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Estela Perez Luque is a researcher at the University of Skövde within the School of Engineering Science and Interaction Lab (ILAB) . She specializes in Digital Human Modeling (DHM) , ergonomics, and simulation-based design for automotive and industrial applications, with a focus on integrating human variability into engineering workflows. Affiliated with the INFINIT research environment and projects like ADOPTIVE (Automated Design & Optimization of Vehicle Ergonomics) and VIVA (Virtual Vehicle Assembler). Collaborates with Chalmers University of Technology and SAFER Vehicle and Traffic Safety Centre . Her research explores: Ergonomic simulation tools for automotive design Exoskeleton evaluation using DHM frameworks 3D body shape prediction models for standardized DHM manikins Eye-tracking in VR for understanding motor redundancy Biomechanical reference points in vehicle packaging design Publications demonstrate expertise in multi-objective optimization for balancing ergonomics and productivity, statistical modeling of human variability, and VR validation for experimental setups. Funded by the Knowledge Foundation , Vinnova , and Swedish industry partners. Her work emphasizes open-source contributions, including MATLAB scripts for body shape prediction and validation protocols for VR eye-tracking. She collaborates with international teams at conferences like DHM 2025 and IEA 2021 .

• Digital Human Modeling
• Ergonomics
• Virtual Reality
• +4 more

Terje Falck-Ytter is a Professor in the Department of Psychology at Uppsala University, specializing in Developmental Psychology. With a PhD awarded in 2010 following clinical training in psychology, his research focuses on characterizing and understanding the development of infants later diagnosed with neurodevelopmental conditions, particularly autism spectrum disorder (ASD). His primary research interests include developmental psychology, autism spectrum disorder, infant development, twin studies, genetics and environmental influences, eye tracking research, and neurodevelopmental conditions. His work investigates how genetic and environmental factors contribute to various developmentally informative measures early in life. Falck-Ytter's extensive publication record demonstrates significant contributions to understanding social attention, sensory processing, and early biomarkers for autism. His research utilizes advanced methodologies including eye tracking, twin studies, and genetic analyses to uncover developmental pathways and early indicators of neurodevelopmental conditions. His work has practical implications for early detection and intervention strategies for children at risk. Infants' looking preferences for social versus non-social objects reflect genetic variation (2024) Social attention: Developmental foundations and relevance for autism spectrum disorder (2023) The sensory-first account of autism (2023) Joint attention in infancy and the emergence of autism (2019) Enhanced pupillary light reflex in infancy is associated with autism diagnosis in toddlerhood (2018) Falck-Ytter leads the Babytwins Study Sweden (BATSS), a multi-method infant twin study examining genetic and environmental factors influencing infant brain and behavioral development. His research has been widely cited and has appeared in high-impact journals including Nature Human Behaviour, Nature Communications, and Biological Psychiatry. His work bridges clinical practice and research, with implications for early identification and intervention strategies for children with neurodevelopmental conditions. He has mentored numerous researchers and collaborated extensively with international teams studying early autism markers across European populations.

• Developmental Psychology
• Autism Spectrum Disorder
• Infant Development
• +6 more

Yiannis Karayiannidis is an Associate Professor in the Department of Automatic Control at Lund University's Faculty of Engineering (LTH). He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). A portion of his research team is based at Chalmers University of Technology, specifically within the Division of Systems and Control (SYSCON). He actively contributes to the robotics and control community through editorial roles and leadership in professional organizations. His research focuses on advanced robotic control systems, particularly in human-centered environments. Key areas include robotic manipulation, dual-arm coordination, force and grasping control, physical human-robot interaction, robot learning, navigation, reactive planning, multi-agent systems, and nonlinear and adaptive control. These interests are reflected in both his teaching and research output. The recent publication trends highlight a strong focus on autonomous navigation in dynamic environments, robust force control, multi-agent coordination, and learning-based manipulation. His work integrates model-based control with data-driven methods, particularly in complex and uncertain settings. Topics such as obstacle avoidance, tunnel-following MPC, friction modeling, and semantic feature estimation demonstrate technical depth and real-world applicability. Prof. Karayiannidis holds significant academic service roles: Associate Editor, IEEE Robotics and Automation Letters Associate Editor, IEEE/ASME Transactions on Mechatronics Associate Editor, International Conference on Robotics and Automation (ICRA) Associate Editor, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Associate Editor, European Control Conference Treasurer, IEEE Robotics Chapter in Sweden He has supervised numerous PhD and master's students and is actively involved in research projects funded by national and strategic initiatives. His editorial and supervisory roles underscore his leadership in the robotics and control community. He also contributes to infrastructure development as a manager of RobotLab LTH. He is involved in several key research labs and teams: RobotLab LTH – a shared robotics research infrastructure where he serves as a manager Division of Systems and Control (SYSCON), Chalmers University of Technology – hosts part of his research team Center for Construction Robotics – collaborative network involving Lund University researchers WASP and ELLIIT research networks – strategic national platforms for AI and robotics

• Robot Control
• Robotic Manipulation
• Human-Robot Interaction
• +12 more

Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +5 more

Amanda Berg is an Adjunct Associate Professor at the Department of Electrical Engineering, Linköping University, affiliated with the Computer Vision Laboratory (CVL). Her research focuses on thermal imaging, computer vision, and machine learning applications in environmental monitoring and security systems. Key research areas include: Thermal-Visible Spectrum Correlation 3D Reconstruction from Satellite Data Anomaly Detection in Wild Environments Multispectral Video Analysis Wildlife Management via UAV Imagery Her work spans computer vision, anomaly detection, and thermal imaging, with recent publications on neural radiance fields, wildfire detection benchmarks, and multispectral tracking algorithms. The 15 most recent articles demonstrate a strong focus on thermal infrared applications and AI-driven image analysis.

• Computer Vision
• Thermal Imaging
• Machine Learning
• +5 more

Ulla Ahonen-Jonnarth is an Associate Professor at the University of Gävle, specializing in decision analysis, risk management, and environmental policy. Her research focuses on multi-criteria decision models, geospatial decision frameworks, and human judgment in complex trade-offs. She has contributed to understanding stakeholder collaboration strategies between universities and society, and the psychological aspects of balancing environmental and personal gains/losses. Her work spans experimental psychology (e.g., anchoring effects in judgments) to geospatial visualization tools for decision support. She has published extensively in journals like Renewable Energy Focus, Journal of Multi-Criteria Decision Analysis, and ISPRS International Journal of Geo-Information. Her earlier research (2000s) explored forest ecology, mycorrhizal symbiosis, and plant responses to heavy metals. No scientific awards or student advisees are explicitly listed in the provided materials. Her professional activities include collaborative projects with institutions like the Swedish University of Agricultural Sciences and contributions to forest management simulations.

• Decision Analysis
• Risk Management
• Environmental Policy
• +3 more

Alina Galimshina is a Postdoctoral Researcher at Chalmers University of Technology’s Department of Building Technology (School of Architecture and Civil Engineering). Her work focuses on robust and sustainable building renovation strategies, integrating life cycle assessment (LCA), life cycle cost analysis (LCC), and uncertainty quantification. She holds a PhD from ETH Zurich (2018), an MSc from Lund University (2015), and a BSc from Nosov Magnitogorsk State Technical University (2010). Research interests include: Environmental building design Robust optimization under climate change Cost-effective renovation solutions Bio-based materials for carbon sequestration Parametric tools for real-time LCA Key contributions include the Bombyx design-integrated LCA tool and methodologies for probabilistic assessment of building renovation strategies. Her work emphasizes bridging gaps between architects, energy consultants, and policymakers through collaborative BIM tools and policy-relevant insights. Recent studies highlight the critical role of heating system replacement over traditional insulation measures, challenging conventional renovation practices. Climate change scenarios analysis reveals significant cost and emissions savings under RCP2.6 frameworks. Collaborations include the DUREE project and international institutions like the Pacific Institute for the Mathematical Sciences. She co-authored guidelines for design decision-makers under IEA EBC Annex 72, promoting standardized LCA methodologies in early-stage building design.

• Life Cycle Assessment
• Environmental Building Design
• Life Cycle Cost Analysis
• +4 more

Björn Olofsson is an Associate Professor and Senior Lecturer in the Department of Automatic Control at Lund University's Faculty of Engineering. He also serves as the Director of First and Second Cycle Studies and is a Project Manager. He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). His academic affiliations span Lund University and Linköping University, where he was appointed Docent in 2020. He holds an M.Sc. in Engineering Physics and a Ph.D. in Automatic Control, both from Lund University. His academic journey reflects a strong foundation in engineering and control systems. His research focuses on the autonomy of robots and vehicles, with emphasis on motion planning and optimal motion control. He explores applications in ground vehicles, unmanned aerial and surface vehicles, and industrial robotics. His work intersects with key global challenges, including sustainable transport and digitalization, aligning with UN Sustainable Development Goals related to technology and health. The 15 most recent publications analyzed show a consistent trend in autonomous systems, predictive control, and robotics. Topics include uncertainty-aware motion planning, human-robot collaboration, maritime autonomy, and learning-based control. The research integrates AI, machine learning, and advanced control theory, applied across aerial, marine, and terrestrial domains. Björn actively supervises multiple PhD students and has led numerous research projects, such as ELLIIT B14 and the Center for Construction Robotics. He is involved in organizing academic events like Robotics Week for Schools and manages the RobotLab LTH infrastructure. He has taught a range of courses including Applied Robotics, Autonomous Vehicles, and graduate-level courses on motion planning and optimal control. He also supervises Master’s theses in Automatic Control and Vehicular Systems.

• Autonomy of robots and vehicles
• Motion planning
• Optimal motion control
• +6 more

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +6 more

Prayag Tiwari is a Senior Lecturer at Halmstad University's School of Information Technology, where he conducts cutting-edge research at the intersection of artificial intelligence, quantum computing, and healthcare applications. With an SMIEEE designation and an active research profile, Dr. Tiwari contributes significantly to both academic and practical advancements in intelligent information systems. His research interests span a diverse spectrum including Artificial Intelligence, Quantum Machine Learning, Deep Learning, Healthcare, Bioinformatics, NLP/LLM, Computer Vision, Multimodal Learning, and Intelligent Systems. This interdisciplinary focus enables him to tackle complex problems across multiple domains, particularly in developing quantum-inspired algorithms for healthcare applications and advancing multimodal learning techniques for real-world challenges. Dr. Tiwari's publication record demonstrates consistent high-impact contributions across multiple domains, with recent work showing a clear trend toward quantum machine learning applications, particularly in healthcare and bioinformatics. His research frequently combines traditional deep learning approaches with quantum computing principles to develop novel solutions for drug interaction prediction, medical data processing, and multimodal analysis. The interdisciplinary nature of his work is evident in publications spanning computer vision, natural language processing, and blockchain applications for healthcare security. 2022 Best Paper Award for a publication in Neural Networks Multiple Highly Cited Papers in various journals Editor for Neural Networks and IEEE Transactions on Fuzzy Systems As an academic leader, Dr. Tiwari serves as an editor for prestigious journals including Neural Networks and IEEE Transactions on Fuzzy Systems, contributing to the advancement of knowledge in his fields of expertise. His extensive publication record in top-tier venues such as TMLR, IJCV, TNNLS, AAAI, ACL, CIKM, ICASSP, ECIR, and ECML demonstrates significant scholarly impact. While specific grant information isn't detailed in the provided materials, his research output suggests active funding for projects in quantum machine learning and healthcare applications. Dr. Tiwari's work appears to be conducted within collaborative research environments at Halmstad University, particularly within the School of Information Technology's research groups focused on artificial intelligence and quantum computing applications. His publications frequently involve multi-institutional collaborations, suggesting active participation in both national and international research networks focused on advancing machine learning techniques for practical applications.

• Artificial Intelligence
• Quantum Machine Learning
• Deep Learning
• +6 more

Mårten Björkman is a Professor at the School of Electrical Engineering and Computer Science , Royal Institute of Technology (KTH) , specializing in Robotics, Perception and Learning . His research bridges 3D computer vision with multimodal sensor fusion, including force-torque sensing, to enhance human-robot interaction. Recent work focuses on modeling sequential data for robotic control, human movement prediction, and power systems. Key research areas include: Reinforcement learning for robotic systems Human movement analysis and prediction Implicit regularization in neural networks Online learning for dynamical systems Power system control with AI 3D perception through multi-view vision His recent publications span deep learning for assembly inspection , EEG-based brain-robot interfaces , safe reinforcement learning , and multimodal motion generation . The work intersects robotics, machine learning, and human-centric system design. The Robotics, Perception and Learning division at KTH hosts his research, equipped with state-of-the-art facilities for robotic vision and human interaction studies.

• Robotics
• Computer Vision
• Human-Robot Interaction
• +7 more

Jörgen Ahlberg is an Adjunct Associate Professor at the Computer Vision Laboratory (CVL) within the Department of Electrical Engineering (ISY) at Linköping University. He specializes in computer vision, focusing on facial recognition, thermal infrared imaging, and radar micro-Doppler analysis. University: Linköping University Department: Department of Electrical Engineering (ISY) Lab: Computer Vision Laboratory (CVL) Email: jorgen.ahlberg@liu.se His research spans facial recognition, hyperspectral imaging, UAV-based wildlife detection, and advanced tracking algorithms. Recent work includes detection-guided attention mechanisms for radar target classification and self-supervised learning frameworks for tracking applications. He received the Entrepreneurial Teacher of the Year award at Linköping University in 2023. His academic contributions are reflected in publications covering thermal infrared tracking, biometric data filtering, and multi-sensor surveillance systems.

• Computer Vision
• Machine Learning
• Remote Sensing
• +3 more

Mikael Nilsson is a Senior Lecturer and key member of the Faculty of Engineering (LTH) at Lund University , Sweden. He is affiliated with the Centre for Mathematical Sciences and serves as Programme Director for the international master's program in Machine Learning, Systems and Control since 2019. Education : M.Sc.E.E. and Ph.D. in Applied Signal Processing (2007) from Blekinge Institute of Technology Research Focus : Developing novel Machine Learning models with emphasis on Deep Neural Network Designs Applications in Computer Vision (3D object estimation, traffic safety, radiation therapy) Applied research in Engineering Health and Natural & Artificial Cognition profile areas Recent Research Outputs (2022-2023): Medical imaging segmentation for cancer therapy (Radiation Oncology) Urban traffic surveillance systems (IEEE ITSC) 3D shape estimation from monocular vision Multi-view pose estimation via segmentation masks Yield forecasting using satellite-ground data fusion Key Collaborations : Swedish Government Agency for Innovation Systems (Vinnova) - DAIDESS project (2023-2026) Interdisciplinary work in Engineering Health and Medical Imaging Industrial collaboration with Axis Communications AB (2016-2019)

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

• cyber-physical systems
• formal verification
• runtime monitoring
• +5 more

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Wireless Networking
• Signal Processing
• Machine Learning
• +3 more