Maurice Lamb is a researcher at the University of Skövde , affiliated with the Interaction Lab (ILAB) and the School of Informatics . His work bridges digital human modeling (DHM) , virtual reality , and human-robot interaction (HRI) with applications in automotive design and cognitive science . Research interests include: Automated design optimization using simulation-based multi-objective methods Collaborative tools for remote design reviews and CAD integration Cognitive implications of inverse kinematics solvers (FABRIK) in motor planning Impact of XR technologies on motor skill learning and human-agent coordination Methodological challenges in NARS (Negative Attitude toward Robots Scale) application Key projects include PLENUM (Vinnova-funded) and OKAVIM (AFA Insurance-funded), focusing on remote collaboration , ergonomics , and cognitive support in VR/XR . His publications span IEEE , Springer , and Elsevier journals, often co-authored with experts like Francisco Garcia Rivera, Erik Billing, and Dan Högberg.
Bandaru Sunith is an Associate Professor at the University of Skövde's School of Engineering Science, with previous affiliation to The Virtual Systems Research Centre (closed May 2017). His research spans multi-objective optimization, digital twins, and knowledge-driven decision support systems for manufacturing applications. He actively leads the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) research profile and contributes to the ADOPTIVE project focused on vehicle ergonomics optimization. Dr. Sunith's research interests include: Multi-objective optimization and evolutionary algorithms Digital twin frameworks for manufacturing systems Knowledge discovery and visualization for decision support Anomaly detection in industrial processes Factory layout optimization integrating human well-being metrics His recent publications (2023-2025) demonstrate a clear trend toward integrating advanced AI techniques with traditional optimization methods. The research increasingly focuses on practical industrial applications, particularly in automotive manufacturing, with strong emphasis on translating theoretical advances into usable tools. His work bridges the gap between computational optimization and real-world manufacturing challenges, often incorporating human factors considerations. Dr. Sunith has secured significant research funding from Swedish innovation agencies: Virtual Factories with Knowledge-Driven Optimization (VF-KDO) funded by Knowledge Foundation (Grant 2018-0011) Integrated Manufacturing Analytics Platform for Predictive Maintenance (IMAP) funded by Vinnova (Grant 2021-02537) LITMUS project on human-centric sustainable production funded by Knowledge Foundation He has supervised multiple researchers including Henrik Smedberg and Mahesh Kumbhar, resulting in collaborative publications and the development of practical tools like Mimer - a web-based platform for knowledge discovery in multi-criteria decision support. His research group maintains strong industry connections, particularly with automotive manufacturers in Sweden.
Raul D.S.G. Campilho serves as an Assistant Professor at the School of Engineering, Polytechnic Institute of Porto, specializing in mechanical engineering education and research. His institutional affiliations include Instituto Superior de Engenharia do Porto (ISEP) where he teaches, and ongoing collaborations with Faculdade de Engenharia da Universidade do Porto (FEUP). His research focuses on advanced mechanical engineering domains with particular emphasis on computational fracture mechanics and sustainable manufacturing. Key expertise areas include: Numerical modeling of adhesive joint failure using Cohesive Zone Models (38% research fingerprint) Finite element analysis of composite materials (32% fingerprint) Simulation-driven design of structural adhesives and hybrid joints Automation systems for manufacturing process optimization Robotics applications in industrial settings His recent publications reveal strong trends in sustainable manufacturing technologies, laser-based material processing, and advanced joining techniques for dissimilar materials. Current work emphasizes energy-efficient production systems and high-precision machining innovations. Professional contributions include extensive co-authorship with 362 researchers across institutions including University of Coimbra, University of Minho, and international collaborators. His advising activities focus on mechanical engineering students through research supervision in computational mechanics and manufacturing laboratories.
Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.
Pär Mårtensson is an Associate Professor at the Stockholm School of Economics , specializing in pedagogy and faculty development. His academic work bridges interdisciplinary research practices, deep learning applications in industrial contexts, and innovative approaches to business education. 2025: Deep learning for manufacturing quality inspection 2023: Simulation-based optimization in battery production 2019: Interdisciplinary research quality evaluation His research spans pedagogy , industrial engineering , and risk mitigation strategies , with a strong focus on translating synthetic data and simulation into real-world industrial solutions. Publications highlight his expertise in: Deep learning and computer vision Interdisciplinary research evaluation Business education transformation Management spirituality methodologies Risk dynamics in venture capital While no explicit awards or grants are listed, his leadership role in faculty development underscores his institutional impact.
Juha Fiskari is a Professor at the Department of Engineering, Mathematics and Subject Didactics (IMD) at Mid Sweden University . His work focuses on chemical engineering and environmental technology, particularly in the forest bioeconomy and pulp & paper processes. He is based in Sundsvall, Sweden, and his email is juha.fiskari@miun.se. Research Interests include: Kraft lignin valorization and sustainability Deep eutectic solvent applications Enzymatic and chemical pulp bleaching Biorefinery feedstocks (e.g., Acacia, Eucalyptus) Non-destructive lignin analysis Thermoplastic lignin composites Key Projects involve EU initiatives like "PriDES" and research on data-driven industrial transformation (DRIVEN), sustainable forest bioeconomy (HIPS), and IRS TransTech. His publications span journals such as BioResources , Chemical Engineering Research & Design , and Nature Protocols .
Olof Björkqvist is a Senior Lecturer at Mid Sweden University, affiliated with the Department of Natural Sciences, Design and Sustainable Development (NDH). His research focuses on energy technology, critical infrastructure protection, and industrial symbiosis. Research Interests: Björkqvist investigates bio-sludge recycling from the pulp and paper industry, energy optimization in industrial processes, simulation-based modeling for energy system cooperation, and policy frameworks for critical infrastructure protection. Publications: His work spans topics including black soldier fly larvae feed production, energy system cooperation, techno-economic evaluations of bioenergy integration, and risk assessments in district heating markets.
Daniel Varro is a Professor and Head of Unit at the Department of Computer Science (IDA) of Linköping University, Sweden. He leads the Software and Systems (SAS) department, focusing on AI, software engineering, and cyber-physical systems. His research is supported by major grants like the Vinnova 5.6 million SEK project for AI-generated software quality assurance. Affiliation: Department of Computer Science (IDA), Linköping University Department: Software and Systems (SAS) Research Focus: Model-based systems, large language models for code analysis, reinforcement learning, and cyber-physical safety verification. His recent work includes empirical studies on machine learning notebooks, infrastructure code smells, and data leakage in large language models. He collaborates extensively within the Wallenberg Autonomous Systems Program (WASP) and trains doctoral students in software engineering.
Peter Lundberg is an Adjunct Professor at Linköping University, affiliated with the Department of Health, Medicine and Care (HMV) and Department of Diagnostics and Specialist Medicine (DISP) . His research focuses on quantitative magnetic resonance imaging (MRI/MRS/NMR) for organ function analysis, particularly in liver disease (MASLD, HCC), neurological disorders (pediatric brain tumors, MS), and metabolic syndrome . He also works on text-based AI and federated learning applications in medical imaging. Developed non-invasive liver diagnostics replacing biopsies Co-founder of physiologically-based digital twin models for alcohol metabolism Active in MR safety standardization through ESMRMB Research Trends in recent articles include: Multi-organ interactions (liver-heart axis) Deep learning for brain tumor classification Population-level MASLD prevalence studies Advancements in MR spectroscopy for neurochemical analysis Collaborations with international societies like ISMRM and ESMRMB , and research centers including the Center for Medical Imaging and Visualization (CMIV) and Wallenberg Center for Molecular Medicine .
Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .
Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.
Hana Dobsicek Trefna is an Associate Professor at Chalmers University of Technology, working within the Biomedical Electromagnetics research group in the Department of Electrical Engineering. Her academic career has focused on developing microwave-based technologies for cancer treatment, with particular expertise in hyperthermia systems for deep-seated tumors. Dr. Dobsicek Trefna's research interests center around microwave hyperthermia treatment systems, with significant contributions in UWB antenna design, electromagnetic field focusing algorithms, and treatment planning for deep tumors. Her work spans both theoretical development and clinical implementation, with special focus areas including head and neck tumor treatment, pediatric brain tumor applications, and quality assurance methodologies for hyperthermia devices. She has pioneered approaches to time-reversal focusing, multi-frequency heating, and hot-spot management in deep tissue heating applications. Analysis of her recent publications reveals a strong emphasis on practical clinical implementation of hyperthermia systems, with increasing focus on pediatric applications and quality assurance protocols. Her work bridges engineering innovation with clinical oncology needs, particularly in developing standardized procedures and validation methods for hyperthermia devices. Her research shows consistent progression from fundamental electromagnetic principles to clinical implementation challenges. Fokuserad mikrovågs hypertermi: för minskade långsiktiga effekter av strålning av barn med hjärntumörer (2022-2025, funded by Vetenskapsrådet) Creation of advanced cancer treatment planning to boost the effect of Radiotherapy by combining with hyperthermia (HYPERBOOST, 2020-2024, EU-funded) Multiple projects on hyperthermia for pediatric brain tumors (funded by Barncancerfonden) Kliniskt hypertermisystem för bättre cancerbehandling i hals och huvud (2017-2018, VINNOVA-funded) Dr. Dobsicek Trefna leads the Biomedical Electromagnetics research group, which focuses on developing microwave-based diagnostic and therapeutic systems. Her team works closely with clinical partners to translate engineering innovations into practical cancer treatment solutions, with particular emphasis on systems for treating deep-seated tumors and pediatric applications where conventional treatments have significant side effects.
Jelke Dijkstra is an assistant professor at Chalmers University of Technology, working within the Department of Geology and Geotechnology's Geotechnics research group. His academic career focuses on experimental approaches to geomechanics, with particular emphasis on understanding soil behavior through innovative testing methodologies. Dr. Dijkstra's research interests center on experimental geomechanics, especially in capturing and understanding fundamental properties of granular materials using unconventional methods. He has developed new experimental techniques for handling and probing (loose) soils and is an expert in designing and conducting non-standard material testing and physical model experiments, including geotechnical centrifuge tests. His work extensively utilizes 2D imaging techniques for deformation and stress analyses, and he has pioneered the use of geo-electrical techniques to track significant changes in soil samples. His research bridges fundamental soil mechanics with practical engineering applications. Analysis of his recent publications (2024-2025) reveals a strong focus on critical geotechnical challenges, particularly related to sensitive clays, railway infrastructure, and soil-structure interaction. His work spans multiple scales from nanometric clay analysis to full-scale infrastructure modeling. Key themes include quick clay genesis, pile foundation behavior in soft soils, concrete corrosion mechanisms, and advanced monitoring techniques for infrastructure performance. His research demonstrates a consistent interdisciplinary approach combining experimental methods with numerical modeling. Dr. Dijkstra actively participates in numerous collaborative research projects funded by various organizations including Formas, Vetenskapsrådet (VR), Trafikverket, and EU programs. His work addresses critical infrastructure challenges related to climate change adaptation, sustainable transportation systems, and improved geotechnical design methodologies for soft soil conditions.
Tobias Gebäck is an Associate Professor at Chalmers University of Technology's Applied Mathematics and Statistics department, affiliated with the SuMo Biomaterials research center. His work focuses on mathematical modeling and numerical methods for mass transport in soft porous materials , with applications in drug delivery systems and absorbent product development . He also investigates charged particle transport mechanisms relevant to cancer radiation therapy . Key research areas: mathematical modeling, numerical methods, transport in porous media, radiation therapy applications Active collaborations: SuMo Biomaterials research center Recent publications emphasize stochastic modeling of 3D structures , Lattice Boltzmann simulations for transport phenomena in complex materials, and multi-scale diffusion analysis of bio-based systems. This work connects fundamental mathematics with industrial applications in pharmaceutical and consumer product development.
Scott MacKinnon is a Full Professor in Maritime Studies within the Mechanics and Maritime Sciences department at Chalmers University of Technology. His extensive research portfolio focuses on maritime safety, human factors in navigation, and the impact of digitalization on maritime operations. With numerous publications spanning from 2015 to 2025, he has established himself as a leading academic in maritime human factors research. MacKinnon's research interests span maritime safety systems, human factors in navigation, autonomous shipping technologies, and sociomaterial perspectives on technology implementation. His work consistently examines how digitalization and automation impact crew performance, safety management, and regulatory frameworks. He investigates practical applications of decision support systems while maintaining focus on human-centered design principles in maritime contexts. His recent publications reveal a clear progression toward examining neurophysiological aspects of operator performance, autonomous shipping challenges, and AI integration in navigation systems. The research demonstrates increasing sophistication in methodology, moving from traditional observational studies to incorporating advanced data analytics and physiological measurements in bridge simulators. MacKinnon has secured significant research funding through multiple European Commission projects including AUTOBarge, SAFEMODE, and EfficienSea 2, as well as collaborations with Swedish maritime authorities. His grant portfolio demonstrates strong industry relevance and international collaboration. He maintains active collaborations with researchers across Europe, particularly with Monica Lundh and Reto Weber at Chalmers, and participates in interdisciplinary teams examining the human dimensions of maritime technological change. His research approach consistently integrates theoretical frameworks with practical maritime applications.
