جست‌وجوی استاد و پژوهشگر

• Offshore Renewable Energy Systems
• Wave Power Farm Modeling
• Extreme Weather Resilience
• +۳ مورد دیگر

Malin Göteman is an Associate Professor at the Department of Electrical Engineering, Uppsala University. Her research focuses on offshore renewable energy systems, particularly modeling and optimizing large-scale wave power farms and analyzing their resilience to extreme weather conditions. Deputy Director, Center for Natural Disaster Studies (CNDS), Sweden Specialized in wave energy converter dynamics and hybrid offshore energy systems Collaborates on SPH-based numerical wave-current tanks and CFD validation Research Interests: She investigates wave energy farm interactions, hydrodynamic performance of floating platforms, extreme wave load modeling, and survivability strategies using machine learning. Her work spans renewable energy integration, coastal protection, and power system stability under extreme conditions. Recent Publications: Her 2025 articles address resilience of offshore energy systems to metocean extremes and reduced-order modeling via Bayesian design. Earlier works (2023-2024) cover SPH validations for floating wind-wave systems, neural network survivability approaches, and hybrid energy-water supply solutions. Collaborations: She works with international teams on projects like Lysekil wave energy test sites and DeepCwind floating platforms. Key areas include grid-connected wave parks, multi-fidelity surrogate modeling, and comparative studies on offshore wind dependencies.

Peter Hedströmمشاهده پروفایل

استاد

• Materials Science
• Materials Design
• Phase Transformations
• +۸ مورد دیگر

Peter Hedström is a Professor of Materials Science at the Department of Materials Science and Engineering, KTH Royal Institute of Technology. He leads the Hultgren Laboratory for Materials Characterization and directs the Center for X-rays in Swedish Materials Science (CeXS) and the Vinnova competence center NEXT. His research focuses on advanced materials characterization, structure-property relations, and materials design, particularly in metallic alloys, steels, ceramics, and composites. He co-founded companies Ferritico and Scatterin based on his research. Hedström’s work leverages large-scale infrastructure like synchrotron and neutron methods, with key projects including ENDUREIT for improving duplex stainless steels and Track-AM for additive manufacturing analysis. Education: PhD from Luleå University of Technology. Earlier roles at MEFOS/Swerim before joining KTH in 2008. Research Interests: Phase transformations, materials characterization (e.g., synchrotron/X-ray/neutron techniques), additive manufacturing, machine learning applications, and fatigue mechanics. His group explores topics like low-temperature embrittlement, microstructure-strength relationships, and cemented carbide sintering. Articles Trends: Recent work emphasizes in-situ observations of phase separation, precipitation kinetics, and microstructural stability under fatigue. Studies often integrate computational modeling with experimental methods, highlighting interdisciplinary approaches. Grants/Projects: Directs CeXS (hosting the Swedish beamline P21 at PETRA III) and NEXT. Active in EIT Raw Materials (ENDUREIT) and MMD initiatives. Supervises PhD/postdoc projects in neutron scattering, Mg-AM, and machine learning. Labs/Teams: Hultgren Laboratory, SwedNess graduate school, and collaborations with industrial partners like Ferritico.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

• 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.

Tobias Andermannمشاهده پروفایل

استادیار

• Biodiversity Informatics
• Computational Biology
• Machine Learning Applications in Ecology
• +۵ مورد دیگر

Tobias Andermann serves as an Assistant Professor at Uppsala University's Department of Organismal Biology, specializing in Systematic Biology. He leads the Biodiversity Data Lab, an interdisciplinary research group combining ecology, molecular biology, geomatics, and machine learning to address the biodiversity crisis through innovative computational approaches. His research focuses on quantifying biodiversity loss using AI-driven analysis of environmental DNA, remote sensing data, and fossil records. Key interests include modeling extinction rates across geological timescales, developing standardized biodiversity assessment methods, and predicting species distribution changes under anthropogenic pressures. His work demonstrates current extinction rates are 2000-10,000 times higher than natural background levels, comparable to historical mass extinction events. Methodologically, Andermann integrates machine learning with large-scale environmental DNA datasets and high-resolution remote sensing to develop predictive models of biodiversity distribution. His lab pioneers field sampling protocols for environmental DNA collection and AI frameworks that translate remote sensing data into biodiversity metrics for unsurveyed sites. The Biodiversity Data Lab maintains a dynamic, non-hierarchical research environment focused on high-impact solutions to the biodiversity crisis. Current projects include developing environmental DNA protocols for fungi and insects, analyzing land-use impacts on species communities, and creating neural network models for cross-scale biodiversity forecasting. The lab emphasizes practical applications for conservation policy, notably supporting the UN's 30% protected area target established at COP15.

• Game Theory
• Control Theory
• Cybersecurity
• +۵ مورد دیگر

Kim Hammar is a postdoctoral researcher at KTH Royal Institute of Technology, with affiliations at the University of Melbourne (2025-2028) and Imperial College London. He works under Prof. Tansu Alpcan and Prof. Emil Lupu, focusing on the intersection of game theory, control theory, and large-scale systems for networking and security applications. Previously, he completed his Ph.D. at KTH under Prof. Rolf Stadler and Prof. Pontus Johnson. His research spans cybersecurity, networked systems, and adaptive control mechanisms. Key contributions include applying optimal stopping reinforcement learning conjectural online learning causal modeling to intrusion response and network security. His 2025-2024 publications highlight advancements in automated security through game-theoretic and control-theoretic approaches, with a focus on dynamic environments. Kim received the VR International Postdoctoral Fellowship in 2025. He has served as an assistant for Computer Networks (EP111U) Computer Systems (EP121U) at KTH.

Ilie Sarpeمشاهده پروفایل

پژوهشگر

• Graph Mining
• Temporal Networks
• Algorithm Design
• +۵ مورد دیگر

Ilie Sarpe is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, mentored by Prof. Aristides Gionis. He is an active member of both VandinLab and AIDA Lab, focusing on developing rigorous algorithms for temporal network analysis. His educational background includes: PhD in Computer Engineering from the University of Padova (2019-2023), with thesis on 'Efficient and Rigorous Techniques for the Analysis of Large Temporal Networks' Master's Degree in Computer Engineering (summa cum laude) from the University of Padova (2017-2019) Bachelor's Degree in Computer Engineering from the University of Padova (2014-2017) Sarpe's research centers on the design of scalable algorithms for data-mining problems, particularly in graph-mining and clustering. He specializes in probabilistic algorithms with rigorous theoretical guarantees, with a strong focus on temporal networks. His work integrates tools from sampling theory, probability, concentration inequalities, and statistical learning theory to develop efficient solutions for complex network analysis problems. His publications demonstrate a consistent focus on temporal network analysis, with recent work accepted at top venues including KDD 2024, WWW 2022, CIKM 2021, and SIAM SDM 2021. The research trajectory shows increasing sophistication in handling temporal motifs, dense subnetwork discovery, and centrality measures in evolving networks. His scientific recognition includes: SoBigData TNA Fellowship (2022) 3 years Ph.D. Fellowship (2019) Award for Scientific Degrees (2017) Two 'Mille e una lode' awards (2016, 2017) Sarpe actively supervises master's thesis projects in data mining at KTH and has secured research funding through the SoBigData TNA Fellowship. His research group affiliation with VandinLab and AIDA Lab provides collaborative opportunities across multiple institutions. He maintains active laboratory work focused on developing practical implementations of his theoretical algorithms, as evidenced by his GitHub repositories containing C++ implementations of his published methods.

• 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.

• Computational Weld Mechanics
• Finite Element Analysis
• Fatigue and Fracture of Materials
• +۳ مورد دیگر

Professor Zuheir Barsoum is a faculty member at KTH Royal Institute of Technology, serving as Vice Head (Research) in the Department of Engineering Mechanics. His research focuses on computational weld mechanics, fatigue assessment of materials, and structural integrity of welded joints. Key areas include high-frequency mechanical impact (HFMI) treatments for fatigue improvement, finite element analysis, and lightweight metal joining. Funded by VINNOVA, SSAB, Volvo, and others, his work addresses industrial challenges in structural durability. Current PhD students include Martin Edgren (bridge structural health monitoring), Mehdi Ghanadi (fatigue of high-strength steels), Yu Zhu (laser cladding simulations), and Kaushik Iyer (LCC modeling of welded structures). He teaches courses like Advanced Design of Welded Structures (SD2420) and oversees degree projects in Lightweight and Solid Mechanics. Notable achievements include the 2010 Henry Granjon Prize for fatigue design research. His startup Winteria AB commercializes digital quality assurance solutions for welding production, aligning with Industry 4.0 trends. Recent research emphasizes probabilistic fatigue modeling, machine learning for weld geometry analysis, and material defect characterization. Collaborations include Chalmers University and Swerim. His work bridges advanced manufacturing, computational mechanics, and industrial applications to enhance structural reliability and lifecycle cost optimization.

• Fusion Plasmas
• Tokamak Experiments
• Electromagnetic Field Theory
• +۴ مورد دیگر

Pär Strand is a Professor at Chalmers University of Technology, affiliated with the Department of Astronomy and Plasma Physics. His research focuses on transport in fusion plasmas , particularly through analysis of experiments at JET and development of simulation tools for ITER and other tokamak facilities. A key contributor to EU projects, he directs the Chalmers e-Science Centre, emphasizing data-driven methodologies and large-scale simulation technologies. Expertise: Fusion plasma dynamics, electromagnetic field theory, integrated modeling frameworks Projects: Code development for ITER/JET, FAIR data principles in fusion research, turbulence transport simulations Research Trends: Recent publications highlight advancements in: Tokamak power exhaust solutions (divertor shaping, neutral baffling) Machine learning applications for pedestal dynamics and disruption prediction High-order solvers for plasma transport equations Validation of D-T fusion power predictions against JET experiments

Anders Behndigمشاهده پروفایل

استاد

• Ophthalmology
• Cataract Surgery
• Corneal Crosslinking
• +۹ مورد دیگر

Anders Behndig is a Professor and senior consultant (attending) physician at the Department of Clinical Sciences, Section of Ophthalmology , Umeå University. His clinical and research work focuses on cataract surgery, corneal crosslinking for keratoconus, intraocular lens complications, and surgical outcomes analysis. Position: Professor, Senior Consultant Ophthalmologist Institution: Umeå University, Sweden Department: Clinical Sciences/Ophthalmology Behndig’s research spans cataract surgery optimization (e.g., prioritization during pandemics, bilateral surgery trends), corneal crosslinking (epi-on vs. epi-off protocols), and intraocular lens stability . His work often leverages large-scale registries like the Swedish National Cataract Register and EUREQUO to analyze complication risks and surgical outcomes. Recent publications highlight trends in refractive surgery (customized crosslinking for myopia), uveitis and diabetic retinopathy in cataract outcomes, and machine learning applications for predicting posterior capsule rupture. Collaborative efforts include multinational registry studies and evaluations of anesthesia techniques in cataract procedures.

Johan Klarbringمشاهده پروفایل

استادیار

• Material Science
• Energy Materials
• Computational Physics
• +۶ مورد دیگر

Johan Klarbring is an Assistant Professor in the Department of Physics, Chemistry and Biology (IFM) at Linköping University. His research focuses on theoretical material science using ab initio computational methods to study energy materials such as solid oxide fuel cells, batteries, solar cells, and thermoelectric devices. He combines quantum mechanics and statistical physics to analyze phase stabilities and electronic properties of dynamically disordered solids. His work emphasizes computational approaches to understand material behavior at finite temperatures, including defect formation, ionic transport, and anharmonic effects. Collaborations involve international teams and cutting-edge facilities like supercomputers for large-scale simulations. Key research themes include perovskite materials, machine learning force fields, and thermoelectric properties. Notable contributions address dynamic nanodomains in perovskites and vacancy-driven ion conduction mechanisms. His group (Ab initio methods and energy materials (TEAM)) explores eco-friendly applications of disordered solid materials. Recent projects include studies on lead-free perovskites, superionic transitions, and magnetism in halide double perovskites. He has contributed to over 30 peer-reviewed articles, with high-impact work in Nature Nanotechnology and Chemical Science .

Atakan Aralمشاهده پروفایل

عضو هیئت علمی

• Edge Computing
• Edge AI
• Internet of Things (IoT)
• +۳ مورد دیگر

Atakan Aral is a Visiting Lecturer at the Department of Computing Science, Umeå University. His research focuses on Edge Computing, Edge AI, and the Internet of Things (IoT), with a particular emphasis on resource management and sustainable environmental monitoring. He is affiliated with the Autonomous Distributed Systems Lab and Green Distributed Computing Group, both part of the Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative. His work spans theoretical frameworks and practical implementations in edge intelligence and distributed systems. Research Interests: Edge Computing architectures and workflows Neuromorphic and energy-efficient AI systems Federated learning and multi-cluster collaboration Sensor networks for environmental monitoring Optimization of resource allocation in distributed systems Key Publications Trends: Recent work emphasizes neuromorphic edge AI applications, hierarchical federated learning, and energy-efficient IoT deployments. His articles often intersect computing continuum concepts with real-world challenges like rural environmental monitoring and latency-critical systems. Scientific Awards: No awards explicitly listed in the provided text. Advising & Grants: No formal student advisees or grant details provided. However, he contributes to the De facto Center of Excellence in Autonomous Distributed Systems (2023–2029), indicating involvement in large-scale collaborative research. Labs & Teams: Member of the Autonomous Distributed Systems Lab (lead in distributed systems research) and Green Distributed Computing Group (focused on sustainability in computing).

• Radio Astronomy
• Astrophysics
• Black Hole Imaging
• +۳ مورد دیگر

John Conway is a Professor of Radio Astronomy at Chalmers University of Technology , serving as Director of Onsala Space Observatory . His work spans multiple domains in observational astrophysics, focusing on: High-resolution VLBI imaging of black holes and AGN Instrumentation development for submillimeter telescopes Multiwavelength studies of M87 and Sgr A* black holes Large-scale radio surveys with LOFAR and SKA technologies As a key member of the Event Horizon Telescope collaboration, he contributes to polarization analysis and magnetic field studies around supermassive black holes. His instrumentation projects include work on the Onsala Twin Telescopes and SKA data stacking techniques. Current research involves black hole shadow characterization , jet dynamics , and machine learning applications for radio source detection. He collaborates extensively with international teams across projects like ALMA, LOFAR, and APEX.

Ali Mansourianمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +۷ مورد دیگر

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.