جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Radiation Detectors
• Biomedical Engineering
• +۳ مورد دیگر

Mats Danielsson is a Professor at KTH Royal Institute of Technology, leading the Medical Imaging research group within the Department of Particle Astrophysics and Medical Imaging. He has coordinated major projects like the ERC Advanced Grant for the Si3 project (starting 2024) and the EIC Pathfinder's 1MICRON project (starting 2025). His work focuses on advancing photon-counting detectors, X-ray technologies, and medical imaging systems. Notable recognitions include the 2024 KTH Innovation Award and the 2022 Hans Wigzell Science Prize. Danielsson has co-founded companies such as Sectra Mamea AB and C-RAD AB, and holds 135 patents with over 150 scientific publications. Education: MSc (1990) and PhD (1996) from KTH, followed by postdoctoral research at Lawrence Berkeley National Lab (1996–1998). He joined KTH in 1999, where he has held his current professorship since then. His research spans medical imaging, detector innovation, and radiation physics applications in healthcare. Research Interests: Development of high-resolution CT detectors, photon-counting technologies, compact X-ray sources, and AI-driven image processing. His recent work emphasizes minimizing radiation exposure while enhancing diagnostic precision through novel detector designs and machine learning algorithms. Key Projects: ERC Si3 project (3D detector for nuclear medicine), EIC 1MICRON (micrometer-scale imaging), and MedTechLabs collaboration with Karolinska Institutet. He has pioneered innovations such as MicroDose mammography and advanced photon-counting spectral CT systems. Awards: KTH Innovation Award (2024), Hans Wigzell Prize (2022), IVA membership (2017), Polhem finalist (2014), and INGVAR Award (2004). Advising & Grants: Over 150 scientific publications, 135 patents, and leadership in multi-institutional projects. Teaches courses on medical imaging and modern physics at KTH. Labs/Teams: Director of the Medical Imaging Group at KTH, co-founder of MedTechLabs, and collaborator across academia and industry in medical imaging innovation.

Xiaoming Huمشاهده پروفایل

استاد

• Multi-agent systems
• Nonlinear control systems
• Systems theory
• +۴ مورد دیگر

Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.

Niclas Abrahamssonمشاهده پروفایل

استاد

• Second Language Acquisition (SLA)
• Bilingualism Research
• Phonetics and Phonology
• +۷ مورد دیگر

Niclas Abrahamsson is a Professor of Swedish as a Second Language at Stockholm University, where he also serves as the Director of the Centre for Research on Bilingualism . His research focuses on second language acquisition (SLA) , bilingualism , and phonological/phonetic development , particularly through the lens of age of acquisition and critical periods . He leads the MOB (Meta-research on Bilingualism) group, analyzing trends in bilingualism research and Swedish as a Second Language. PhD in Bilingualism (2001, Stockholm University) BA in Linguistics, Phonetics, and Psychology (1993) His research explores how age of onset and language aptitude influence nativelike attainment in L2 speakers, with a focus on voice onset time (VOT) analysis, neural constraints , and lexical deficits in bilinguals. His recent studies challenge the notion that bilingualism inherently causes linguistic costs, attributing observed deficits instead to second language acquisition processes. Abrahamsson has secured major grants from the Swedish Research Council (VR) , Bank of Sweden Tercentenary Foundation (RJ) , and Byggmästare Olle Engqvists Stiftelse . He supervises PhD students working on topics like Swedish compounding acquisition , language-dependent memory , and foreign accented speech perception . He teaches SLA, bilingual development, and psycholinguistics at all academic levels. Key Awards : VR Grant 2016-01630, RJ Sabbatical Grant SAB16-0051:1, Byggmästare Olle Engqvists Stiftelse Grant 200-0676 Abrahamsson's work frequently employs ERP studies and meta-analyses , emphasizing methodological rigor. He collaborates with institutions like the Multilingualism Lab and has contributed to foundational theories in critical period hypothesis and language aptitude research.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Saleh Javadiمشاهده پروفایل

مدرس ارشد

• Systems engineering
• Signal processing
• Machine learning
• +۱ مورد دیگر

Saleh Javadi is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He is actively engaged in research and teaching within the field of systems engineering. His educational background includes: B.Sc. in Electrical-Control Engineering from Amirkabir University of Technology (2009) M.Sc. in Electrical, Electronic and Systems Engineering from The National University of Malaysia (2013) Ph.D. in Systems Engineering from Blekinge Institute of Technology (BTH) (2021) Saleh Javadi's research focuses on signal processing, machine learning, and computer vision , with applications spanning remote sensing, intelligent transportation systems, and AI-driven industrial optimization. His work bridges theoretical advancements with practical implementations, particularly in SAR imagery analysis, drone-based agricultural monitoring, and traffic surveillance systems. His recent publications demonstrate a strong focus on remote sensing technologies, particularly Synthetic Aperture Radar (SAR) image processing and analysis. There's a clear trend toward applying machine learning techniques to solve complex problems in aerial and satellite imagery, traffic monitoring, and agricultural applications. His research shows interdisciplinary connections between computer vision, signal processing, and practical engineering applications. Saleh Javadi has received significant recognition for his innovative work: Innovator of the Year award (SKAPA – Innovation Prize in Memory of Alfred Nobel) in Blekinge for innovative efforts in optimizing and reducing energy consumption in industries by using artificial intelligence ÅForsk Entrepreneur's prize at the Swedish Innovation Council Day – Swedish Incubators & Science Park's annual conference in May 2019 Dr. Javadi is involved in practical applications of his research through projects such as "Artificiell intelligens AI kan reducera ogräsfrön i utsäde" (ongoing) and "Bekämpa Renkavle med hjälp av drönare och Artificiell Intelligens (AI)" (completed). His work demonstrates a strong commitment to translating academic research into real-world solutions that address industrial and environmental challenges. His research appears to be conducted within a collaborative framework, working with colleagues on drone technology, SAR image analysis, and AI applications across multiple domains including agriculture, maritime monitoring, and transportation systems.

Ying Wangمشاهده پروفایل

دانشیار

• English for academic purposes
• Applied corpus linguistics
• Second language writing
• +۴ مورد دیگر

Ying Wang is an Associate Professor in English linguistics at Karlstad University since 2020, specializing in English for academic purposes, applied corpus linguistics, and second language writing. She holds a PhD from Uppsala University (2013) and has taught courses at both undergraduate and graduate levels focusing on academic writing, second language pedagogy, and corpus methodology. Her research explores rhetorical structures in disciplinary genres, evaluative language resources, and the impact of extramural English activities on L2 writing development. Notable projects include the Swedish Learner English Corpus (SLEC) initiative and analyses of predatory publishing practices in political science. She has also examined government communication strategies during the UK's COVID-19 pandemic response through corpus-assisted discourse studies. Key research contributions span formulaic language use in ELF contexts, methodological innovations in corpus linguistics, and linguistic comparisons between well-established and predatory journals. Her work bridges theoretical linguistics with practical applications in education and scholarly publishing ethics. Publications span prestigious journals like English for Specific Purposes , Text & Talk , and Journal of Second Language Writing , reflecting her interdisciplinary approach to language studies. Current projects emphasize corpus-driven research on academic communication practices and their pedagogical implications.

Dilian Gurovمشاهده پروفایل

استاد

• Software Specification and Verification
• Multi-Agent Strategic Planning
• Formal Methods
• +۳ مورد دیگر

Dilian Gurov is a Professor in Computer Science at KTH Royal Institute of Technology, associated with the Digital Futures Faculty and the Division of Theoretical Computer Science. He also coordinates the Doctoral Programme in Computer Science at the CSC school. Before joining KTH in 2002, he earned a Ph.D. from the University of Victoria, Canada (1998), and worked at the Swedish Institute of Computer Science (1997-2002). His research focuses on software specification and verification, including contracts, program models, logics, and tools, as well as multi-agent strategic planning involving knowledge-based strategies in imperfect information settings. Key contributions include the CAV Distinguished Paper Award 2023 for 'Automatic Program Instrumentation for Automatic Verification' and an EASST award for 'Checking Absence of Illicit Applet Interactions: A Case Study' (2004). He leads projects funded by VR (SEFROS, ContraST) and Vinnova (AVerT2) and collaborates with industries like Scania on formal verification of C programs. His service roles span over 30 conference committees and organization roles, including PC memberships for iFM, TAP, and ISoLA. Teaching responsibilities include courses such as 'Formal Methods,' 'Program Semantics and Analysis,' and 'Knowledge in Games with Imperfect Information.' His work emphasizes practical applications of formal methods, bridging academic research with industry needs through collaborations and tool development (e.g., CVPP, ProMoVer, TriCo).

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +۴ مورد دیگر

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

Markus H. Flierlمشاهده پروفایل

دانشیار

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

• Neurology
• Neurosciences
• Radiology and Medical Imaging
• +۱۰ مورد دیگر

Anna Falk Delgado is an Associate Professor (Docent) in Radiology at Karolinska Institutet, affiliated with the Department of Clinical Neuroscience. She leads the Clinical Brain Imaging Methods research group and focuses on advanced neuroimaging techniques. University Medical Degree, Karolinska Institutet, 2009 Docent, Karolinska Institutet, 2021 Affiliated to Research, Department of Clinical Neuroscience, 2024-2027 Her research spans Neurology , Neurosciences , and Radiology and Medical Imaging , with expertise in PET/MR , Cone-Beam CT , Brain Tumor Imaging , and Stroke Radiology . She pioneered ultra-fast multi-contrast MRI protocols and dual-layer CT angiography for stroke evaluation. Recent publications emphasize technical innovations in hybrid imaging, stroke triage optimization, and pediatric neuroimaging safety. Her work integrates machine learning , radiation reduction , and clinical translation of imaging technologies.

• Nursing Education
• Clinical Practice Learning
• Caritative Caring
• +۳ مورد دیگر

Maria Koldestam is a researcher at the Department of Health and Caring Sciences within the Faculty of Health and Life Sciences at Linnaeus University, specializing in nursing education and caritative caring frameworks for clinical practice learning. Her research focuses on: Development and evaluation of the MILO (Model for Improvements in Learning Outcomes) conceptual model grounded in caritative caring Undergraduate nursing students' learning experiences during clinical practice Children's speech intonation patterns and forest-based preventive health interventions Integration of caring and learning as parallel processes in educational settings Analysis of her 2021-2025 publications reveals a consistent methodological emphasis on phenomenographic and cross-sectional approaches to examine student perceptions, supervisor experiences, and semester-based learning progression within nursing education. Her work bridges theoretical caritative caring principles with practical clinical training applications. She leads two major research projects: "Learning to focus" investigating contrastive intonation in Stockholm and Skåne Swedish children, and "The forest – an active preventive health care for children?" implementing forest-based school interventions for grades 3-6. Koldestam is also affiliated with the "Connected children" research centre focused on knowledge-based prevention strategies.

Claes Lundströmمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Artificial Intelligence
• Digital Pathology
• +۴ مورد دیگر

Claes Lundström is an Adjunct Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) school. His primary research focuses on medical imaging, integrating machine learning, visualization, and human-computer interaction in clinical settings. He leads the technical side of digital pathology research at the Center for Medical Image Science and Visualization (CMIV), directing national-scale grants and serving as Arena Director for the Analytic Imaging Diagnostics Arena (AIDA). Since 2010, he has held dual roles as Research Director at Sectra AB and academic researcher, driving innovations that translate into commercial healthcare solutions. Lundström's work spans AI-driven diagnostics, uncertainty visualization, and precision orthopedics, with contributions recognized through leadership in major projects like the €70M BIGPICTURE initiative. His academic credentials include a PhD (2007) and Docent degree (2014), alongside extensive industry collaboration. Education: PhD in 2007 and Docent degree in 2014 from Linköping University. PhD: 2007 Docent: 2014 Research interests emphasize AI integration in diagnostics, particularly digital pathology and medical imaging workflows. His work addresses challenges like domain adaptation in AI models, uncertainty quantification in segmentation tasks, and interactive visualization tools for clinicians. Recent projects include the VAI-B platform for AI validation in breast imaging and the BigPICTURE collaboration for global pathology data sharing. Key grants include leadership roles in national-scale projects, such as the AIDA arena and SCAPIS study-based AI lab. His contributions to AI ethics and clinical adoption include studies on human-AI collaboration and quality assurance frameworks. Lundström also oversees labs like CMIV and AIDA, fostering cross-disciplinary research in medical imaging and AI.

Mariela Morveliمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computer Science
• Argumentation
• +۶ مورد دیگر

Mariela Morveli is a postdoctoral researcher in Artificial Intelligence at Umeå University's Department of Computing Science. She works within the Formal Methods for Trustworthy Hybrid Intelligence research group, focusing on argumentation frameworks, intelligent agents, and explainable AI. Education: Ph.D., Federal University of Technology of Parana (Brazil) M.Sc., Federal University of Rio de Janeiro (Brazil) B.Sc., National University of San Agustin of Arequipa (Peru) Her research spans argumentation theory, multi-agent systems, and explainable AI, with emphasis on goal reasoning, activity reasoning, and handling uncertainty through formal methods. She has developed frameworks like QuAD-V for transparent task delegation and explored probabilistic models for argumentation systems. Recent work trends show strong focus on contrastive explanations , gradual semantics , and hybrid reasoning mechanisms that integrate symbolic and probabilistic approaches. Her publications address challenges in human-agent interaction , trust modeling , and conflict resolution in dynamic environments. She contributes to the field through rule-based argumentation systems , rhetorical argument strength calculations , and neuro-symbolic architectures like SATyrus for constraint processing.