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Kåre Synnes is a Professor in Pervasive and Mobile Computing at Luleå University of Technology, where he is affiliated with the Department of Computer Science, Electrical and Space Engineering. He is an active researcher and educator, recognized as an "Excellent lärare" (Excellent Teacher) and maintains his office in Luleå at location A3305a. Professor Synnes conducts research in pervasive computing and multimedia communication, with significant applications in eHealth. His work bridges theoretical computer science with practical implementations in distributed systems. He is also an affiliated researcher at the Centre for Distance-spanning Technology (CDT), focusing on technologies that enable communication across geographical boundaries. His recent publications demonstrate a clear trajectory toward sustainable computing solutions, with emphasis on circular economy applications, supply chain management, and environmental impact assessment. His research integrates computer science with economic and sustainability principles, showing how digital technologies can address contemporary environmental challenges through innovative auction systems, behavior analytics, and resource management frameworks. IST Prize winner as co-founder of Marratech AB Synnes has been deeply involved in European research projects since 1995, including multiple Framework Programme projects (FP4 through FP7). His work as an expert/reviewer for European projects since 2009 demonstrates his standing in the research community. His entrepreneurial success with Marratech AB, which was acquired by Google to form the basis for Google+ Hangouts, shows his ability to translate research into impactful commercial applications. Professor Synnes maintains strong connections with industry through his European project work and his entrepreneurial experience. His research group likely focuses on pervasive computing applications with real-world implementations, particularly in eHealth contexts, though specific lab details aren't provided in the available information.

• Pervasive Computing
• Mobile Computing
• Distributed Systems
• +3 more

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +2 more

Thomas Winkler is an Associate Professor at the Division of Micro and Nanosystems, KTH Royal Institute of Technology, Sweden, and collaborates with TU Braunschweig, Germany. His research focuses on solving life science challenges using microsystems tools, particularly in neuropsychiatric disorders like schizophrenia. He develops organ-on-chip models, engineered microfluidic platforms, and biosensors for point-of-care diagnostics. Winkler leads an interdisciplinary ERC-funded team addressing metabolic coupling in neurovascular units and oxidative stress biomarkers. Key achievements include the ERC Starting Grant (2023) and work on electrochemical sensors for clozapine monitoring. He teaches courses such as Microsystem Technology (EK2350) and supervises PhD and postdoctoral researchers. Current projects include machine learning-guided robotic organoid maturation and electrochemical technology development for the CHIPzophrenia initiative. His lab actively seeks talent through open positions in Stockholm and Braunschweig. Scientific awards include the ERC Starting Grant and Marie Skłodowska-Curie Actions Fellowship. Research spans sensor development, microfabrication, and biomaterials, with a focus on translating lab technologies to clinical applications. Collaborations bridge engineering and life sciences, emphasizing personalized mental healthcare solutions.

• Microsystems Technology
• Organ-on-Chip
• Neuropsychiatric Disorders
• +3 more

Magnus Boman is a Professor of AI and Health at the Department of Medicine, Solna, Karolinska Institutet (KI), where he leads the AI@KI initiative to support researchers in AI integration. He is affiliated with the Chronic Inflammatory Disease Epidemiology research group under Johan Askling. His research focuses on AI applications in precision medicine, multimodal prediction, ethical norms in AI systems, energy-efficient computing, and quantum sensor data interpretation. Research Interests: Artificial Intelligence in healthcare and precision medicine Multimodal data analysis for disease prediction and treatment Machine learning for clinical decision support systems Ethical and societal implications of AI Grants: Swedish Research Council: Improving breast cancer histology image classification (2024-2026) Scalable Federated Learning (2022-2025) Ai in sustainable cities (VINNOVA, 2019) Advising & Students: Supervised over 50 PhD and Master's students across KI, KTH, and Stockholm University, focusing on AI applications in healthcare, machine learning, and computational epidemiology. Notable projects include predictive modeling for mental health outcomes and variant filtering in genetic data. Labs & Teams: Leads AI@KI, fostering AI adoption in medical research. Collaborates with the Johan Askling group on epidemiology and chronic disease studies.

• Artificial Intelligence in Healthcare
• Precision Medicine
• Machine Learning Applications in Healthcare
• +5 more

Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.

• Distributed Systems
• Machine Learning
• Big Data
• +3 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Zenun Kastrati is an Associate Professor at the Department of Informatics, Linnaeus University. His research focuses on Artificial Intelligence, Natural Language Processing, Machine Learning, Semantic Web, Sentiment Analysis, and Learning Technologies. He contributes to the Data-driven Business Innovation (DBI) and Interaction Design Research Groups, leading projects like Forest 4.0, RAPID, and IGNITE. His recent work involves Explainable AI, medical imaging, and multilingual NLP. Ph.D. in Computer Science (NTNU, 2018) Master's in Computer Science (EU TEMPUS Programme) Previous Lecturer/Researcher at University of Prishtina His research spans AI applications in medical diagnostics , NLP , sentiment analysis , and semantic technologies . Key projects include Forest 4.0 (environment monitoring) and RAPID (online education in Pakistan). Publications highlight his expertise in deep learning , transformer models , and context-aware systems . Recent publications demonstrate trends in Explainable AI (XAI) for healthcare, medical imaging techniques, and multilingual NLP frameworks. Other work explores social media analytics , student feedback analysis , and pedagogical document classification . Zenun's teaching includes Fundamentals of Programming , Object-Oriented Programming , Web Applications , Data Analytics , and Adaptive Web courses at BSc and MSc levels.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +4 more

Neda Haj Hosseini is a Senior Lecturer and Associate Professor in Biomedical Engineering at Linköping University's Department of Biomedical Engineering (IMT) . She contributes to teaching courses like TBMT56 - Medical Technology and TBME08 - Biomedical Modeling and Simulation , while leading research initiatives in AI-driven cancer diagnostics and biomedical optics. Research Focus: Development of AI methods for cancer diagnostics, optical coherence tomography (OCT) applications, and fluorescence spectroscopy in surgical guidance Affiliations: Center for Medical Image Science and Visualization (CMIV) , Analytic Imaging Diagnostic Arena (AIDA) , Swedish Medical Technology Association Recent Research Trends demonstrate expertise in applying deep learning to: Pediatric brain tumor classification using multimodal imaging Optical biopsy techniques for intraoperative decision support Automated biomarker quantification in histopathology Medical imaging data integrity and algorithm validation Scientific Awards include grants from: Joanna Cocozza Foundation (2022) Swedish Childhood Cancer Foundation (2024) Academic Leadership involves mentoring students in projects such as: "Multiple Instance Attention-based Learning for Brain Tumor Classification" "Vision Transformers for Multiclass Brain Tumor Tissue Classification" "Reaction-diffusion Models for Image-driven Tumor Simulation"

• Biomedical Engineering
• Medical Imaging
• Artificial Intelligence in Healthcare
• +3 more

Tommy Lundberg is a Senior Lecturer and Docent in Physiology at Karolinska Institutet. He works at the Department of Laboratory Medicine, Division of Clinical Physiology. His email address is tommy.lundberg@ki.se, and his postal address is H5 Laboratoriomedicin, H5 Klinisk Fysiologi Gustafsson, 141 52 Huddinge. Lundberg is affiliated with the university library and has held positions since 2022. Research Interests: Lundberg's research focuses on skeletal muscle mass and function adaptation, particularly in athletic performance, disease contexts, aging, and transgender individuals undergoing hormone therapy. He investigates molecular, metabolic, morphological, and functional responses to resistance and aerobic exercises. His work also explores biological maturity selection biases in youth sports, bio-banding applications in soccer and ice hockey, and the impact of anti-inflammatory drugs on muscle hypertrophy. Article Trends: Lundberg's recent publications (2025–2024) cover topics like sex differences in disc golf, muscle atrophy in space exposome, longitudinal hormone therapy effects in transgender individuals, and bio-banding in youth sports. Earlier works delve into molecular pathways in muscle hypertrophy, concurrent training effects, mitochondrial function, and imaging techniques (CT/MRI) for muscle assessment. Scientific Recognition: Most prominent young researcher in Sport Science, Swedish Central Association for Sport Promotion (SCIF), 2017 Teaching and Editorial Roles: Lundberg teaches human physiology and sports science in nursing, physiotherapy, and biomedical analytics programs. He leads a contract education course in advanced exercise physiology and contributes to a PhD course on scientific writing. He is an Associate Editor for Frontiers in Physiology - Exercise Physiology (2022) and a member of the editorial board for Translational Exercise Biomedicine (2024). Collaborations and Expertise: He collaborates with the Swedish Football Association and Swedish Ice Hockey Association on bio-banding studies. Lundberg served as an invited speaker at the ACSM Annual Meeting (2024) on transgender athletes and as an expert panelist for World Rugby's transgender workshop (2020). His supervision includes Andrea Tryfonos (2021) and thesis evaluations at Mid Sweden University and Linköping University.

• Physiology
• Sports Science
• Molecular Biology
• +3 more

Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)

• Total Productive Maintenance
• Artificial Intelligence
• Multi-Criteria Decision Making
• +3 more

Professor Maria Eriksdotter is a leading academic in geriatrics and dementia research at the Karolinska Institutet , holding the Department of Neurobiology, Care Sciences and Society . She also serves as a Senior Consultant in Themes Inflammation and Ageing at Karolinska University Hospital Huddinge and previously as Dean of KI South (2019–2023). Her work spans translational research, clinical trials, and national registry development, with a focus on Alzheimer's disease, cholinergic therapies, and aging. Her research group pioneered NGF cell therapy for Alzheimer's patients, demonstrating safety and cognitive stabilization in clinical trials. She chairs the SveDem registry , tracking over 100,000 dementia patients to refine diagnostics and care. Studies from SveDem revealed mortality reduction with cholinesterase inhibitors and highlighted pandemic-era diagnostic delays. Recent publications analyze dementia subtypes , comorbidities , and precision medicine in neurodegeneration. Her work intersects neuroimaging , epidemiology , and public health policy , addressing ageism and improving geriatric care systems. Collaborations span Karolinska University Hospital , NSGene Inc , and international institutions.

• Alzheimer's Disease
• Geriatrics
• Dementia
• +6 more

Lars Hanson is a Professor of Product Design Engineering at the University of Skövde's School of Engineering Science. His research focuses on ergonomics, digital human modeling, and optimizing manufacturing systems with a strong emphasis on human well-being and sustainable production. He leads projects like LITMUS (Industry 4.0 to 5.0 transition) and has contributed to developing tools such as IPS IMMA for ergonomic simulations. Active in virtual verification of human-robot collaboration and smart textile systems for workplace safety Published extensively in journals like International Journal of Human Factors Modelling and Simulation and IEEE Access Editor of conference proceedings and contributor to industry standards in automotive and healthcare sectors Research interests include multi-objective optimization of factory layouts, musculoskeletal risk assessment, and integrating ergonomic evaluations into product design processes. Current projects address Industry 5.0 sustainability challenges through digital twin technologies and smart manufacturing solutions.

• Ergonomics
• Digital Human Modeling
• Manufacturing Systems
• +5 more

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

• Data Science
• Machine Learning
• Time Series Analysis
• +5 more

Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.

• Electromagnetic Inverse Problems
• Material Characterization
• Biomedical Imaging
• +3 more

Anna Johansson is an Assistant Professor at the Karolinska Institutet , leading research in cancer epidemiology and biostatistics at the Department of Medical Epidemiology and Biostatistics . She serves as a Docent and group leader, with affiliations at Karolinska University Hospital, Capio St Göran Hospital, and Uppsala University Hospital. Research Interests: Her work spans Cancer epidemiology with focus on breast cancer Reproductive factors and cancer risk Socioeconomic inequalities in healthcare Applied biostatistics and register-based methods Nordic cross-country collaborations Recent Article Trends show emphases on Nordic cancer survival comparisons Pregnancy-associated cancer outcomes Stage-specific mortality analysis Impact of healthcare disruptions Statistical methods for registry data Fertility preservation in oncology Scientific Awards Senior Investigator Award, Swedish Cancer Society Teaching & Mentorship includes survival analysis, epidemiological designs, and data management workshops within the Karolinska Doctoral Programme. She supervises PhD students in cancer and reproductive epidemiology. Collaborative Networks extend to the Nordic Cancer Union, Cancer Registry of Norway, and clinical research teams across Sweden and the Nordic region.

• Cancer Epidemiology
• Breast Cancer
• Reproductive Epidemiology
• +3 more