جست‌وجوی استاد و پژوهشگر

Franziska Klüglمشاهده پروفایل

استاد

• Multi-Agent Systems
• Agent-Based Simulation
• Artificial Intelligence
• +۴ مورد دیگر

Franziska Klügl is a Professor in Computer Science at Örebro University's Faculty of Business, Science and Engineering, affiliated with the Center for Applied Autonomous Sensor Systems (AASS). She currently leads the KKS-funded TeamRob project on Human-Robot Teamwork and serves as Deputy Dean of the faculty since January 2023, chairing the academic appointment committee. Previously, she headed the Computer Science department (2020-2022) and served on the faculty board (2019-2022). Her research focuses on: Multi-agent systems : Development of languages, processes, and tools for agent-based simulation Interdisciplinary applications : Transportation, economics, epidemics, production, and mining simulations Simulation engineering : Integrating AI, machine learning, and formal methods to create accessible modeling tools for domain experts She created SeSAm , a visual programming tool for agent-based simulation that enables rapid prototyping of complex models. Analysis of her recent publications reveals three dominant themes: Human-robot collaboration frameworks and intention recognition systems Economic impacts of automation on labor markets and engineering services Advanced simulation methodologies using affordance theory and reinforcement learning She teaches software engineering, multi-agent systems, and agent-based modeling across multiple programs, including the WASP AI&ML PhD course. She leads research groups at the Machine Perception and Interaction Lab and oversees the TeamRob human-robot teamwork project.

Bor Gregorcicمشاهده پروفایل

دانشیار

• Physics Education Research
• Science Education
• Educational Technology
• +۵ مورد دیگر

Bor Gregorcic serves as an Associate Professor (Docent) and University Lecturer in Physics Education at Uppsala University's Department of Physics and Astronomy. His academic work bridges theoretical frameworks in science education with practical applications of educational technology. Gregorcic's research program spans several interconnected domains: Physics education research focusing on conceptual understanding in mechanics and statistical mechanics Embodiment and multimodal approaches to science learning Educational technology applications including interactive whiteboards and digital learning environments Emerging applications of artificial intelligence in physics education contexts Teacher professional development and pedagogical content knowledge His recent publication trajectory (2023-2025) reveals a significant pivot toward AI applications in physics education, with multiple studies examining ChatGPT's performance in interpreting physics concepts, visual representations, and kinematics graphs. This represents an evolution from his earlier foundational work on embodiment in science education and multimodal learning, where he explored conceptual blending, gesture studies, and the use of digital tools like Algodoo. His research consistently addresses how students develop conceptual understanding through various representational forms. Gregorcic has been recognized with the 'Excellent Teacher' award at Uppsala University, highlighting his commitment to pedagogical excellence. He maintains active research collaborations with Giulia Polverini, Elias Euler, Trevor Volkwyn, Ebba Koerfer, and other international scholars. His work appears predominantly in leading physics education research journals including Physical Review Physics Education Research, European Journal of Physics, and Physics Education, demonstrating significant impact in the field.

Daniel Edlerمشاهده پروفایل

پژوهشگر

• Network Science
• Biodiversity
• Ecological Modeling
• +۳ مورد دیگر

Daniel Edler is a Researcher and Postdoctoral Fellow at the Department of Physics, Umeå University. His work focuses on network science, biodiversity analysis, and ecological modeling, with a particular emphasis on developing computational tools for community detection and biogeographical mapping. He contributes to interdisciplinary research, integrating methods from computer science, ecology, and information theory. Edler leads the development of Infomap Bioregions, a tool for mapping biogeographical regions using species distribution data, and CoordinateCleaner, which standardizes biological occurrence records. His research also explores threats to Madagascar’s biodiversity and the interplay between socio-political factors and biodiversity data availability through tools like Bio-Dem. He has co-developed raxmlGUI 2.0, a phylogenetic analysis interface, and contributes to the Infomap software package for network analysis. Edler’s publications highlight themes in higher-order network flows, multilayer community detection, and ecological network modules. His work appears in journals such as Science , American Journal of Botany , and Methods in Ecology and Evolution . He is an active member of the Complex Systems research group at Umeå University.

Jonas Beskowمشاهده پروفایل

استاد

• Multimodal Interaction
• Speech Synthesis
• Robotics
• +۳ مورد دیگر

Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.

• Digital Archaeology
• 3D Visualization
• Spatial Technology
• +۷ مورد دیگر

Nicolò Dell'Unto serves as Professor of Archaeology at Lund University's Department of Archaeology and Ancient History within the Faculty of Humanities and Theology. His research pioneers digital methodologies for archaeological analysis, specializing in 3D visualization, spatial technology, and virtual reality applications that transform how we perceive and investigate the past. Based at Helgonavägen 3 in Lund (Room LUX:A122), he directs the Digital Archaeology Laboratory (DARKLab) and oversees undergraduate/postgraduate digital archaeology programs. His educational background includes: Archaeology studies at University of Rome, La Sapienza PhD in Technology and Management of Cultural Heritage from IMT Lucca, Italy Postdoctoral fellowship at University of California Merced Dell'Unto's research focuses on how laser scanners, photogrammetry, GIS, and virtual reality technologies fundamentally reshape archaeological practice. His work bridges technical innovation with theoretical frameworks in landscape archaeology, emphasizing practical field applications while addressing methodological challenges in data interpretation. Key themes include digital documentation standards, 3D data management, and the cognitive impact of visualization tools on archaeological reasoning. His publication trends reveal a shift toward AI integration in archaeological data interpretation, infrastructure development for 3D data sharing, and cross-disciplinary collaborations examining Mediterranean connectivity. Recent works emphasize practical frameworks for implementing digital tools in fieldwork while addressing sustainability challenges in digital heritage preservation. Award highlights: Einar Hansen Prize for Humanities (2017) Royal Physiographic Society of Lund election (2024) Best Paper Award (2014) Highly Cited Research recognition (2017) Dell'Unto supervises PhD and master's students while leading major projects including RE-OSTRAKON (3D artifact scanning), TETRARCHs (data reuse), and AIR (Archaeological Interactive Report). His DARKLab serves as Sweden's national infrastructure for digital archaeology, collaborating with institutions like University of Oslo's Museum of Cultural History where he holds a visiting professorship since 2019. He actively shapes digital archaeology policy as Domain Specialist for Swedish National Data Service, Board Member for Statens historiska museer, and Open Science Champion at Lund University, driving national standards for archaeological data management and open science practices.

Stefano Bonettiمشاهده پروفایل

دانشیار

• Ultrafast magnetism
• Spin dynamics in condensed matter
• Terahertz physics
• +۳ مورد دیگر

Stefano Bonetti is an Associate Professor in the Department of Physics at Stockholm University , leading the Ultrafast Condensed Matter Dynamics Group . His research focuses on manipulating quantum materials using terahertz (THz) and near-infrared laser fields to study spin dynamics and ultrafast phenomena at nanoscale and femtosecond timescales. PhD in Materials Physics (KTH Royal Institute of Technology, Sweden) MSc in Engineering Physics (KTH) BSc in Technical Physics (Politecnico di Milano, Italy) Recent research efforts involve time-resolved X-ray microscopy to visualize spin currents and magnetization dynamics, leveraging facilities like free-electron lasers. His work bridges experimental physics and applied materials science, aiming to enhance energy efficiency in data storage technologies by understanding ultrafast spin-lattice interactions . Key scientific awards and grants: ERC Starting Grant (2017-2021) Wallenberg Academy Fellow (2018-2023) VR's free grant (2019-2023) International Career Grant (COFUND) (2015-2019) He has contributed to developing THz-based techniques for magnetic control and authored foundational work on spin-wave solitons and nonlinear magnetoelastic coupling . His group collaborates internationally, utilizing advanced synchrotron and free-electron laser facilities.

Anders Björnمشاهده پروفایل

استاد

• Nonlinear Potential Theory
• Analysis on Metric Spaces
• Differential Equations
• +۳ مورد دیگر

Anders Björn is a Professor at the Department of Mathematics (MAI) at Linköping University (LIU), affiliated with the Analysis and Mathematics Education (ANDI) division. His research focuses on Nonlinear Potential Theory , particularly p-harmonic functions , quasiminimizers , and Newtonian Sobolev spaces in metric spaces. He co-leads the research group in this area and contributes to Analysis on Metric Spaces . He teaches undergraduate and graduate courses, including Real Analysis and Functional Analysis . Organizational Roles: Organizer of the Mathematical Colloquium, Local Representative for Svenska Matematikersamfundet (Swedish Mathematical Society). Editorial Work: Former Technical Editor for Acta Mathematica (2004–2015) and Arkiv för Matematik (1993–2015), both published by Institut Mittag-Leffler. His research explores foundational questions in mathematical analysis, blending pure mathematics with interdisciplinary applications. He emphasizes understanding properties of solutions to differential equations in general settings, akin to constructing an 'identikit' of mathematical phenomena. His work is published in collaboration with the ANDI group, and he actively engages in promoting mathematics through outreach and academic service.

• 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.

Federica Violaمشاهده پروفایل

پژوهشگر

• Cardiovascular Imaging
• 4D Flow MRI
• Cardiovascular Modeling
• +۵ مورد دیگر

Federica Viola is a Researcher at Linköping University, affiliated with the Department of Health, Medicine and Caring Sciences (HMV) and the Division of Diagnostics and Specialist Medicine (DISP). She is part of the Cardiovascular Magnetic Resonance Group (CMR) and the HEART4FLOW initiative. Her primary research focuses on improving cardiovascular 4D flow MRI data quality, hemodynamic modeling, and applying deep learning techniques for automated analysis in clinical settings. Key affiliations include the Center for Medical Image Science and Visualization (CMIV), which develops advanced imaging tools for healthcare. She collaborates with faculty members such as Professors Tino Ebbers, Petter Dyverfeldt, and Carljohan Carlhäll on projects integrating imaging and computational models to study cardiovascular diseases like hypertension and diabetes. Her research emphasizes personalized medicine, combining 4D flow MRI with mathematical models to assess diastolic function, quantify blood flow dynamics, and evaluate treatment effects. Recent work addresses challenges in reproducibility of cardiac models and automated segmentation using AI. Her contributions span cardiovascular imaging technology, hemodynamic analysis, and interdisciplinary collaborations in the Circulation and Metabolism (CircM) strategic network. She actively publishes in journals like Scientific Reports , Journal of Cardiovascular Magnetic Resonance , and Frontiers in Cardiovascular Medicine .

Tony Lindebergمشاهده پروفایل

استاد

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +۴ مورد دیگر

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

• Visualization
• Computer Graphics
• Flow Visualization
• +۳ مورد دیگر

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

• Particle Physics
• High Energy Physics
• Instrumentation Development
• +۷ مورد دیگر

Richard Brenner is a Professor and Head of Department at the Department of Physics and Astronomy , Uppsala University. He is a key member of the ATLAS detector team at the CERN Large Hadron Collider (LHC) , focusing on instrumentation development and real-time data processing for dark matter detection. His work bridges semiconductor detector signals with machine learning systems , emphasizing radiation resistance in high-energy environments. Role: Head of Department of Physics and Astronomy Affiliation: Uppsala University and CERN Research Focus: Dark Matter, Higgs Boson, Particle Physics His recent 15 publications (2025) span topics like dark matter searches , Higgs boson production , vector boson fusion , and machine learning applications in data analysis. Keywords include High Energy Physics , Experimental Physics , and Quantum Interactions , with subfields such as Collider Physics , Detector Engineering , and Theoretical Modeling

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +۸ مورد دیگر

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.