جست‌وجوی استاد و پژوهشگر

• Information Visualization
• Visual Analytics
• Multidimensional Data
• +۵ مورد دیگر

Rafael Messias Martins is a Researcher at Linnaeus University, affiliated with the Department of Computer Science and Media Technology within the Faculty of Technology. He holds an MSc in Computer Science from the University of São Paulo and a PhD in Computer Science from the University of Groningen. His primary research focuses on Information Visualization and Visual Analytics, particularly emphasizing Multidimensional Data and Networks. He is a core member of the Information and Software Visualization (ISOVIS) research group and leads multiple ongoing and completed research projects, including InfraVis (a national research infrastructure for data visualization) and initiatives addressing medication risks and carbon mitigation in forestry. His work bridges theoretical advancements in visualization with practical applications in education, healthcare, and environmental science. Education MSc in Computer Science, University of São Paulo, Brazil PhD in Computer Science, University of Groningen, Netherlands Research Interests His research explores the intersection of visualization techniques with complex data analysis, emphasizing: Interactive visual analytics for high-dimensional data Machine learning interpretability through visualization Educational data analytics for K-12 institutions Applications in healthcare (e.g., medication risk prediction) and environmental science (e.g., carbon footprint reduction) Development of national visualization infrastructures (InfraVis) Recent Trends in Articles His recent work emphasizes: Enhancing trust in machine learning models through visual explanations Optimizing visualization tools for educational stakeholders Algorithmic fairness in urban planning simulations Scalable dimensionality reduction techniques for streaming data Grants & Collaborations He has coordinated projects such as IDEAL (interaction design curriculum development), TimberVis (3D timber structure visualization), and seed projects addressing carbon mitigation and medication risks. Collaborations span academic, industrial, and governmental partners in Sweden and internationally. Labs & Teams He leads the ISOVIS group, which develops open-source tools like SBGTool (student grouping analytics) and FeatureEnVi (feature engineering visualization). The group also contributes to InfraVis, a national platform for visualization resources.

Håkan Geijerمشاهده پروفایل

استاد مدعو

• Medical Radiology
• Radiation Dosimetry
• Image Quality Optimization
• +۶ مورد دیگر

Håkan Geijer is an Adjunct Professor of Medical Science at Örebro University, affiliated with the Department of Health Sciences. He holds a PhD in Medical Science from Linköping University (2001) and became an Associate Professor at Örebro University in 2006. His primary role is as a senior physician at Örebro University Hospital’s Radiology Department, where he specializes in medical radiology and nuclear medicine. Education: Bachelor of Medicine, Gothenburg University PhD in Medical Science (2001), Linköping University Associate Professor (Docent) in Medical Science (2006) Adjunct Professor since 2018 Research Interests: Dr. Geijer’s research focuses on optimizing radiation dose and image quality in radiological practices, with recent emphasis on computed tomography (CT). He investigates vein graft patency in cardiac surgery and evaluates diagnostic imaging resource utilization. His work bridges clinical practice and radiological innovation, addressing radiation safety and imaging efficacy. Publications: His 15 most recent articles reflect advancements in coronary artery bypass graft outcomes, radiation dose reduction techniques, and patient experience in diagnostic imaging. Key topics include no-touch vein graft superiority, CT applications in cardiology, and algorithm-driven diagnostic protocols. Grants & Collaborations: Collaborates with thoracic surgery teams on vein graft studies and contributes to evidence-based medicine initiatives in Region Örebro County. His work often involves multidisciplinary teams analyzing imaging workflows and patient outcomes. Labs & Teams: Active in medical imaging research groups at Örebro University, focusing on CT optimization and interventional radiology techniques.

• Computer Graphics
• Real-Time Rendering
• Algorithms
• +۳ مورد دیگر

Tomas Akenine-Möller is a Professor in the Department of Computer Science at Lund University, affiliated with the Faculty of Engineering (LTH). He is also part of ELLIIT, the Linköping-Lund initiative on IT and mobile communication. His research focuses on real-time rendering, computer graphics algorithms, and human vision modeling, contributing to UN Sustainable Development Goals through technological advancements. Education details are not explicitly provided in the text, but his expertise stems from a strong academic background in computer science and engineering. His research interests include algorithms optimization, ray-tracing techniques, graphic hardware performance, and visual perception models. Recent work trends involve temporal edge detection in human vision, neural compression of material textures, and luminance-preservation in graphics. His projects emphasize practical applications of graphics algorithms in real-world scenarios. No scientific awards are explicitly listed. However, he has supervised doctoral research, including the project 'Evaluating and Improving Rendered Visual Experiences' (2019-2024), where he acted as an assistant supervisor. This project explores enhancing visual rendering quality and user experience. No specific lab or team affiliations are mentioned, but his collaborative networks span multiple countries, reflecting interdisciplinary research partnerships.

Magnus Johannessonمشاهده پروفایل

استاد

• Metascience
• Experimental Economics
• Genoeconomics
• +۳ مورد دیگر

Magnus Johannesson is a Professor and Chaired Professor in the Department of Economics at the Stockholm School of Economics (SSE). His research focuses on Metascience , Experimental Economics , and Genoeconomics , with emphasis on topics like research reproducibility, genetic influences on behavior, and experimental design. He has contributed to high-impact journals such as Nature , Science , and PNAS . Research Interests: Replicability and reproducibility in social sciences Genetic correlates of economic and political preferences Experimental methodologies in behavioral economics Recent work highlights efforts to address analytical flexibility in medical research, evaluate the robustness of studies on gender diversity and economic behavior, and explore the genetic basis of educational attainment. His publications often challenge conventional findings through large-scale replication projects and meta-analyses. Notable contributions include the Estimating the Reproducibility of Psychological Science (2015) and Genome-wide association study identifies 74 loci associated with educational attainment (2016). He actively promotes methodological rigor through pre-registration, structured robustness tests, and open science practices. Professional networks include SSE’s Department of Economics, with collaborations on projects like the COVID-19 Preprint Replication Project . Contact: Email , Google Scholar .

• Mathematics
• Applied Mathematics
• Combinatorics
• +۳ مورد دیگر

Johan Björklund is a Lecturer at the University of Gävle. His research focuses on Mathematics, with a specialization in Combinatorics and Mathematics Education. He has contributed to peer-reviewed journals and presented at academic conferences. Research interests include subgraph counts in network models and integrating programming into mathematics teacher education. His work bridges theoretical mathematics and pedagogical innovation. Notable publications span topics like uniform attachment models in Combinatorics and collaborative teaching strategies in Mathematics Education. No scientific awards are explicitly mentioned in the provided data. He advises no listed students and has no mentioned grants or labs. Contact via johan.bjorklund@hig.se.

• Applied Mathematics
• Materials Science
• Computational Modeling
• +۳ مورد دیگر

Jens Wittsten is a Researcher affiliated with the Department of Engineering at the University of Borås' Academy of Textiles, Technology and Economics. He serves as the main supervisor for doctoral student Markus Klintborg and holds office in room C801. His work bridges applied mathematics, materials science, and computational engineering. Research interests include modeling phenomena in moiré heterostructures (e.g., twisted graphene layers), semiclassical quantization in strained lattices, and seismic data processing techniques. He has contributed to understanding electronic phase transitions, magic angles in bilayer graphene systems, and numerical methods for wave propagation modeling. His publication trends reflect interdisciplinary focus: recent works address both fundamental physics (e.g., Hofstadter butterfly studies) and applied engineering challenges (e.g., warehouse optimization via GPU-accelerated routing). Jens advises one doctoral candidate and maintains an active research portfolio spanning over 25 peer-reviewed articles since 2010. His methodological innovations include contributions to seismic apparition techniques and dealiasing algorithms.

Bengt Lindholmمشاهده پروفایل

دانشیار

• Renal Medicine
• Chronic Kidney Failure
• Peritoneal Dialysis
• +۴ مورد دیگر

Bengt Lindholm is an Associate Professor and Senior Research Specialist at the Karolinska Institutet , leading the Renal Medicine at Novum group within the Department of Clinical Science, Intervention and Technology. His work focuses on clinical research in renal medicine, particularly chronic kidney failure, cardiovascular complications, metabolic disorders, and peritoneal dialysis optimization. He maintains active collaborations with Karolinska University Hospital and the Polish Academy of Sciences. Education: MD, PhD, B.Sc. (Economics) Key Research Areas: Chronic kidney disease, dialysis-related complications, vascular calcification, and nutritional interventions Awards: Life-Time Achievement Award in Peritoneal Dialysis (2006) Honorary Member, Polish Society of Nephrology (1998) Honorary Member, Spanish Society of Nephrology (2012) Medal from Polish Academy of Sciences (2015) Distinguished Fellow of ERA-EDTA (2017) Bengt Rippe Research Prize (2022) With over 775 publications and an h-index of 113, Lindholm's research explores dialysis fluid composition, remote monitoring systems, and nutritional strategies for CKD patients. His team has published extensively on peritoneal dialysis efficiency, cardiovascular risk factors, and medical interventions for hyperkalemia.

• Breast cancer research
• Translational oncology
• Molecular analyses of tumor tissue
• +۷ مورد دیگر

Theodoros Foukakis serves as Associate Professor and Senior Physician at Karolinska Institutet's Department of Oncology-Pathology in Stockholm, Sweden. He leads the Translational Breast Cancer Research Group, a multidisciplinary team comprising clinicians, bioinformaticians, epidemiologists, biostatisticians, AI engineers, and molecular biologists. His research focuses on understanding treatment resistance mechanisms in breast cancer through molecular analyses of tumor tissue and blood samples, with the goal of developing new predictive biomarkers for treatment response. Foukakis' research interests center on translational breast cancer research, particularly in the areas of neoadjuvant therapy, treatment resistance mechanisms, and biomarker development. His work spans both HER2-positive and triple-negative breast cancer subtypes, with significant contributions to clinical trials investigating novel treatment approaches including immunotherapy combinations and antibody-drug conjugates. He has pioneered research in applying machine learning and AI techniques to analyze tumor microenvironment characteristics and optimize treatment selection based on molecular profiles. His work bridges clinical practice with cutting-edge genomic and computational approaches to personalize breast cancer treatment. Analysis of his recent publications (2024-2025) reveals a strong focus on precision oncology approaches, with particular emphasis on biomarker development, treatment response prediction, and optimizing therapeutic strategies through AI applications. His work spans clinical trials (PANTHER, KEYNOTE, ARIADNE), biomarker discovery (SAMHD1, immune microenvironment characterization), and implementation of machine learning models for treatment optimization. A significant portion of his research addresses the critical challenge of balancing treatment efficacy with minimizing toxicity through tailored approaches. Foukakis actively mentors doctoral students and postdoctoral researchers in his translational breast cancer research group. His grant portfolio includes leadership of the ARIADNE trial, a randomized phase IIB study investigating trastuzumab deruxtecan and biology-driven selection of neoadjuvant treatment for HER2-positive breast cancer. His research has been supported by multiple national and international funding sources focused on advancing precision medicine in breast cancer care. The Translational Breast Cancer Research Group led by Foukakis operates as a comprehensive research ecosystem that integrates clinical data with molecular and computational analyses. The group collaborates extensively with other research teams at Karolinska Institutet and participates in numerous international clinical trials. Their work on the ARIADNE study represents a novel approach to treatment selection based on intrinsic molecular subtypes as determined by Prosigna® analysis, potentially enabling both treatment de-escalation for some patients and escalation for others based on molecular characteristics.

Fredrik Engströmمشاهده پروفایل

دانشیار

• Mathematical Logic
• Dependence Logic
• Generalized Quantifiers
• +۷ مورد دیگر

Fredrik Engström is a Senior Lecturer and Associate Professor in Logic at the Department of Philosophy and Logic, Faculty of Humanities, University of Gothenburg. He also serves as Vice-Dean for Postgraduate Education and Facilities within the Faculty of Humanities. He has been affiliated with the University of Gothenburg since 2006, progressing from research assistant to senior lecturer in 2018. PhD in Mathematics (2004), Chalmers University of Technology Doctoral studies partially completed at the University of Birmingham under Richard Kaye Former Senior Lecturer in Mathematics, Mid Sweden University Head of Department (2016–2021), Department of Philosophy, Linguistics and Theory of Science His research lies at the intersection of mathematical logic, philosophical logic, and cognitive modeling. Key areas include dependence logic, generalized quantifiers, definability, logicality, and the foundations of team semantics. He leads the VR-funded project The Foundations of Team Semantics: Meaning in an Enriched Framework , which explores the expressive power and philosophical implications of modern logical systems. The 15 most recent publications reflect a sustained focus on formal systems with team semantics, particularly dependence logic extended with generalized quantifiers. His work combines deep technical results in model theory and proof theory with cognitive and philosophical considerations, especially in reasoning under bounded resources and the nature of logical constants. Collaborations with prominent logicians such as Juha Kontinen, Jouko Väänänen, and Claes Strannegård highlight the interdisciplinary nature of his research. Scientific Awards: No awards listed in the provided text. Fredrik Engström has supervised or co-supervised several students, though specific names are not listed. He has led significant institutional roles, including departmental leadership and vice-dean responsibilities. His grants include funding from the Swedish Research Council (VR) for foundational research in logic. He is active in multiple research communities, presenting at international logic and philosophy workshops. He is involved in research teams focused on logic and cognition, particularly in projects combining formal logic with cognitive modeling. His future work likely continues to explore the foundations of semantics, the limits of expressiveness in logical systems, and the cognitive plausibility of formal reasoning frameworks.

Per Carlbringمشاهده پروفایل

استاد

• Clinical Psychology
• Internet-based Cognitive Behavioral Therapy
• Mental Health Interventions
• +۷ مورد دیگر

Per Carlbring is a Professor of Psychology at Stockholm University, specializing in clinical psychology. He is a licensed psychologist, psychotherapist, and specialist in clinical psychology. His research focuses on interventions for mental health in people with and without neurodiversity, including autism and ADHD, as well as mechanisms and neural correlates in the brain. He leads the Clinical Psychology, Neuroscience and Health research group at the Department of Psychology. Professor Carlbring has published over 350 scientific articles, with a strong emphasis on internet-based cognitive behavioral therapy, digital mental health interventions, and the application of new technologies like AI and virtual reality in psychological treatment. His recent work explores the intersection of technology and mental healthcare, with numerous 2025 publications examining innovative approaches to treating anxiety, depression, OCD, and ADHD. His research interests include clinical psychology, internet-based interventions, neurodiversity, autism, ADHD, virtual reality therapy, and AI applications in mental healthcare. He has led multiple significant research projects including ERiCA (internet-mediated psychodynamic therapy for adolescents with depression), ZeroOCD (smartphone-based OCD treatment using augmented reality), and studies on AI-delivered therapy for social anxiety. Professor Carlbring's work demonstrates a consistent focus on making evidence-based psychological treatments more accessible through digital platforms while maintaining therapeutic efficacy. His recent publications show increasing integration of artificial intelligence in mental health interventions, examining both its potential and ethical considerations. He has led numerous research projects including alcohol preventive measures in the workplace, Anna vs. Judith (comparing AI-delivered PDT and CBT for social anxiety), internet-based emotion recognition training for adolescents with autism, and stress management for working adults with ADHD. His work spans clinical trials, systematic reviews, and development of innovative digital therapeutic approaches.

Filip Lindskogمشاهده پروفایل

استاد

• Actuarial Mathematics
• Insurance Mathematics
• Financial Mathematics
• +۵ مورد دیگر

Filip Lindskog is a Professor of Insurance Mathematics at Stockholm University (SU) , where he heads the Mathematical Statistics division within the Department of Mathematics . With a background in financial mathematics and actuarial science, his research focuses on quantitative risk management, non-life insurance pricing, and applications of biostochastics and biostatistics. He has co-authored the textbook Risk and Portfolio Analysis: Principles and Methods (Springer, 2012) and supervises PhD students in actuarial mathematics. Education \n \n MSc in Engineering Physics, KTH Royal Institute of Technology (2000) \n PhD in Mathematical Statistics, ETH Zürich (2004) \n Research Interests Filip's work spans actuarial mathematics , financial risk modeling , and insurance analytics . He investigates stochastic processes in regime-switching environments, capital requirements for insurers, and mathematical frameworks for claims reserving. His recent publications emphasize machine learning applications in risk adjustment, asymptotic analysis of Poisson models, and regulatory compliance under IFRS 17.\n Scientific Contributions \n \n Editor, Scandinavian Actuarial Journal (2018–present) \n Director of SU's Master's Program in Actuarial Mathematics (2016–present) \n Head of SU's Mathematical Statistics Division (2018–present) \n \n Students and Collaborations Current and former PhD students include Nils Engler , Lina Palmborg , Jonas Alm , and Johan Nykvist . Former postdocs include Julie Thøgersen , Abhishek Pal Majumder , and Kristoffer Lindensjö . His research group explores discrete random structures, financial applications of biostatistics, and insurance modeling under capacity constraints.\n

• Cryptography
• Theoretical Computer Science
• Algorithms
• +۴ مورد دیگر

Douglas Wikström is a Lecturer in Theoretical Computer Science at the Royal Institute of Technology (KTH), specifically within the School of Electrical Engineering and Computer Science. He is based at Lindstedtsvägen 5, Floor 5, Stockholm, with contact information including phone +46 8 790 81 38 and email dog@kth.se. Dr. Wikström specializes in cryptography and theoretical computer science, with a strong focus on electronic voting systems, security protocols, and mix-net technologies. His research spans zero-knowledge proofs, verifiable computation, and cryptographic protocol design. His work demonstrates significant contributions to the theoretical foundations of secure voting systems and privacy-preserving technologies. His recent publications reveal a continued emphasis on cryptographic verification techniques, security analysis of voting protocols, and efficient implementations of cryptographic primitives. The research trajectory shows consistent contributions to both theoretical aspects of cryptography and practical implementations of secure systems. As an educator, Dr. Wikström teaches several advanced courses including Algorithms, Data Structures and Complexity (DD2350), Advanced Algorithms (DD2440), Fundamentals of Cryptography (DD2448), and Applied Cryptography (DD2520), where he serves as course coordinator, teacher, and examiner. His teaching portfolio demonstrates a strong commitment to both theoretical foundations and practical applications of computer science.

Kristoffer Sahlinمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Algorithms
• +۶ مورد دیگر

Kristoffer Sahlin is an Assistant Professor at Stockholm University's Department of Mathematics and a SciLifeLab Fellow at the National Center for Molecular Biosciences. He leads the Sahlin Group, which develops algorithms and statistical models for analyzing large biological datasets with a focus on DNA, RNA, and protein sequencing data. Dr. Sahlin obtained his PhD in Computer Science from KTH Royal Institute of Technology and conducted postdoctoral research at Pennsylvania State University and the University of Helsinki. Prior to his PhD, he earned bachelor's and master's degrees in mathematics and mathematical statistics from Stockholm University. His research focuses on developing scalable algorithms for high-throughput genomic and transcriptomic sequencing data to address sequence alignment, genome assembly, structural variation detection, and transcriptome analysis. His work bridges computer science, mathematics, and biology with strong emphasis on practical applications to biological and biomedical questions. Recent publications demonstrate expertise in strobemers for sequence similarity detection, Strobealign for flexible seed size read alignment, and methods for reference-free transcriptome analysis using long-read sequencing technologies. His research spans genomic mapping, transcriptome reconstruction, and nanopore sequencing analysis. SciLifeLab Fellow Dr. Sahlin mentors PhD students Alexander Petri and Ivan Tolstoganov, and has previously mentored Benjamin Maier (now a PhD student at EMBL-EBI Cambridge) and Sebastian Lindbom Gunnari. His group actively seeks motivated BSc and MSc students with programming skills for thesis projects in algorithms for Life Sciences and Bioinformatics. The Sahlin Group is part of the Computational Mathematics research group at Stockholm University, which works with mathematical modeling and method development for studying systems ranging from molecular to planetary levels.

• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +۵ مورد دیگر

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.

Evgeny Osipovمشاهده پروفایل

استاد

• Artificial Intelligence
• Vector Symbolic Architectures
• Hyperdimensional Computing
• +۳ مورد دیگر

Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.