Per-Erik Hellström is a Professor at KTH Royal Institute of Technology, affiliated with the Department of Electronics and Embedded Systems. His research focuses on semiconductor process technology, particularly the heterogeneous integration of materials like SiGe, Ge, high-κ dielectrics, and metal gates with Si CMOS to advance integrated circuits. He leads KTH's FDSOI CMOS process and circuit technology, emphasizing sequential 3D integration for future CMOS developments. Additionally, he manages the Si and SiC process line at Electrum Laboratory, overseeing tool maintenance, process control, and upgrades. Researcher ID: ORCID Location: Kistagangen 16 Email: pereh@kth.se His work involves developing nanometer-sized transistors through double patterning techniques and studying material integration for enhanced device performance. He teaches courses in electrical circuits, semiconductor devices, and nanotechnology at both Bachelor's and Master's levels, including Electrical Engineering (IF1330) , Embedded Electronics (IE1206) , and Introduction to Integrated Circuits (IL2241) . He also supervises degree projects and exams. Scientific achievements include the 2020 G03 Best Paper Award for gate stack research. His recent publications highlight advancements in Type-II superlattices, 3D integration, and high-temperature sensors. Key collaborators include PhD students working on nanotechnology and process engineering.
Professor Geraint Jewell is affiliated with the University of Sheffield , serving as Director of the Rolls-Royce University Technology Centre in Advanced Electrical Machines (since 2006) and Director of the EPSRC Future Electrical Machines Manufacturing Hub (since 2019). He is a graduate of the university (BEng 1988, PhD 1992) and has held academic roles since 1994. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship at Rolls-Royce (2006-2008) Former Faculty Director of Research and Innovation (2008-2011) Former Head of Department (2013-2019) His research focuses on power-dense electrical machines for aerospace applications , including permanent magnet machines , switched reluctance machines , and linear actuators . He has supervised ~20 PhD students and led collaborations with Rolls-Royce on high-temperature devices (up to 800°C) and aero-engine starter-generators. Recent publications analyze stator insulation thermal degradation , eddy current control in additively manufactured materials , and magnetic loss prediction in silicon steel. His work spans electromagnetic modeling , core loss calculation , and advanced manufacturing techniques for electrical machines. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship (2006-2008) He has advised PhD students across topics like consequent-pole PM machines , doubly salient SynRMs , and core loss characterization . His Electrical Machines and Drives Research Group explores modular motor design and magnetic material optimization for aerospace and electric vehicles.
Professor Ben Horan is the Head of School of Engineering at Deakin University , Faculty of Science Engineering and Built Environment. He holds a Doctor of Philosophy and Bachelor of Engineering from Deakin University, with expertise in electrical engineering , control systems , and human-centred computing . As a leading researcher in virtual reality (VR) applications, he focuses on safety training, aged care, and extended reality (XR) systems. PhD in Electrical Engineering (Deakin University) Graduate Certificate of Higher Education (Deakin University) Bachelor of Engineering (Deakin University) His research spans VR for electrical safety training , automated vehicle interactions , and XR applications in museums . His work includes grants from the Department of Health, Melbourne Water Corporation, and City of Greater Bendigo. Recent publications analyze 360° video realism, cognitive load in virtual workplaces, and AR for Industry 5.0. Professor Horan supervises PhD candidates exploring topics like autonomous vehicle pedestrian interactions , VR stress mitigation , and industrial XR systems . He has completed supervision of 10+ PhD and Master’s students.
David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.
Iraklis Lazakis is a Reader in Maritime Operations and Maintenance at the Department of Naval Architecture, Ocean and Marine Engineering (NAOME), within the Faculty of Engineering at the University of Strathclyde. He joined the university as a PhD researcher in 2007 and began his academic career in 2011, establishing himself as a key figure in maritime systems research and education. His research interests span a broad range of topics including ship operations, systems maintenance and reliability, condition monitoring, risk and asset management, shipyard productivity, and offshore renewable energy systems (wind, wave, and tidal). His work bridges academic theory with industrial application, drawing from his 8 years of prior industry experience in maritime surveys, accident investigations, and ship repairs. The trends in his recent publications reflect a strong focus on data-driven and digital solutions for sustainable maritime operations. Key themes include the development of simulation and optimization tools, application of virtual reality for safety, cost reduction in offshore wind O&M, and decarbonization strategies such as onboard CO2 capture. His work increasingly integrates AI, digital twins, and human factors to enhance system performance and crew wellbeing. He has received numerous accolades, including: SNAME Faculty Advisor of the Year (2024) SNAME WES Best Paper Award (2023) Multiple Knowledge Transfer Partnerships Certificates of Excellence (2020, 2022) Laureate of the Franz Edelman Award (2012) ISSC Committee IV.2 Membership (2012–2015) Lazakis actively supervises undergraduate, postgraduate, and PhD students, and leads or contributes to a wide portfolio of research and knowledge exchange projects. His recent projects include decarbonizing UK shipping, structural surveys of vessels like Calmac and the Royal Yacht Britannia, and development of low-cost underwater gliders. He plays a strategic role in supporting colleagues with funding applications, publications, and industry collaboration. His work contributes to UN Sustainable Development Goals related to sustainable energy and industry innovation.
Dr. Sönke Knoch is a researcher affiliated with the Ubiquitous Media Technology Lab (UMTL) at the Saarland Informatics Campus and the German Research Center for Artificial Intelligence (DFKI) GmbH . His work focuses on Human-Computer Interaction , Activity Recognition , Process Mining , and Industry 4.0 technologies. Current Affiliation: DFKI GmbH (Saarland Informatics Campus) Academic Role: Researcher Research Interests span digital twins, augmented reality in manufacturing, and safety-critical systems. He leads projects like RZzKI (AI and Digital Transformation) and BaSySafe (risk assessment via management shells). His work addresses zero-defect manufacturing and cognitive support for impaired workers . Recent Publications focus on digital twins for industrial safety, AR-based task adaptation , and AI quality management in smart factories. Key themes include human-centric AI , real-time process conformance , and context-aware systems . Leadership includes contributing to the WALL-ET project for autonomous logistics and co-developing the PARTAS system for cognitively impaired workers.
Philip Brunner is a Professor of Hydrogeology at the University of Neuchâtel's Faculty of Science since 2012. He is based at the Center for Hydrogeology and Geothermics (CHYN), leading the Laboratory of Hydrogeological Processes. His work centers on sustainable water resource management through quantitative tools. He earned his PhD from ETH Zurich, focusing on sustainable salt and water management in Western China's agricultural basins. Post-PhD, he conducted three years of postdoctoral research in Australia, developing new approaches for simulating river-aquifer interactions. Brunner's research spans surface water-groundwater interactions, numerical modeling, and remote sensing. He integrates methods from numerical modeling, remote sensing, scientific computing, and isotopic chemistry. His interdisciplinary collaborations with mathematicians, biologists, and physicists address challenges in agriculture, ecohydrology, engineering, and sustainable resource management. Recent publications highlight innovative tracer techniques (noble gases, microbes), low-cost monitoring systems, and advanced numerical models. His work tackles climate change impacts on ecosystems, groundwater in conflict zones, and sustainable practices in diverse environments including mountains and agricultural regions. He teaches courses such as Introduction to Hydrological Processes (Master), Numerical Modeling (Master), Remote Sensing (Master), and Introduction to Soil Physics (Bachelor, in French). His laboratory serves as a center for experimental and computational hydrogeological research.
Matthew A. Franchek is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston, where he has served since 2002. His career spans over three decades, including prior roles as Professor and Chair at the University of Houston (2002–2009), Director of the Biomedical Engineering Program (2002–2009), and faculty positions at Purdue University from 1992 to 2002. He earned his Ph.D. (1991), M.S. (1988), and B.S. (1987) in Mechanical Engineering from Texas A&M University and the University of Texas at Arlington, respectively. Dr. Franchek’s research focuses on Dynamic Systems, Measurement and Control , with expertise in linear/nonlinear system identification, multivariable control theory, diagnostics/prognostics, and adaptive control. His engineering applications span internal combustion engines , exhaust after-treatment , noise/vibration control , and health prognostics for cardiovascular/respiratory systems . His recent publications highlight applications in superconductor manufacturing, aeroelastic stability, magnetic actuators, and subsea engineering. 2002 Best Paper Award, ASME Journal of Dynamic Systems, Measurement and Control 2001 ASME Dynamic Systems and Control Division Young Investigator Award 1997 CASA/SME University Lead Award 1997 Feddersen Faculty Fellow, Purdue University Multiple teaching awards at Purdue University (1994–2001) and Texas A&M University (1991) He has served as an Associate Editor for the ASME Journal of Dynamic Systems, Measurement and Control, held leadership roles in ASME and IEEE, and organized symposia on nonlinear control and robust control at international conferences. His professional activities include advisory roles at Cummins Incorporated and reviewing for NSF and numerous journals.
Annick Hubin is a Professor in the Department of Sustainable Materials Engineering at the Faculty of Engineering, Vrije Universiteit Brussel. She serves in additional leadership roles including R&D Central management and as Head of a Research Group. Her work focuses on electrochemical processes with applications in materials engineering, corrosion science, and sustainable technologies. Her research interests span electrochemical kinetics, thermodynamics of aqueous solutions, electrode processes, electroreduction of metals and alloys (plating, extraction, refining, recycling), and environmental electrochemistry. She specializes in investigating basic electrochemical reactions using techniques such as potentiometric titrations, voltammetry, chronoamperometry, chronopotentiometry, and impedance measurements. Her work also examines mass transport in electrochemical processes and the action of organic inhibitors for metal deposition or dissolution reactions. Her recent publications reveal a strong focus on corrosion science, battery technologies, and electrochemical materials. There is a clear trend toward applying advanced characterization techniques and machine learning to solve complex problems in electrochemistry and materials science. Her research increasingly addresses sustainability challenges, particularly in battery technology and low-carbon solutions. Professor Hubin actively supervises doctoral students and participates in numerous research projects, demonstrating her commitment to mentoring the next generation of scientists and engineers. She has secured substantial research funding for projects spanning fundamental and applied research in materials engineering. She leads or participates in several significant research initiatives including DESTINY (Low-carbon solutions network), fundamental research on sulfide-based all-solid-state batteries, and projects focused on atmospheric corrosion prediction using machine learning. Her laboratory appears to specialize in electrochemical characterization and materials development for energy applications.
Prof. Thomas H. Kolbe serves as Chair of Geoinformatics at the Technical University of Munich (TUM), where he leads research in spatial, temporal, and semantic modeling of urban environments. His work focuses on developing foundational frameworks for 3D/4D city models, digital twins, and smart city applications through international standardization efforts including CityGML and IndoorGML. His research spans virtual city modeling, urban system simulation, and GIS integration with emerging technologies. Current projects emphasize AI-driven urban scenario generation, semantic streetspace modeling, and IoT integration in digital twin ecosystems. Recent publications demonstrate strong interdisciplinary connections between computer vision, urban planning, and geospatial data science, with particular emphasis on practical implementations of 3D city models for sustainability challenges. Prof. Kolbe actively contributes to professional organizations including the Round Table GIS eV (as Chairman since 2013) and the Munich Data Science Institute (as core member since 2021). His leadership extends to the Leonhard Obermeyer Center for digital methods in the built environment and the Hans Eisenmann Forum for agricultural sciences. His work bridges theoretical geoinformatics with practical urban applications through numerous collaborative projects with municipal governments and industry partners.
Karim Ismail is a Professor at the Department of Civil and Environmental Engineering, Carleton University. His research focuses on sustainable transportation modeling, road safety analysis, intelligent transportation systems, and computer vision applications for traffic data collection. Specializes in non-motorized transportation , including pedestrian and cyclist behavior. Develops probabilistic highway design standards using reliability and risk analysis. Pioneers vision-based safety evaluation techniques and traffic conflict modeling. His recent publications explore automated analysis tools for roundabout traffic, deep learning applications for proximity detection, and wireless sensor frameworks for collision avoidance. Notable accolades include the Michel Van Aerde Award (2025) and multiple Transportation Research Board honors. Supervised graduate students: Al-Haideri, Rulla (Ph.D. 2025) Mohammadi, Shahriar (Ph.D. 2022) Kassim, Ali (Ph.D. 2014)
Igor Wojnicki is a Professor at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, where he serves as Vice-Dean of the Faculty of Cooperation and Education. His primary affiliation is with the Department of Applied Informatics, where he maintains an active research laboratory focused on knowledge engineering and smart systems. His research spans multiple domains with evolving focus: Early career: Deductive databases and rule-based inference engines (PhD thesis on "A Rule-based Inference Engine Extending Knowledge Processing Capabilities of Relational Database Management Systems") Mid-career: Graph-based knowledge representation and Tabular Trees (XTT predecessor) Current focus: Smart city applications, particularly energy-efficient lighting control systems and graph-based urban data integration His recent publications demonstrate a clear trajectory toward applied urban computing, with over 15 significant papers in the last five years addressing smart city infrastructure optimization. Key themes include dynamic street lighting control, graph-based computational methods for urban environments, and energy conservation in public infrastructure. Wojnicki actively contributes to academic-practical collaboration through initiatives like the Green AGH Campus Project and IBM academic partnerships. His technical leadership includes development of the ReDaReS system for relational database knowledge processing and the Jelly View technology for advanced database queries. His laboratory maintains strong industry connections, particularly with IBM through student internship programs and technology transfer initiatives. The team produces both theoretical frameworks and practical implementations, with notable outputs including the Osiris GUI system and Magellan GPS software for Poland.
Bhushan Gopaluni is a Professor in the Department of Chemical and Biological Engineering at the University of British Columbia, where he also serves as Associate Dean for Education and Professional Development in the Faculty of Applied Science. He holds associate faculty positions in multiple interdisciplinary institutes including the Institute of Applied Mathematics, Institute for Computing, Information and Cognitive Systems, Pulp and Paper Center, and Clean Energy Research Center. He previously held the Elizabeth and Leslie Gould Teaching Professorship from 2014 to 2017. Education: Ph.D. in Chemical Engineering, University of Alberta (2003) Bachelor of Technology in Chemical Engineering, Indian Institute of Technology, Madras (1997) Research Interests: Professor Gopaluni's research spans several critical areas at the intersection of chemical engineering, machine learning, and process control. His primary focus includes the development of advanced process control strategies using reinforcement learning and machine learning techniques. He has made significant contributions to battery technology research, particularly in capacity estimation and remaining useful life prediction for lithium-ion batteries. His work also encompasses sustainable energy systems, industrial process monitoring, fault diagnosis, and the application of digital twin technology in chemical processes. His research methodology emphasizes the integration of data-driven approaches with fundamental process understanding, leading to practical solutions for complex industrial challenges. This includes the development of interpretable machine learning models for industrial applications, real-time optimization strategies, and advanced monitoring systems for process industries. Publications and Research Impact: Professor Gopaluni's recent publications demonstrate a strong focus on cutting-edge applications of machine learning in chemical engineering. His work prominently features battery technology and energy systems, with multiple papers addressing lithium-ion battery capacity estimation and management. He has also contributed significantly to process control applications, including drilling process monitoring, greenhouse gas reduction in marine transport, and renewable carbon tracking in biofuel processing. His research extends to advanced computational methods including deep learning, reinforcement learning, and causal discovery in industrial processes. Awards and Recognition: Killam Teaching Prize (University of British Columbia) Dean's Service Medal (University of British Columbia) D.G. Fisher Award in Process Control (Canadian Society for Chemical Engineers) Elizabeth and Leslie Gould Teaching Professor (2014-2017) Professional Service and Editorial Roles: Professor Gopaluni currently serves as Associate Editor for three prestigious journals: Journal of Process Control, The Journal of Franklin Institute, and Results in Control and Optimization. His service to the academic community extends through his role as Associate Dean for Education and Professional Development, where he oversees educational initiatives across the Faculty of Applied Science. Industry Experience: From 2003 to 2005, Professor Gopaluni worked as an engineering consultant at Matrikon Inc. (now Honeywell Process Solutions), where he designed and commissioned multivariable controllers for British Columbia's pulp and paper industry and implemented controller performance monitoring projects across oil & gas and chemical industries.
Andreas Jung is an Associate Professor of Physics and Astronomy at Purdue University, affiliated with the CMS experiment at CERN. His research focuses on understanding the electroweak scale stabilization via precision measurements of top quark interactions, Higgs boson studies, and detector R&D. He also explores quantum algorithms for high-energy physics and supply chain optimization. Jung earned his Ph.D. from the University of Heidelberg (2009) and a diploma from the University of Dortmund (2004). Education: Ph.D. in Physics, University of Heidelberg, 2009 (Dissertation: D* Meson Cross Section Measurement) Diploma in Physics, University of Dortmund, 2004 (Commissioning of H1 Fast Track Trigger) Research Interests: High Energy Physics, Particle Physics, Detector Development, Quantum Computing Applications, Material Science for Detectors, and Collider Experiments. His work includes analyzing top quark spin correlations, quantum annealing for vertex reconstruction, and carbon fiber composites for CMS upgrades. Awards: Senior Distinguished Researcher fellowship at Fermilab LHC Physics Center (2019) 3-year PhD scholarship from German Research Society (2004–2007) Teaching & Leadership: Teaches courses on particle physics and data science. Serves as Convener of CMS TOP Physics Analysis Group and leads detector mechanics R&D. Engages in quantum computing collaborations with DoD and industry partners. Labs/Teams: Jung Research Group at Purdue, CMS Collaboration, and Purdue Quantum Science & Engineering Institute (PQSEI). Active in detector development for the High-Luminosity LHC upgrade, including carbon fiber support structures and silicon pixel detectors.
Prof. Michele Germani is a Full Professor at the Polytechnic University of Marche, Department of Industrial Engineering and Mathematical Sciences. His research focuses on ergonomics, sustainability, and human-centered design, with particular emphasis on industrial automation, wearable technologies, and circular economy principles. He leads projects on ergonomic risk assessment, environmental impact modeling, and smart manufacturing systems. Academic Background: Holds a position in the Faculty of Engineering, contributing to both teaching and research. His work integrates machine learning, sensor-based systems, and virtual reality to address challenges in manufacturing, healthcare, and sustainable design. Research Interests: Prof. Germani explores topics such as design for disassembly, eco-design tools, and the application of advanced technologies (e.g., exoskeletons, cobots) to enhance workplace safety and efficiency. His studies often bridge engineering and environmental science, aiming to reduce carbon footprints through innovative design methodologies. Key Projects: Development of decision support systems for fall risk detection, sensor-based ergonomic evaluation tools, and frameworks for circular economy implementation. He also investigates the impact of Industry 4.0 technologies on workforce dynamics and operator training via mixed reality simulations. Grants & Collaborations: Engaged in interdisciplinary collaborations focusing on sustainable manufacturing, healthcare technology, and smart systems. His research has led to tools like Durabot for durability analysis and Greenbuild for green building design. Labs & Teams: Oversees labs developing eco-design strategies, human-robot collaboration systems, and ergonomic assessment protocols, with a focus on real-world industrial applications.
