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Mattias DahlView profile

Professor

• Systems Engineering
• Applied Mathematics
• Signal Processing
• +7 more

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.

Tufve NyholmView profile

Professor

• Radiotherapy
• Medical Imaging
• Artificial Intelligence
• +5 more

Tufve Nyholm serves as Professor in Radiation Physics at Umeå University with a joint appointment as Medical Physicist at the University Hospital of Northern Sweden. He holds the position of Head of Department within the Department of Diagnostics and Intervention, specifically leading the Section of Biomedical Engineering and Radiation Physics under Oncology. His work bridges clinical radiotherapy applications with advanced imaging research, operating from Location 10C at Norrlands Universitetssjukhus in Umeå. His primary research focuses on medical imaging modalities including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), with strong emphasis on prostate cancer applications. The research integrates artificial intelligence for image analysis, segmentation, and uncertainty estimation to optimize radiotherapy treatment planning. Key initiatives explore correlations between clinical imaging, histopathology, and molecular risk markers to enable personalized radiotherapy solutions. The work spans both technical development of AI tools and clinical validation studies through multi-disciplinary collaboration. Publication trends reveal consistent focus on AI-driven solutions for radiotherapy challenges, particularly in prostate and cervical cancers. Recent work emphasizes resource-efficient AI segmentation, MR-only radiotherapy protocols, and quantitative imaging biomarkers. The research demonstrates strong clinical translation with frequent co-authorship from oncologists, radiologists, and pathologists, reflecting integrated team science approaches. Nyholm leads the Tufve Nyholm Lab research group comprising associate professors, medical physicists, and doctoral students. Current projects include AI-based delineation in radiotherapy (2024-2026), quantitative MRI for individualized radiotherapy (2023), pediatric radiotherapy automation (2022-2024), and the PAMP prostate cancer study. The group receives external funding from the Swedish Cancer Society, Cancerforskningsfonden Norrland, Region Västerbotten, and Prostatacancerförbundet. The lab maintains extensive collaborations across Umeå University including Oncology (Karin Söderkvist, Camilla Thellenberg Karlsson, Björn Zackrisson), Pathology (Anders Bergh), Radiology (Sara Strandberg, Katrine Riklund), Urology (Andreas Josefsson), and Computer Science (Tommy Löfstedt, Polina Kurtser). This multi-disciplinary network enables comprehensive research from molecular markers to clinical implementation.

• Image Processing
• Computer Vision
• Artificial Intelligence
• +4 more

Shafiullah Soomro serves as Associate Professor in the Department of Artificial Intelligence at Quaid-e-Awam University of Engineering Science and Technology, Pakistan. He completed his Ph.D. in Application Software from Chung-Ang University, South Korea (2018), where he was honored as Best PhD Graduate. Ph.D., Application Software, Chung-Ang University (2018) His research spans medical image segmentation, computer vision, and AI applications in environmental monitoring. Specializing in automatic segmentation techniques using machine learning, he combines theoretical frameworks with practical biomedical implementations. Recent work integrates Swedish National Forest Inventory data with airborne laser scanning for forest attribute prediction. Current research projects include ForestMap (global forest cartography), AI-driven tree volume measurement (Sweden-Brazil collaboration), and machine learning models for predicting mechanical properties of oxynitride glasses. Best PhD Graduate, Chung-Ang University Dr. Soomro has mentored multiple MS students across Korea and Pakistan while supervising 2 Master's and 1 PhD students currently. His teaching portfolio includes 11 undergraduate, 2 MS, and 1 PhD courses such as Artificial Intelligence, Digital Image Processing, and Advanced Image Processing/Computer Vision. He actively contributes to academic curriculum design and research community development within the Computer Science and Artificial Intelligence department.

• Nanoparticle characterization
• Holographic microscopy
• Machine learning applications in physics
• +5 more

Daniel Midtvedt is a Senior Lecturer in the Department of Physics at the University of Gothenburg. His research bridges physics, nanotechnology, and machine learning, focusing on advanced optical techniques for nanoparticle characterization and biological applications. His research interests include: Nanoparticle characterization using holographic and interferometric microscopy Machine learning applications for microscopy data analysis Label-free detection methods for biological nanoparticles Light-matter interactions at the nanoscale Development of optical sensors for environmental monitoring Analysis of his publication record from 2020-2025 reveals a strong interdisciplinary approach combining physics, nanotechnology, and machine learning. His work frequently appears in high-impact journals including Nature Communications, Nano Letters, and ACS Nano, demonstrating significant contributions to the field of optical microscopy and nanoparticle analysis. Midtvedt's research shows consistent progression in developing increasingly sophisticated methods for characterizing nanoscale phenomena. Daniel Midtvedt maintains active collaborations with researchers including Giovanni Volpe and Fredrik Hook, indicating strong integration within the physics and biophysics research community at the University of Gothenburg. His work demonstrates both theoretical and practical contributions to the field of nanoscale optical characterization.

• Computer Science
• Dependable Communication Systems
• Computation Systems
• +2 more

Håkan Jonsson is a Senior Lecturer at Luleå University of Technology, working within the Department of Computer Science, Electrical and Space Engineering. His academic appointment is specifically in the Computer Science division, where he focuses on Dependable Communication and Computation Systems as his primary research subject. Dr. Jonsson's research spans two complementary domains: educational methodologies in computer science education and technical computer science research. His educational work explores innovative teaching approaches including flipped classrooms, peer discussion techniques, and just-in-time teaching methods, particularly in programming education. His technical research encompasses image analysis, morphological filtering, and data structuring techniques with applications in materials science and personal data management. His publication record shows consistent output across multiple venues, with recent work appearing in IEEE Frontiers in Education Conference proceedings and Pattern Recognition journal. His research demonstrates both theoretical rigor and practical application, particularly in bridging educational theory with classroom implementation. As a Senior Lecturer, Dr. Jonsson likely plays a significant role in curriculum development and teaching within the computer science program, with particular expertise in programming education and dependable systems.

Viet Thuy VuView profile

Associate Professor

• SAR signal processing
• SAR change detection
• SAR GMTI
• +3 more

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 AbidView profile

Research Fellow

• Machine Learning
• Unsupervised Curriculum Learning
• Computer Vision
• +4 more

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.

Erik SchaffernichtView profile

Senior Lecturer

• Robotics
• Sensor Systems
• Machine Learning
• +6 more

Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).

• Neuroradiology
• Magnetic Resonance Imaging
• Fetal Imaging
• +5 more

Johan Wikström is a Professor in Neuroradiology at the Department of Surgical Sciences, Uppsala University and Akademiska sjukhuset (Uppsala University Hospital). His research spans multiple domains of medical imaging with a focus on magnetic resonance techniques applied to clinical problems in obstetrics, neurology, and women's health. Wikström's research interests center on advanced MRI applications, particularly in fetal imaging, brain disorders, and vascular conditions. He has made significant contributions to understanding preeclampsia through neuroimaging approaches, examining cerebral changes in this pregnancy complication. His work also extends to premenstrual dysphoric disorder, where he investigates structural and functional brain changes related to hormonal fluctuations. Additionally, he has pioneered techniques in fetal MRI, establishing protocols for second-trimester imaging and developing methods for placental perfusion assessment. The analysis of his recent publications reveals a strong focus on the intersection of artificial intelligence and medical imaging, particularly in neuro-oncology applications. His work demonstrates a clear trajectory toward integrating advanced computational methods with traditional radiological techniques to improve diagnostic accuracy and treatment monitoring. There's also a consistent theme of applying sophisticated MRI techniques to address specific clinical challenges in women's health and neurovascular disorders. Wikström actively collaborates with researchers across multiple disciplines including obstetrics, neurosurgery, psychiatry, and biomedical engineering. His recent work shows increasing integration of AI methodologies with traditional imaging approaches, reflecting broader trends in medical imaging research.

Alexandros SopasakisView profile

Associate Professor

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +3 more

Alexandros Sopasakis serves as an Associate Professor in the Department of Mathematics within the Faculty of Engineering at Lund University. Additionally, he holds dual roles as Application Expert for national research infrastructures CIPA (Correlative Image Processing and Analysis) and InfraVis (National Research Infrastructure for Data Visualization). His research expertise spans Mathematical Computer Vision and Machine Learning, with specialized focus on Machine Learning-based segmentation and annotation tool development. Current projects include segmenting glomeruli from synchrotron X-ray microtomography datasets and investigating diabetes through rat models with specific genetic mutations, revealing consistent organ enlargement regardless of gender or blood sugar levels. His work bridges theoretical mathematics with practical medical imaging applications in biological research. No scientific awards were documented in the source material. Similarly, graduate student mentorship details and research grant information remain unspecified. Dr. Sopasakis actively contributes to national research ecosystems through CIPA and InfraVis, providing critical expertise in image processing pipelines and data visualization solutions for complex scientific datasets.

Kalle ÅströmView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +2 more

Kalle Åström is a Professor at Lund University and serves as an application expert for the InfraVis project, focusing on infrastructure visualization applications. His research spans Artificial Intelligence with specialized expertise in Computer Vision , Machine Learning , and Image Processing . He applies these disciplines to infrastructure analysis, demonstrating extensive experience in developing and implementing AI-driven visualization tools for complex infrastructure systems. As an InfraVis application expert, he bridges theoretical AI research with practical infrastructure management solutions, emphasizing tool development for real-world engineering challenges.