Jaime Campos is an Associate Professor in the Department of Informatics at Linnaeus University, Sweden. He coordinates the Information Systems Master's program and leads research projects bridging ICT with industrial maintenance and eHealth. His doctoral work contributed to the EU-funded DYNAMITE project on dynamic maintenance decisions, involving collaboration with Finland's Technical Research Center (VTT). His research focuses on digital transformation , decision support systems , and human-centric solutions across domains. Key interests include: Industrial applications of big data analytics IoT-enabled preventive maintenance eHealth technologies for elderly care Sustainable infrastructure management Recent publications emphasize Industry 4.0/5.0 transformation, AI in healthcare, predictive maintenance models, and gravel road maintenance optimization. His work consistently integrates emerging technologies like cloud computing, MLOps, and open-source frameworks to address real-world challenges. Campos directs projects including: FRONT-VL : IoT solutions for aging populations HUG : Sustainable gravel road maintenance Data-driven gravel road assessment (ongoing doctoral project)
Foteini Liwicki is an Associate Professor at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, leading the Embedded Intelligent Systems LAB and the Machine Learning Focus Group – Brain Analysis since 2022. Her work bridges Artificial Intelligence and Neuroscience with applications in communication disorders, neurodegenerative conditions, and mental health. Dr. Liwicki's research focuses on multimodal brain analysis, particularly inner speech mechanisms, neurodegenerative disorders like dementia, neurodevelopmental conditions such as ADHD, and the therapeutic effects of singing on mental health. She develops computational methods for EEG-fMRI integration and interpretable machine learning approaches to understand human communication across diverse populations. Her recent publications demonstrate a strong interdisciplinary approach, combining machine learning with neuroscience, geology, and educational technology. The research trends show increasing focus on multimodal data fusion, brain-computer interfaces, and practical applications of AI in healthcare and resource management. Scientific Awards: 2023-2025: Kompetensutveckling till professor, dnr LTU-154-2023 2022: Grants for Excellent Research Projects Proposals of SRT.ai 2022 2020-2021: Ansökan juniora lovande forskare, dnr LTU-4449-2019 Dr. Liwicki actively supervises multiple PhD and Master's students across diverse research areas including brain signal analysis, inner speech detection, geological data analysis, and AI applications in healthcare. She has received significant research funding including Kempestiftelserna grants for projects on inner speech, ADHD prediction, and singing therapy for psychiatric disorders. She leads the Machine Learning Focus Group – Brain Analysis, which develops computational methods for multimodal brain analysis, and collaborates extensively with international institutions including the University of Nantes, Kyushu University, and various European research centers.
Reza Khoshkangini is an Associate Professor at Malmö University, Faculty of Technology and Society, Department of Computer Science and Media Technology. He is affiliated with the Sustainable Digitalisation Research Centre (SDRC) and his research spans across multiple domains of artificial intelligence and computer science. His research interests focus on Machine Learning, Artificial Intelligence, Federated Learning, and Computer Vision applications. Dr. Khoshkangini's work demonstrates a strong interdisciplinary approach, applying AI techniques to healthcare, financial systems, and digital sustainability. His research bridges theoretical computer science with practical applications in real-world domains. Analysis of his recent publications reveals a strong trend toward multimodal AI systems that integrate different data sources (visual, audio, sensor data) and address privacy concerns through federated learning approaches. His work shows significant application focus in healthcare technology (particularly patient monitoring and reproductive medicine) and financial forecasting, while maintaining strong theoretical foundations in computer vision and machine learning. Dr. Khoshkangini is actively involved in several research projects including EIVF-AI (Enhancing in vitro fertilization with environmental optimization using AI), Developing a Framework for Leveraging Knowledge Graph & Deep Learning in Mobility Transportation, and Intelligent and Trustworthy IoT Systems. These projects reflect his commitment to applying advanced AI techniques to solve practical problems across multiple domains. He is a member of the Sustainable Digitalisation Research Centre, which studies both social and technological aspects to promote sustainable digitalisation. His work contributes to the center's mission of examining how digital technologies can be developed and implemented in ways that support sustainable societal development.
Bengt Jacobson is a Professor and group leader of the Vehicle Dynamics group at Chalmers University of Technology (Department of Vehicle Engineering and Autonomous Systems). He earned his PhD in Machine Elements from Chalmers in 1993 and was appointed Associate Professor in 1998. His industrial experience includes serving as Technical Expert at Volvo Car Corporation (2001-2010) focusing on active safety and vehicle dynamics. Jacobson holds board positions at the Swedish Vehicular Engineering Association (since 2012) and Heavy Vehicle Transport and Technology (since 2023). His research focuses on vehicle dynamics in the ground plane for passenger cars and heavy trucks, including powertrain systems, hybrid vehicles, active safety controls, tire modeling, and aerodynamic interactions. Recent work explores trajectory control, stability algorithms, energy efficiency, and human-vehicle interactions under dynamic conditions. Jacobson's 200+ publications consistently address vehicle dynamics optimization, with recent trends in MPC control validation, safety envelope definition for articulated vehicles, crosswind stability, and high-fidelity tire modeling. His group develops simulation tools like OpenPBS for assessing high-capacity transports. He leads 44+ projects involving industry partnerships (e.g., Volvo) and supervises research on electric propulsion systems, automated driving, and performance-based standards. The Vehicle Dynamics group collaborates extensively with automotive OEMs and infrastructure authorities.
Martin Jakobsson is an Associate Professor at KTH Royal Institute of Technology, working within the Division of Health Informatics and Logistics in the School of Engineering Sciences in Chemistry, Biotechnology and Health. He serves as a faculty member of Digital Futures and sits on the board of the KTH Center for Sports Engineering. His research focuses on robust network protocols and ICT solutions for wireless networks, WSNs, wearables, and Internet of Things applications in health, wellbeing, and sports domains. His work spans wireless sensor networks, machine learning applications in healthcare, and innovative technologies for sports performance analysis. Analysis of his recent publications (2018-2025) reveals a strong research trajectory in applying machine learning to physiological monitoring, particularly in intraoperative hypotension prediction, trauma care improvement, and wearable health monitoring systems. His work increasingly integrates drone technology for motion capture in sports performance analysis and explores LPWAN applications for health monitoring. Current projects include collaborations with Karolinska University Hospital on AI for hypotension prediction, trauma care improvement, and lifestyle intervention apps VINNOVA-sponsored projects focusing on signal processing and machine learning for surgical patients Development of the 'My Digital Drone Twin' system for sports performance analysis LPWAN applications for wearable sensor systems, including smart shoe technology As an educator, he teaches multiple courses including Communication Systems, Mobile Communications and Wireless Networks, and Network Security, demonstrating his commitment to training the next generation of engineers in wireless technologies and network security.
Jonny Nordström is a Researcher at Uppsala University's Department of Surgical Sciences, with additional affiliation at the Centre for Research & Development in Gävleborg. His primary research focus is in advanced cardiac imaging methodologies, particularly quantitative positron emission tomography (PET) using O-15-water tracers for assessing cardiac function and pathology. Research interests include: Quantitative accuracy in cardiac PET imaging Motion correction and image reconstruction techniques Validation of PET against cardiac MRI Novel applications for diagnosing valvular and ischemic heart disease Radiation optimization in medical imaging His publications demonstrate consistent focus on improving cardiac PET methodologies, with recent work emphasizing motion artifact correction, hybrid PET/MRI validation, and clinical applications for conditions like mitral regurgitation and ventricular hypertrophy. The research shows increasing sophistication in quantitative analysis techniques over time. No scientific awards or student advising relationships are mentioned in the available information.
Andreas Fhager is an Associate Professor in Biomedical Electromagnetics at Chalmers University of Technology, where he leads the research group of the same name. His work focuses on developing microwave-based imaging diagnostics for breast cancer, stroke, and other biomedical applications, encompassing system design, signal processing, electromagnetic modeling, and optimization. He co-founded Medfield Diagnostics AB to commercialize microwave diagnostic equipment, demonstrating strong translational research capabilities. His research centers on biomedical electromagnetics with emphasis on microwave imaging for medical diagnostics. Key areas include breast cancer detection through tomographic systems, stroke diagnosis using ultra-wideband technology, and traumatic injury monitoring via wearable devices. He develops advanced electromagnetic models, optimization algorithms, and signal processing techniques to improve diagnostic accuracy while reducing hardware complexity. His work bridges theoretical innovation with practical clinical applications, particularly targeting prehospital care settings where rapid diagnosis is critical. Analysis of his recent publications (2021-2025) reveals three dominant trends: hardware simplification (reducing transmission channels, frequency points, and system components), noise/multipath mitigation (using lossy gels, dielectric antennas, and asymmetry detection), and clinical translation (wearable abdominal injury monitors, stroke triage tools, and muscle rupture diagnostics). His research increasingly focuses on real-world implementation, with studies using porcine models and phantom testing to validate systems for emergency medical applications. No scientific awards were mentioned in the provided text. Fhager co-founded Medfield Diagnostics AB, indicating active engagement in research commercialization and likely related grant acquisition. While specific grants aren't detailed, his leadership of a research group and extensive publication record suggest successful funding from sources like the Swedish Research Council or EU programs. He teaches Electromagnetic Field Theory, Medical Signals and Systems, and Diagnostic Imaging, contributing to academic training in biomedical engineering. His entrepreneurial activity demonstrates effective translation of academic research into medical technology solutions. He leads the Biomedical Electromagnetics research group at Chalmers University of Technology, which develops end-to-end microwave diagnostic systems from electromagnetic modeling to prototype validation. The group's work includes antenna design (e.g., dielectric rod antennas), computational methods (e.g., discrete dipole approximation), and clinical testing (e.g., porcine models for abdominal injuries). Current projects focus on wearable prehospital diagnostics and stroke triage tools, with future directions likely expanding into point-of-care applications and integration with AI-driven analysis.
Robert Braun is an Associate Professor and Docent at Linköping University, affiliated with the Department of Management and Engineering (IEI) within the Fluid and Mechatronic Systems (FLUMES) division. His research focuses on distributed simulation methods, including co-simulation, parallel computing, and simulation-based optimization. He holds a doctoral degree from Linköping University, defended in 2015, and his employment is shared with SICS, East Swedish ICT, and SKF. He teaches undergraduate courses in mechatronics and hydraulics and is a core developer of Hopsan, an open-source simulation tool. Education: Doctoral thesis in 2015 on 'Distributed system simulation methods: For Model-Based Product Development'. Research Interests: Co-simulation tool coupling, parallel simulation optimization, and simulation-based teaching. Active in projects like OpenCPS (EU-funded) for model-driven development. Publications: Recent works address transmission line modeling in co-simulation, morphing wing actuator systems, and interoperability in aircraft system development. His research emphasizes numerical robustness and scalability in multi-domain simulations. Teaching & Labs: Develops Hopsan for fluid and mechatronic systems simulation. Involved in organizing the Scandinavian International Conference on Fluid Power (SICFP’25).
Niklas Rönnberg is an Associate Professor and Senior Lecturer in Sound Technology at Linköping University's Department of Science and Technology (ITN), part of the Institute of Technology. His research focuses on sonification as a complementary modality in interdisciplinary applications, including information visualization, interaction design, and cognitive psychology. He teaches courses in sound technology, research methodology, and digital media production for Media Technology, AI and Engineering, and Graphic Design & Communication programs. His academic journey includes a Master's in Communication Science and a PhD in Technical Audiology (2014). He has led projects like 'Sound of Art,' where sonification transformed Nordic artworks into musical experiences. His work explores how musical elements enhance data understanding and interaction in fields like process control and decision support. Rönnberg actively promotes sonification's societal impact through conferences like the International Conference on Auditory Display. Research affiliations include Media and Information Technology (MIT) and Visualization and Interaction Design (VID) groups. He has contributed to over 30 peer-reviewed publications, focusing on sonification's role in urban planning, AI education, and multimodal interfaces. His current interests emphasize making sonification accessible beyond academia, particularly in public spaces and creative industries.
Robin Teigland is a Professor of Strategy and Management of Digitalization at Chalmers University of Technology, part of the Entrepreneurship and Strategy Division within the Department of Technology Management and Economics. She holds a PhD from the Stockholm School of Economics (SSE) and has held prior roles as a Professor of Business Administration at SSE. Her research focuses on the intersection of strategy, technology, innovation, and entrepreneurship, with emphasis on digital innovation networks, disruptive technologies (e.g., AI, blockchain), and circular economy initiatives. She currently leads projects such as SuRF-LSAM (microfactories for circular economy) and chairs the Mistra-funded C2B2 program. Robin is also an impact entrepreneur, developing blue circular economies through startups like Circular Ocean LDA and Ekbacken Studios AB. Education: B.A. in Economics, Stanford University MBA, Wharton School MA in International Studies, University of Pennsylvania PhD in Business Administration, Stockholm School of Economics Research Interests: Her work explores digital transformation, circular economy transitions, and the societal impacts of technologies like blockchain and AI. Key areas include: Open source communities and entrepreneurial ecosystems Disruptive technologies' role in labor markets and firm structures Circular economy microfactories and ocean-based sustainability Professional Roles: Fellow of the Royal Swedish Academy of Engineering Sciences (IVA) Member of Sweden's Productivity Commission Former board member of Luftfartsverket AB and Akademiska Hus AB Awards & Recognition: Ranked among Sweden’s top 100 public speakers (2019) Listed as one of Sweden’s most influential women in technology by Veckans Affärer Projects & Grants: Robin directs initiatives funded by Vinnova, Formas, Mistra, and others, including the Ocean Data Factory and 4boards.ai. Her work bridges academia, industry, and public sectors to address challenges like sustainable manufacturing and ocean data governance. Impact Entrepreneurship: Beyond academia, she co-founded startups focused on circular economy solutions in Portugal and Sweden, leveraging her diverse background in venture capital, consulting, and international business.
Per Stenstrom is a Professor of Computer Engineering at Chalmers University of Technology, Sweden since 1995. His research focuses on computer architecture, particularly high-performance memory systems and energy-efficient computing. He has authored/co-authored four textbooks, over 200 publications, 20 patents, and supervised approximately 25 PhD students. Awarded ACM Fellow and IEEE Fellow. Member of Academia Europaea and the Royal Swedish Academy of Engineering Sciences. Co-founder of the HiPEAC Network of Excellence. Served as Editor or Associate Editor for journals like ACM Transactions on Architecture and Code Optimization and IEEE Transactions on Computers . Program Chair for major conferences including ISCA, HPCA, IPDPS, and ACM International Conference on Supercomputing. His research innovations include cache compression techniques, memory optimization strategies, and energy-aware resource management in multicore systems. Ongoing work emphasizes secure and scalable cache partitioning, hybrid memory systems, and defense mechanisms against microarchitectural attacks.
Nutapong Somjit holds dual academic roles as an Associate Professor in the School of Electronic and Electrical Engineering at the University of Leeds and an adjunct faculty member in the Micro and Nanosystems Department at KTH Royal Institute of Technology, Sweden. His career includes a research leadership position at TU Dresden and a Doctoral Research Award from IEEE in 2012. Specializing in high-frequency components and sustainable microsystems, his work emphasizes innovative fabrication techniques and MEMS integration. He has received over a dozen awards, including the 2009 EuMIC Best Paper Award and editorial roles in IET Electronics Letters. Education: MSc (Dresden University of Technology, 2005), PhD (KTH, 2012) Awards: 6 major honors including IEEE fellowships and chair positions at international conferences Research focuses on next-gen RF systems, with 15+ peer-reviewed articles since 2006. Key innovations include 3D-printed antennas, cost-effective MEMS phase shifters, and high-aspect-ratio TSV fabrication using magnetic assembly techniques. His work bridges microfabrication scalability with practical high-frequency applications. Grants: Not explicitly stated in provided text Labs: Leads research teams at both Leeds and KTH focusing on millimeter-wave and nanoscale systems
Johan Netz is an Assistant Professor at Karlstad University, focusing on innovation management, idea screening methodologies, and the impact of emerging technologies like AI on organizational structures. His work bridges academic research with practical applications in service innovation and agile methodologies. Primary research interests include optimizing idea evaluation processes, understanding expert decision-making patterns, and analyzing how technological advancements reshape innovation ecosystems. He frequently collaborates with colleagues such as Alexandre Sukhov, Peter Magnusson, and Filip Högberg. Recent work explores AI's disruptive potential in service ecosystems and the balance between organizational stability and change in agile environments. Despite prolific publishing since 2012, no awards or grants are explicitly mentioned in the provided text. Notable contributions include frameworks for improving idea screening through holistic approaches and leveraging frontline employees for innovation input. His research often emphasizes practical tools and methodologies for service innovation and decision-making optimization.
Mohammad Rajiullah is a Senior Lecturer in Computer Science at Karlstad University, Sweden. His research focuses on low latency networking, 5G/6G systems, satellite internet integration, and IoT optimization. He leads major EU initiatives including the MAGDALENA (6G-SANDBOX) project (Principal Investigator) evaluating LEO satellite-5G integration, and the XR health training use case in the 6G-PATH project for immersive medical education. Currently PI of the EU IMAGINE-B5G SIMONE project developing AR/VR platforms over 5G/6G. Active in national projects like DRIVE (8-year initiative for latency-sensitive mobile services) and international collaborations such as the 5G-DiGITs curriculum development. His work spans over 50 peer-reviewed publications, with recent focus on Starlink performance analysis, NB-IoT optimization, and 5G non-standalone network evaluations. He maintains the CARL-W testbed for hybrid 5G/satellite testing. Teaching includes core courses in Operating Systems, Computer Networking, and IoT. His research emphasizes cross-disciplinary projects involving academia-industry partnerships across Europe and Asia. He has contributed to EU deliverables like 6G-SANDBOX D6.3 and NEAT D3.1 , showcasing leadership in collaborative network research.
Dr. Renaud Detry is Associate Professor of Robot Learning at KU Leuven with dual appointments in Electrical and Mechanical Engineering. His research develops learning algorithms for robotic perception and manipulation in challenging environments including construction, healthcare, and space operations. Current projects address uncertainty-aware learning systems, robotic sand grading, multi-task policy training, and trajectory prediction for shared control. Applications range from terrestrial construction automation to NASA's Mars Sample Return mission, where he served as machine-vision lead. As associate editor for IEEE Transactions on Robotics and conference organizer for ICRA/IROS, he advances robotic learning methodologies. Received best presentation award at ECCV 2022 for spacecraft pose estimation research.
