Bio:  Danilo Pau is the technical director, IEEE, AAIA & ST Fellow, APSIPA Life member, and Sigma-Xi member in System Research and Applications, Colleoni, Agrate Brianza site. He has produced 109 invention requests, 80 EU, and 71 US application patents in STMicroelectronics (ST). He has given 141 invited talks, including keynotes, seminars, and tutorials at universities and conferences. He graduated in Electronic Engineering in 1992 from Politecnico di Milano, and has been in ST for 32 years. He has been involved in tiny AI since 2016 (through the release of the 2024 Unified AI Core Technology), now part of SM32CubeMX, Stellar-Studio, SPC-Studio.AI, MEMS Studio, STM32 Developer Cloud, and the Suite. He served as Industry Ambassador for the Italy section and coordinated IEEE Region 8 South Europe industry ambassadors as part of Action for Industry; He was vice-chairman of the “Intelligent Cyber-Physical Systems” Task Force IEEE CIS, coordinator of the IEEE R8 AfI internship initiative. He serves the EDGEAI Foundation as Program co-chair, TPC member, and as chair of the TinyML on Device Learning, co-founded AutoTinyML and GenerativeAI at the Edge, and co-chair of TinyML Talks.

Bio: Chang Gao received his Ph.D. degree with distinction in Neuroscience from the Institute of Neuroinformatics, University of Zürich and ETH Zürich in March 2022, and his M.Sc. degree from Imperial College London in September 2016 and his B.Eng. degree from the University of Liverpool and Xi’an Jiaotong-Liverpool University in July 2015. Since August 2022, he has been an Assistant Professor at Delft University of Technology, The Netherlands, in the Department of Microelectronics, where he leads the Lab of Efficient Machine Intelligence (EMI) and conducts research on algorithm-hardware co-design for edge intelligence.

Bio: Mike Holenderski is an assistant professor at the Eindhoven University of Technology. His research focuses on machine learning for industrial systems, with an emphasis on reliability, explainability, and data efficiency, and with applications in predictive maintenance, quality control, and process optimization.

Bio: Maxim Yudayev is a graduating PhD in the team of Prof. Bart Vanrumste at the e-Media Research Lab of KU Leuven in Belgium. He develops novel software and hardware edge AI technologies that enable real-time distributed sensing and continuous multimodal processing of sensor data for intelligent ambient healthcare applications. Prior to KU Leuven, Maxim worked at the Nokia Bell Labs research centre in Antwerp, Belgium, where he patented a look-ahead computation hardware mechanism for efficient and energy-preserving AI computer architectures. With domain expertise in digital design, networks, electronics, distributed parallel software, and embedded systems, he focuses on delivering practical solutions from the systems-level perspective to meet complex R&D challenges.

Bio: Louis Flynn is a leading postdoctoral researcher in human-robot interaction and rehabilitation robotics at the Robotics & Multibody Mechanics (R&MM) and BruBotics research groups of Vrije Universiteit Brussel (VUB) in Belgium. His work focuses on the mechatronic design and advanced control of lower-limb assistive technologies, including active prostheses (like the CYBERLEG X-Leg) and exoskeletons. Dr. Flynn is a key contributor to the design of adaptive controllers that use human-in-the-loop optimization (HILO) strategies to personalize robotic assistance of intelligent mobility devices, reduce user effort, and maximize functional ability of the wearer. His research is dedicated to translating compliant actuation and bio-inspired robotics into safe, energy-efficient, and user-accommodating devices for real-world impact.

Bio: Kasim Sinan Yildirim is an Associate Professor at the Department of Information Engineering and Computer Science, University of Trento, Italy. His research interests revolve around low-power and networked embedded sensing systems, with a specific focus on software systems and programming support for intermittent computing, hardware support for batteryless computing, transiently-powered networking, and tiny machine learning on the batteryless edge. He has authored several publications in prestigious journals and conferences, including SenSys, EuroSys, MobiSys, ASPLOS, UbiComp, and OSDI. He serves on the technical program committees of several conferences, including SenSys, EWSN, EMSOFT, DATE, MobiSys, and NSDI, and as an associate editor for the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).

Bio: Salvatore Tedesco is leading a research team in the data analytics area with Tyndall National Institute, UCC. His research interests include wearable and pervasive computing, edge intelligence, edge AI, and applied artificial intelligence, with applications in healthcare and well-being, human motion analysis in sports and clinical populations, digital health, physiological monitoring, wireless communications, agriculture technology, and Industry 4.0. Dr. Tedesco has authored over 100 peer-reviewed publications in international journals and conference proceedings. He has secured more than €1.5 million in competitive funding as Principal Investigator and Co-Principal Investigator and has successfully managed and led over 35 industrial and research projects. He was the Founder and Chair of the Tyndall Early-Career Researchers Network (TEC-Net) (2020–2022) and served as Vice-President of the Young European Associated Researchers (YEAR) Network Association (2021–2025).

Bio: Charis Kouzinopoulos is an Assistant Professor of the Internet of Things and Coordinator of the Computer Systems Research Area in the Department of Advanced Computing Sciences of the Faculty of Science and Engineering at Maastricht University.

His research vision centers on developing sustainable, low-power, intelligent systems across high-impact societal sectors, such as digital agriculture, space applications and the industry 4.0 as well as frontier scientific domains, including high-energy particle physics and gravitational wave analysis.

Bio: Kuan-Hsun Chen is an Assistant Professor at the University of Twente, the Netherlands. He received his PhD in Computer Science from TU Dortmund University, Germany, in 2019. His research focuses on dependable embedded and computing systems through hardware–software co-design, with an emphasis on cross-layer techniques for improving timing predictability, efficiency, and resilience in emerging architectures. His interests include real-time and data-intensive computing platforms, system software, AI compilation, and emerging memory technologies. He is passionate about open-source development, particularly in relation to RTEMS and tl2cgen. He has served on the Technical Program Committees of leading conferences in computer systems, embedded systems, real-time systems, and design automation, including EuroSys, DAC, RTSS, and RTAS, and is an Associate Editor of IEEE Nanotechnology Magazine. He is also a Senior Member of IEEE.

Bio: Jie Yang is an Associate Professor in the Web Information Systems (WIS) group at the Department of Software Technology, TU Delft. He also co-leads GENIUS, an ICAI lab (part of the national program LTP ROBUST) dedicated to developing and using Generative AI. Previously, he was a machine learning scientist at Amazon Research in Seattle. His research focused on human-centered computing for robust AI, with a focus on language technologies. His work has received six “best paper” awards or nominations at premier AI conferences, including ACM TheWebConf/WWW (both 2022 and 2023), AAAI/ACM AIES (2023), AAAI HCOMP (2022), ACM SIGIR (2024), and ACM HT (2017). The work finds application across a wide range of societal domains, via collaboration with medical centers, libraries, banks, and companies. Jie is an associate editor for the Journal of Human Computation and Frontiers of Artificial Intelligence; he regularly serves on the senior program committees of ACL ARR, TheWebConf/WWW, AAAI, and CIKM.