid detection of disease biomarkers at trace levels, enabling early-stage diagnosis, prognosis and monitoring of various human diseases. These devices leverage Raman spectroscopy, optofluidic technologies and advanced computational methods to overcome the challenges associated with PoC diagnostics. Applications span cardiovascular, inflammatory bowel disease and a particular focus on traumatic brain injuries (TBI). TBIs are notoriously difficult to diagnose in pre-hospital settings, where most acute cerebral damage occurs. Current diagnostic tools are woefully inadequate, and no PoC technology exists. To address this, we have developed three portable micro-engineered devices including a low-cost, handheld device for non-invasive neurological diagnostics through the eye. This technology analyses the neuroretina, as a projection of the central nervous system, by safely acquiring molecular Raman fingerprints of TBI biochemistry. Specific spectral bands are successfully classified using a robust artificial neural network algorithm, enabling automated data interpretation and reducing reliance on specialist expertise. Clinically, this technology offers rapid, cost-effective diagnostics for TBI at the PoC, such as roadside accidents or pitch-side in contact sports as well as monitoring of neurological diseases. This transformative platform represents a significant step towards accessible and timely PoC diagnostics, improving outcomes for patients with TBIs and other neurological conditions.
CTSoc AdministratorCharlotte Kobert charlotte.kobert@ieee.org
