Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.
Prof. Dr.-Ing. Weihan Li is a Junior Professor at RWTH Aachen University, specializing in Artificial Intelligence and Digitalization for Batteries. He is affiliated with the Institute for Power Electronics and Electrical Drives (ISEA) and the Center for Ageing, Reliability, and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL). His research bridges informatics, electrochemistry, and power electronics to advance battery technology through AI. B.Sc. in Automotive Engineering (Tongji University, 2014) M.Sc. in Automotive Engineering and Transport (RWTH Aachen, 2017) Ph.D. in Electrical Engineering and Information Technology (RWTH Aachen, 2021, summa cum laude) Prof. Li’s research focuses on AI-driven battery modeling, diagnostics, and optimization. Key areas include digital twin technology, electrochemical parameterization, and lifetime prediction using field data. He explores multi-scale kinetic processes, thermal management, and mechanical-electrochemical coupling effects in battery systems. The articles listed reflect his leadership in AI-powered battery analytics, spanning degradation prediction, fast charging, failure mode analysis, and grid-scale storage. His work emphasizes both theoretical innovation (e.g., diffusion models, physics-informed neural networks) and practical applications (e.g., second-life battery screening, automotive integration). Clarivate Highly Cited Researcher 2024 BMBF BattFutur Research Group (€2M+) German Thesis Award (Körber Foundation) Reichart Prize vgbe Innovation Prize Battery Young Research Award Umbrella Award RWTH Innovation Award Prof. Li leads an interdisciplinary research group with over €6 million in grants from BMBF, BMWK, BMDV, European Commission, and industry partners. His teams focus on battery informatics, AI-driven diagnostics, and digitalization of testing processes at CARL and ISEA.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Kirby Nielsen is a Professor of Economics and William H. Hurt Scholar at the California Institute of Technology (Caltech), affiliated with the Division of the Humanities and Social Sciences. His research focuses on Experimental Economics, Decision Theory, and Microeconomic Theory. Contact him via kirby@caltech.edu (note: Gmail may be more reliable currently). Research interests emphasize experimental methods to study decision-making under uncertainty, preference structures, and behavioral anomalies. Recent work explores common ratio effects, gender confidence gaps, and team dynamics in economic contexts. Publications (2017–2024) address topics ranging from risk preferences to comparative analysis of human and primate decision-making. Notable themes include systematic testing of axiomatic models and the timing of information in strategic interactions. No awards or grants are explicitly listed in the provided materials. Education history is not detailed here, though his affiliation with Caltech suggests a strong academic pedigree in economics.
Jinsong Huang serves as Adjunct Professor in the Materials Science and Engineering department at the University of North Carolina at Chapel Hill, where he leads an interdisciplinary research group focused on perovskite-based electronic materials and devices. His laboratory, housed in Murray Hall 1115, maintains active collaborations with academia, industry, and national laboratories while training next-generation scientists and engineers for competitive job markets. Dr. Huang earned his educational credentials through a rigorous academic path: Ph.D. in Materials Science & Engineering from UCLA (2007), M.S. in Semiconductor Physics from Chinese Academy of Sciences (2003), and B.E. in Materials and Photoelectronic Physics from Xiangtan University (2000). His research program spans Perovskite Solar Cells , Photodetectors , and X-ray Imagers , with particular emphasis on fundamental material physics, device design, stability enhancement, and scalable manufacturing. The group's work bridges applied research with deep scientific understanding, focusing on high-performance, low-cost electronic materials that address critical energy and medical imaging challenges. Current projects include self-powered photon-counting detectors, bifacial perovskite modules, and all-perovskite tandem solar cells. Analysis of recent publications reveals a strategic research trajectory toward commercialization of perovskite technologies, with increasing focus on stability, scalability, and real-world performance metrics. The work spans fundamental science (defect engineering, crystal growth) to applied technologies (medical imaging detectors, flexible solar cells), demonstrating remarkable breadth while maintaining technical depth in perovskite material systems. Highly Cited Researcher 2021 in Material Science and Chemistry Principal Investigator for $1.5 million UNC System Research Opportunities Initiative (2025) Multiple student/postdoc awards including Postdoctoral Awards for Research Excellence Consistent high-impact publications in Nature, Science, and Advanced Materials Huang actively mentors students and postdocs, with notable alumni including four of the 41 Tar Heels ranked as 'highly cited researchers' in December 2023. His research group has secured significant funding including the recent $1.5 million UNC System grant for 'Ultra-High Efficiency Perovskite Tandem Solar Cells' focusing on North Carolina's energy production and reduced fossil fuel dependence. The laboratory maintains strong industry partnerships that facilitate technology transfer and real-world implementation of research findings. The Huang Research Group operates as a dynamic interdisciplinary team with scientists from chemistry, materials science, physics, and electrical engineering backgrounds. Their collaborative culture has produced numerous breakthroughs including record-efficiency perovskite modules certified by NREL, self-powered photon-counting detectors published in Nature, and lead-recycling technologies highlighted in Nature Communications. Current facilities support crystal growth, device fabrication, and advanced characterization of perovskite materials for both energy and radiation detection applications.
Matthias Bucher is a Professor at the Department of Electronics and Computer Engineering, Technical University of Crete. He specializes in analog/RF integrated circuits design, MOSFET compact modeling, and device characterization. His research focuses on nanoscale CMOS, wide-band semiconductor devices, and high-voltage MOSFETs. He leads the Electronics Laboratory and teaches courses such as Electronics II and CMOS Analog IC Design. Education: Ph.D. in Electrical Engineering, Swiss Federal Institute of Technology (EPFL), 1999 M.S. in Electrical Engineering, Swiss Federal Institute of Technology, 1993 Research Interests: Prof. Bucher’s work emphasizes charge-based compact models (e.g., EKV3), RF device modeling, and noise analysis in MOSFETs/JFETs. His contributions include open-source tools for Verilog-A modeling and parameter extraction methodologies for advanced CMOS technologies. Labs/Teams: He directs the Electronics Laboratory , focusing on nanoelectronics and high-reliability circuits. His team collaborates on semiconductor device modeling for aerospace and industrial applications. Grants/Awards: While not explicitly listed, his extensive publication record and leadership in open-source projects indicate sustained recognition in semiconductor research communities.
Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.
Nicola Paltrinieri is a Professor of Risk Assessment at the Department of Mechanical and Industrial Engineering, NTNU (Norway), and an Adjunct Professor at the University of Bologna (Italy). His expertise spans risk assessment, hydrogen technologies, process safety, and data-driven safety management. He holds Chartered Engineer and Chartered Scientist certifications and has served on editorial boards for journals like Safety Science and Journal of Risk Research . Education: PhD in Environmental, Safety and Chemical Engineering (University of Bologna, 2012) Master’s in Chemical and Process Engineering (University of Bologna, 2008) Research Interests: Focuses on hydrogen infrastructure safety, Natech accident analysis, risk-based inspection strategies, and AI integration in safety systems. His work emphasizes sustainable energy transitions and mitigating risks in emerging technologies like hydrogen. Key Projects (2022-2026): H2Glass : Decarbonizing glass and aluminum sectors via hydrogen HyInHeat : Hydrogen technologies for industrial heating HYDROGENi : Norwegian research center for hydrogen/ammonia Awards: Onsager Fellowship (2016–2021) Frank Lees Medal (2012) for safety-related publications Grants & Leadership: Head of NTNU Energy Team Hydrogen, coordinator for EU-funded projects like SUSHy , and active in international risk committees (e.g., EFCE, ESRA). His work bridges academia and industry, with over 8 PhD examinations supervised. Labs/Teams: Leads the NTNU Energy Team Hydrogen and collaborates on initiatives like SH2IFT-2 for safe hydrogen fuel handling. His research group focuses on AI-driven risk analysis and hydrogen infrastructure resilience.
Professor Alasdair McDonald holds the Chair in Renewable Energy Technology at the School of Engineering, University of Edinburgh . His work focuses on the integrated electrical-magnetic-mechanical modeling and design of large electrical machines for offshore renewable energy systems , particularly wind turbine powertrains . He previously served as a Lecturer, Senior Lecturer, and Reader in Wind Turbine Technology at the University of Strathclyde. Education: PhD in Structural Analysis of Low-Speed, High-Torque Generators (University of Edinburgh, 2008) MEng (Hons) in Integrated Electrical & Mechanical Engineering (University of Durham, 2004) Research Interests: Design of permanent magnet electrical machines for wind and marine energy Lightweight generator structures and advanced manufacturing methodologies Condition monitoring using SCADA and vibration data Cost of energy optimization for offshore renewables Projects: STREAM 1: Innovations in Forth/Tay Offshore Wind Clusters (EPSRC, 2025-2029) Wind2DC: Medium Voltage DC Power Take-Off Systems (EPSRC, 2023-2026) PV054: Modular Generators for Floating VAWTs (EPSRC & SeaTwirl AB, 2023) Media Contributions: Quoted in research media about floating hydrogen production systems (2025)
Xiaodong Yan is an Assistant Professor in the Department of Materials Science and Engineering and an affiliated faculty member in the Department of Electrical and Computer Engineering at the University of Arizona . His research bridges materials science, nanoelectronics, and quantum computing, with a focus on developing novel quantum materials and devices for next-generation computing systems. Education : BS in Physics (Peking University, China), MS in Electrical Engineering (University of Notre Dame), PhD in Electrical and Computer Engineering (University of Southern California). Postdoctoral Training : Materials Science and Engineering, Northwestern University (2021-2023). Dr. Yan’s research explores the synthesis and physics of emerging quantum materials, particularly 2D materials and van der Waals heterostructures , to create advanced devices for neuromorphic computing , quantum sensing , and low-power electronics . His work spans nanofabrication, device characterization, and algorithm integration. His recent publications in Nature and Nature Electronics highlight breakthroughs in Moiré synaptic transistors with room-temperature neuromorphic functionality and reconfigurable heterojunction transistors for machine learning hardware. These studies emphasize 2D material integration , reconfigurable electronics , and bio-mimicking systems . Scientific Awards : MHI Ph.D. Scholar, Ming Hsieh Department of ECE at USC. Dr. Yan leads the Yan Research Group , which focuses on material and device solutions for neuromorphic computing and quantum sensing . The group actively recruits graduate and undergraduate researchers.
John Dalsgaard Sørensen is a Professor and Head of Research Group at the Department of the Built Environment, Aalborg University, within the Faculty of Engineering and Science. He leads the Risk, Resilience, Safety, and Sustainability of Systems Research Group and is affiliated with the Danish Centre for Risk and Safety Management. His research focuses on structural safety, wind turbine reliability, probabilistic design, and risk assessment of infrastructure systems. He has supervised 13 PhD students and contributed to over 600 publications. Key research areas include wind turbine structural integrity, fatigue analysis of offshore and onshore structures, probabilistic design standards (e.g., Eurocodes), and risk-based decision-making for infrastructure. He leads projects like Windscanner (remote sensing for wind measurements) and MANTIS (cyber-physical maintenance systems). Collaborations span academia and industry, addressing challenges in energy systems, civil infrastructure, and safety engineering. His work emphasizes practical applications of advanced modeling techniques, such as Bayesian networks and stochastic simulations, to enhance reliability and reduce operational costs. He is actively involved in standardization efforts for structural design and serves on boards like Energi- og MiljøData Fonden. Recent activities include presenting at international conferences and advising on media debates related to structural safety.
Joachim Oberhammer is a Professor in Microwave and THz Microsystems at KTH Royal Institute of Technology in Stockholm, Sweden. He leads research in radio-frequency/microwave/terahertz micro-electromechanical systems (MEMS) and has held academic roles since 2005. His work includes pioneering advancements in THz communication, sub-THz radar concepts, and MEMS-based components. Oberhammer has been awarded the 2023 Young Engineer Award by the European Microwave Association and holds multiple grants, including an ERC Consolidator Grant (2013) and SSF framework grants (2014–2025). He has authored over 200 peer-reviewed publications and holds four patents in MEMS and THz technology. Education: M.Sc. in Electrical Engineering (Graz University of Technology, 2000), Ph.D. in Microwave Engineering (KTH, 2004). Postdoctoral research at Nanyang Technological University (2004) and Kyoto University (2008). Guest professorships at Universidad Carlos III de Madrid (2019–2020) and NASA-JPL (2014). Research focuses on MEMS fabrication, THz systems integration, and radar technologies. Key projects include the EU-funded M3TERA and Car2TERA projects, and leadership in SSF framework grants for electronics research. He coordinates the EU RIA projects TeraMeasure and TESLA, advancing terahertz applications. Teaching responsibilities include MSc and PhD courses in MEMS engineering, radar systems, and integrated circuits. His lab develops high-performance THz components, including waveguide switches, antennas, and filters, with applications in communication, sensing, and aerospace.
Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.
Irem Boybat is a Researcher in the In-Memory Computing Group at IBM Research - Zurich, Switzerland, focusing on advanced AI hardware solutions. She holds a Ph.D. in Electrical Engineering from EPFL (2020) and prior degrees from EPFL and Sabanci University. Ph.D., Electrical Engineering, EPFL (2020) M.Sc., Electrical Engineering, EPFL (2015) B.Sc., Electronics Engineering, Sabanci University (2013) Her research bridges in-memory computing and AI, targeting energy-efficient hardware for deep learning and neuromorphic systems. Recent work explores analog AI accelerators, heterogeneous architectures, and scalable models for edge computing. Publications highlight cross-disciplinary innovation in materials, circuits, and system design. She has received the IBM Pat Goldberg Memorial Best Paper Award and EPFL PhD Thesis Distinction. Her invited talks span prestigious venues including the European Phase-Change Symposium, IEEE CICC, and HiPEAC. Collaborations include EU H2020 projects like MANIC and WiPLASH.
Mikael Rinne is an Associate Professor in the Department of Civil Engineering at Aalto University's School of Engineering. His research focuses on rock fracture mechanics and its applications in various engineering contexts including nuclear waste repositories, geothermal energy systems, and underground construction. His expertise spans time-dependent rock failure mechanisms, fracture propagation models, and rock mechanics applications in energy storage and disposal systems. His work has direct applications in projects with Posiva Oy (nuclear waste repository), St1 Deepheat (geothermal energy), and mining operations with companies like First Quantum Minerals. Rinne's research integrates advanced numerical modeling with field applications, particularly in Finnish crystalline bedrock conditions. He has contributed significantly to understanding fracture initiation and propagation in rock masses under various stress conditions, with particular emphasis on long-term stability considerations for deep underground structures. His scholarly work demonstrates strong connections between theoretical fracture mechanics and practical engineering applications, with a focus on ensuring safety and reliability in rock engineering projects. His research has evolved from fundamental fracture mechanics studies to application-focused investigations addressing contemporary challenges in energy and waste management. Rinne has supervised doctoral research in rock mechanics and collaborates with researchers specializing in photogrammetry, virtual reality applications, and energy storage systems, creating a multidisciplinary approach to complex rock engineering problems.
