Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.
Erik Asbjörn Mikkelsen Jensen is a researcher at Chalmers University of Technology affiliated with the Department of Physics, specializing in Subatomic, high-energy and plasma physics. His recent work focuses on applying deep learning techniques to rare event detection in particle physics experiments. Research interests include Subatomic physics Machine learning Object detection The 2025 publication demonstrates a novel CNN-based framework for analyzing 3D track data from the GADGET II TPC, achieving perfect recall for rare two-particle events through simulation-based training with parameter perturbations. This work intersects computer science and high-energy physics.
Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.
Mattias Sandberg is an Associate Professor at the Royal Institute of Technology (KTH) , Sweden, within the School of Engineering Sciences and the Division of Numerical Analysis, Optimization and Systems Theory . He is actively engaged in teaching and examining a broad spectrum of courses in numerical methods, differential equations, and computational mathematics. His research and teaching interests span numerical analysis , partial differential equations , stochastic differential equations , and parallel computing . He is responsible for several core and advanced courses, including Analytical and Numerical Methods for Differential Equations , Computational Methods for Stochastic Differential Equations and Machine Learning , and Numerical Methods for Partial Differential Equations . These courses reflect his deep involvement in both theoretical and applied aspects of computational mathematics. Sandberg also serves as examiner for multiple degree projects and advanced courses, underscoring his role in mentoring and evaluating students at the master's level. His affiliation with KTH and his extensive teaching responsibilities indicate a strong commitment to education and research in applied and computational mathematics.
Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.
Mattias Dahl is a Professor at the Faculty of Engineering, Blekinge Institute of Technology, affiliated with the Department of Mathematics and Natural Sciences since 1993. His research spans systems engineering, applied mathematics, and their applications in simulation, optimization, and modeling of technical systems, particularly in intelligent transport systems (ITS) through collaborations with Swedish Transport Agency and Administration. He has developed measurement systems using drones and satellites, focusing on area-wide change analyses and commercialization of research outputs. Education: B.Eng. in Electrical Engineering, Chalmers University M.Eng. in Computer Engineering, Luleå University of Technology Licentiate in Telecommunication Theory, Lund University of Technology PhD in Applied Signal Processing, Blekinge Institute of Technology (2000) His research emphasizes optimization of technical systems, self-learning methods, and artificial intelligence, with industry collaborations resulting in patents in mobile communication and computer vision. Recent work includes AI-driven weed seed reduction, railway capacity optimization (KAJT), and charging station allocation for EVs. He has contributed to projects like ADAS and Combating Reindeer Poaching with Drones, while also reviewing grants for international journals. Key scientific awards include the Teknikbrostiftelsen scholarship and Vinnova verification funds. His 15 most recent publications focus on radar interference mitigation, traffic data analysis, drone calibration, and charging infrastructure optimization.
Roland Barthel serves as Professor in the Department of Geosciences at the University of Gothenburg's Faculty of Science and concurrently holds the position of Vice-Dean for the Faculty Office of Natural Sciences and Technology. Previously Head of Department (2018-2022), he specializes in groundwater resources within the context of climate change impacts across Sweden and Europe. His research integrates hydrological science with socioeconomic factors to address critical water challenges including drought, scarcity, and extreme weather events. Barthel emphasizes the societal dimensions of water management, particularly regarding unregulated private wells that constitute a significant portion of Sweden's drinking water supply. His interdisciplinary approach bridges natural and social sciences to develop implementable solutions requiring consideration of economic, political, and social frameworks alongside physical system understanding. Analysis of his publication record reveals a dominant focus on data-driven methodologies for groundwater dynamics assessment, drought propagation analysis, and climate adaptation strategies. His work consistently emphasizes regional-scale studies in temperate and high-latitude environments, with increasing attention to interdisciplinary collaboration frameworks. Scientific recognition includes: Student Union's Pedagogical Award (2014) Student Union's Pedagogical Award (2016) As an educator, Barthel pioneered hydrology instruction at the university by developing the master's course Applied Hydrology (GVG460) and undergraduate Hydrology and Hydrogeology (GV2002). He leads Earth Sciences thesis supervision, chairs the program committee, and contributes to multiple geoscience courses. His teaching excellence earned three consecutive pedagogical award nominations with wins in 2014 and 2016. He currently participates in an EU Erasmus+ project developing digital teaching tools with European partners, addressing high market demand for groundwater specialists in Swedish infrastructure and environmental sectors.
Andreas Almqvist is a Professor in Machine Elements at Luleå University of Technology (LTU), working within the Department of Engineering Sciences and Mathematics. He serves as the director and operations manager of the Center for Sports and Performance Technology (SPORTC) and is actively involved in research and teaching related to computational tribology. Almqvist has been affiliated with LTU since completing his Master's degree in 2001, progressing through academic ranks to his current professorship. Almqvist completed his Master of Science in Engineering at LTU in December 2001 and defended his PhD thesis in September 2006 titled 'On the Effects of Surface Roughness in Lubrication.' Following a 2-year postdoctoral position with Shell Global Solutions in England under the Marie Curie Transfer of Knowledge program (2007-2008), he returned to LTU in January 2009. He became Docent in November 2010, was appointed Associate Professor in January 2012, and was promoted to Professor in Machine Elements in May 2017. His research focuses on computational tribology, with particular emphasis on multiphysics and multiscale modeling and simulation of continuum mechanical problems in tribology and sports technology. His work spans contact mechanics, flows in thin gaps, and friction phenomena, with recent applications to skiing performance. Since fall 2021, he has been collaborating with the Swedish Olympic Committee's 'Olympisk Offensiv' research program alongside other prominent Swedish sports scientists. His research approach integrates applied mathematics with practical engineering challenges, often involving collaborations across departments including Fluid Mechanics, Mathematics, and Machine Learning. Analysis of Almqvist's recent publications reveals a strong trend toward applying tribological principles to winter sports performance, particularly skiing. His work bridges fundamental computational methods with practical applications, showing increasing interdisciplinary collaboration across engineering disciplines, sports science, and materials science. The research demonstrates both theoretical advances in modeling techniques and practical applications for performance enhancement in Olympic sports. ERC Grant Recipient Marie Curie Transfer of Knowledge Program Participant VR (Swedish Research Council) Grant Recipient for multiple projects including 'New Concepts in Thin Film Flow Modelling' (DNR 2014-4894) and 'Multiscale Topological Optimization for Lower Friction, Less Wear and Leakage' (DNR 2019-04293) Almqvist serves as Editor-in-Chief for 'The Proceedings of the IMechE Part J - Journal of Engineering Tribology' since 2019 and has been involved in numerous research projects funded by both academic and industrial partners. He has supervised student projects in collaboration with academic and industrial partners and has secured significant research funding from the Swedish Research Council. His educational leadership extends to serving as program director for the Engineering Physics and Electrical Engineering program at LTU. As director of the Center for Sports and Performance Technology (SPORTC), Almqvist leads the Ski and Snow Lab which focuses on physics at different scales related to friction in skiing. The center represents a strategic initiative at LTU to bridge engineering science with sports performance, creating a unique interdisciplinary research environment that brings together expertise from multiple departments including Machine Elements, Fluid Mechanics, and Mathematics.
Christer Åhlund is a Professor and Head of Subject in Pervasive and Mobile Computing at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. He is based in Skellefteå and leads research in distributed computer systems. University: Luleå University of Technology School: Department of Computer Science, Electrical and Space Engineering Department: Computer Science Position: Professor and Head of Subject Professor Åhlund's research interests span network protocols, IoT, cloud-based and distributed data processing, AI/ML, performance analysis, and cybersecurity. For over a decade, his research has focused on digital solutions for societal functions, particularly 'Smart Cities/Regions.' His work integrates theoretical foundations with practical applications to address real-world challenges in urban and regional development. His approach combines technical expertise with an understanding of societal needs, creating solutions that are both innovative and implementable in real-world settings. His research demonstrates a strong trend toward integrating AI/ML techniques with distributed systems to create intelligent, responsive infrastructure. The focus on Quality of Experience (QoE) in heterogeneous networks shows his commitment to user-centered design in technical systems. His recent work on anomaly detection in energy systems and XR applications demonstrates the expanding scope of his research into new application domains. ENiSDA project ESUDA project ACECyberSafe project SSiO project Professor Åhlund has supervised numerous PhD students whose work focuses on smart applications, IoT, machine learning in various contexts, and network resource management. His leadership extends beyond direct supervision to establishing research centers, including 'Security in Society and Critical Infrastructures' at LTU in 2016. He previously served as Scientific Director for LTU's 'Enabling ICT' research and innovation area from 2013-2019 and as department head from 2003-2012. He currently teaches Applied Computer Security, Internet Security, Data Science programming, and Advanced Computer Networks, drawing on his extensive industry experience (12 years in ICT) to bridge theory and practice for students.
Florent Imbert is a Postdoctoral Researcher at Luleå University of Technology, Sweden, within the Department of Systems and Space Engineering, focusing on machine learning research. His educational background includes: Master's degree in Applied Mathematics from La Rochelle University PhD in Computer Science from IRISA (SHADoc team) Research Interests: Dr. Imbert specializes in deep learning applications for handwriting trajectory reconstruction using DigiPen's kinematic sensors (accelerometers, gyroscopes, magnetometers, force sensors), contributing to the KIHT project in collaboration with Stabilo, KIT, and Learn&Go. His current work advances sustainable and energy-efficient machine learning methodologies to reduce computational resource demands in AI systems. Scientific Awards: No awards or fellowships are mentioned in the provided text. Advising and Grants: There is no information available regarding advised students or funded research grants. Labs and Teams: He is an active member of the Machine Learning research team at Luleå University of Technology. Previously, he collaborated with the SHADoc team at IRISA during his doctoral work on the KIHT handwriting analysis project.
György Kovács is a Senior Lecturer at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, working within the Embedded Intelligent Systems LAB. His primary research focus is on Machine Learning applications, particularly in speech and language technology. His research interests span multiple areas of speech and language processing, including: Automatic Speech Recognition across diverse conditions and languages Paralinguistic Speech Processing for emotion detection and speaker state analysis Audio event classification, including medical applications like cough sound analysis Sentiment analysis in written text, with particular interest in hateful language detection Bot detection in social media analysis Kovács has supervised numerous Master's theses since 2021, with topics ranging from accent classification and sentiment analysis to AI-generated code quality assessment. Many of these supervisions have led to co-authored publications with students. His recent publications demonstrate a strong focus on applying machine learning to diverse domains including remote sensing, healthcare technology, and natural language processing. The research shows a clear pattern of interdisciplinary work connecting machine learning with practical applications across environmental monitoring, healthcare, and social media analysis. His academic contributions include co-supervising PhD students such as Sana Al-Azzawi and Nosheen Abid, whose dissertation work on Unsupervised Curriculum Learning for Earth Observation represents significant contributions to the field. Notable publications include work on cloud detection using synthetic datasets, seagrass classification, and emotion classification using EEG in healthcare settings. Kovács also teaches courses including Introduction to Artificial Intelligence (D0032E) and Machine Learning and Pattern Recognition (D0033E), demonstrating his commitment to both research and education in the field of artificial intelligence.
Anders Eklund is a Professor of Medical Engineering at Umeå University with dual appointments: as a Professor with joint clinical employment in the Department of Diagnostics and Intervention (Biomedical Engineering and Radiation Physics section) at Norrland University Hospital, and as a Professor in the Department of Applied Physics and Electronics. He is also an active member of the Centre for Biomedical Engineering and Physics. His research centers on biofluid mechanical applications in clinical neuroscience and ophthalmology, specifically investigating pressure and flow dynamics in the brain and eye to understand diseases like hydrocephalus and glaucoma. This interdisciplinary work bridges engineering principles with clinical practice through close collaboration with neurologists, ophthalmologists, and neurosurgeons. Professor Eklund's publication record demonstrates consistent output in high-impact journals, with recent work emphasizing cerebrospinal fluid dynamics, cerebral blood flow regulation, and glymphatic system function. His methodological approach integrates advanced 4D flow MRI techniques with computational modeling to translate engineering insights into clinical applications. Key research projects include MT4North (2020-2022) for medical technology innovation, studies on intraocular pressure reduction mechanisms in glaucoma (2014-2016), and investigations of microgravity effects on intracranial pressure. His work maintains strong clinical relevance through the Centre for Biomedical Engineering and Physics, which facilitates collaboration between engineers, physicists, and clinicians. The Centre for Biomedical Engineering and Physics provides Professor Eklund's research environment, supporting interdisciplinary projects that combine medical device development, advanced imaging, and clinical validation. This structure enables direct translation of engineering solutions to patient care, particularly in neurological and ophthalmological diagnostics.
Christina Wanhainen is a Professor and subject representative in Ore Geology at Luleå University of Technology, specifically within the Department of Geosciences and Environmental Engineering, which is part of the Department of Civil, Environmental and Natural Resources Engineering. She is based in Luleå, Sweden, with contact information including phone number 0920-492401 and email christina.wanhainen@ltu.se. Professor Wanhainen's research focuses on ore geology, with particular emphasis on greenstone belts, VMS (volcanogenic massive sulfide) deposits, komatiites, and geochronology. Her work spans geographical regions including the Zimbabwe Craton in Africa (particularly the Manica greenstone belt in Mozambique) and the Skellefte district in Sweden. She has been actively publishing research in 2024-2025, demonstrating her current engagement in the field. Her recent publications reveal an evolving research trajectory that increasingly incorporates modern technological approaches, particularly machine learning applications in geometallurgical modeling for ore tracking. This interdisciplinary approach bridges traditional geological methods with contemporary data science techniques, representing an innovative direction in ore deposit research. Professor Wanhainen's work on Archean geological formations contributes significantly to our understanding of early Earth processes and the formation of economically important mineral deposits. Her precise dating techniques help establish accurate timelines for major geological events that occurred billions of years ago. She maintains active international collaborations, particularly with researchers in Mozambique, indicating a global perspective in her geological investigations. Her participation in conferences like the Society for Geology Applied to Mineral Deposits (SGA) Biennial Meeting demonstrates her engagement with the international geological community.
Viet Thuy Vu is an Associate Professor in Systems Engineering at the Department of Mathematics and Natural Sciences, Faculty of Engineering, Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He received his licentiate and doctoral degrees in applied signal processing from BTH in 2009 and 2011 respectively, and has been active at the institution since 2013, progressing from postdoctoral researcher in radar algorithm development to his current position. His research focuses on advanced radar signal processing with particular expertise in Synthetic Aperture Radar (SAR) technologies. His work spans multiple specialized areas including SAR signal processing, SAR applications in change detection, SAR Ground Moving Target Indication (GMTI), and radio occultation. He has authored and co-authored more than 100 scientific publications in these fields. Recent research output shows a strong focus on FOPEN (Foliage Penetration) SAR change detection, with multiple 2025 publications exploring Bayesian approaches, logistic regression enhancements, and experimental validation using CARABAS II/LORA-VHF systems. His work also extends to GNSS radio occultation for ionospheric studies and mathematical formulations for SAR systems with circular apertures. His research demonstrates consistent collaboration with Mats Pettersson and other researchers at BTH and beyond, indicating active participation in radar remote sensing research communities.
Nosheen Abid is a Postdoctoral Researcher at Malmö University's Faculty of Technology and Society, Department of Computer Science and Media Technology. Her research focuses on machine learning applications, particularly unsupervised curriculum learning methods for various domains including earth observation, robotics, and computer vision. Her primary research interests include: Machine Learning and Deep Learning Unsupervised Curriculum Learning Computer Vision and Image Analysis Earth Observation and Remote Sensing Human-Robot Interaction Artificial Intelligence for Environmental Applications Abid is actively involved in the project "Human-robot collaborative learning for ultralight electric utility vehicles," which aims to enhance autonomous capabilities through the integration of end-to-end imitation learning with interactive machine learning. Her publication record shows consistent output across multiple application domains, demonstrating expertise in applying advanced machine learning techniques to solve real-world problems in environmental science, neuroscience, infrastructure analysis, and social media. She is affiliated with the Sustainable Digitalisation Research Centre at Malmö University, which studies both social and technological aspects to promote sustainable digitalisation. Her collaborative work with researchers like Marcus Liwicki and others indicates strong interdisciplinary connections across multiple institutions.
