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Mattias VillaniView profile

Professor

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +3 more

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.

• Materials Science
• Metallurgy
• Additive Manufacturing
• +3 more

Greta Lindwall is a Lecturer at the Royal Institute of Technology (KTH), specializing in materials science and additive manufacturing. She teaches courses such as Metallic Materials, Powder Metallurgy, and Materials in Design and Product Development. Her roles include examiner, course coordinator, and teacher for several advanced programs. Her research focuses on solidification processes in additive manufacturing, particularly using high-energy X-ray techniques and synchrotron imaging to study material behavior during electron beam and laser powder bed fusion. She has contributed to understanding microstructural evolution in steels, grain refinement mechanisms, and process optimization for materials like tool steels and stainless steel. Her work integrates experimental methods with computational modeling, including CALPHAD-based approaches for phase equilibria and microstructural prediction. Key research areas include real-time tracking of solidification, smoke mechanisms in electron beam processes, and the development of novel alloys for AM applications. Lindwall collaborates with initiatives like the LIGHTer Academy and has published extensively on topics such as phase transformations, material properties under AM conditions, and process monitoring technologies. Her contributions bridge fundamental materials science with advanced manufacturing techniques, aiming to improve material performance and process reliability.

• Visualization
• Computer Graphics
• Flow Visualization
• +3 more

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.

• Battery technology
• Materials science
• Electrochemistry
• +7 more

Kristina Edström is a Professor at Uppsala University, specifically affiliated with the Department of Chemistry, Structural Chemistry unit at Ångström Laboratory. She is a leading expert in battery technology and electrochemistry, with extensive research on lithium-ion and sodium-ion batteries, solid-state electrolytes, and surface chemistry of battery components. Current research focuses on battery interfaces (SEI/CEI), polymer-ceramic composite electrolytes, and sustainable battery materials Key methodologies include X-ray photoelectron spectroscopy (XPS), neutron diffraction, and molecular dynamics modeling Recent publications (2024-2025) investigate: boron-modified solid electrolytes, self-healing polymer electrolytes, aqueous Li-ion systems, and manufacturing sustainability for batteries. Her work shows strong emphasis on improving battery safety, efficiency, and environmental impact. Leadership roles include participation in Battery 2030+ research initiatives and NordBatt conferences. She has contributed to developing standardized experimental workflows combining neutron/synchrotron techniques with AI-driven battery research.

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

• Machine Learning
• Network Performance
• IoT Security
• +3 more

Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.

• Electrical Engineering
• Wind Turbine Systems
• Electric Vehicle Powertrains
• +5 more

Torbjörn Thiringer is a Professor in Electrical Engineering at Chalmers University of Technology. His research focuses on electrical systems for wind turbines and electric vehicles, with particular emphasis on system-level analysis and component-level studies of electrical machines, power electronics, and battery systems. Key research areas: Wind turbine systems, Electric vehicle drives, Battery degradation, Power electronics optimization Recent work explores graphene-based thermal management, fuel cell hybrid vehicles, and direct current building distribution efficiency His publications demonstrate interdisciplinary engagement with topics spanning: Finite element analysis of motor designs Life cycle assessment of energy systems Thermal modeling of SiC inverters Wave energy converter optimization Core loss measurement techniques Hydrogen fuel cell integration Professor Thiringer's collaborations span multiple institutions and industry partners, focusing on both theoretical modeling and practical implementation of advanced energy systems.

• Materials Science
• Mechanical Engineering
• Chemical Engineering
• +7 more

Fang Liu is an Assistant Professor at Chalmers University of Technology, Department of Materials and Manufacturing. Her research focuses on uncovering the physical and chemical mechanisms in material systems such as high-temperature alloys, polymer composites, and semiconductors, using advanced microscopy and spectroscopy techniques. Specializes in structural battery composites, creep behavior, and high-temperature corrosion Collaborates with industry partners and theoretical researchers Develops reliable prediction tools for material performance Research Trends (2023–2025): Structural battery composites with carbon fibers and hybrid electrolytes Microstructural analysis via atom probe tomography and focused ion beam High-temperature oxidation and corrosion resistance Mechanical-electrochemical coupling in multifunctional materials Key Collaborations : Leif Asp (Chalmers), Johanna Xu (Chalmers), Marcus Johansen (Chalmers) Industry partners: Office of Naval Research, VINNOVA, Wallenberg AI Program

• Particle Physics
• High Energy Physics
• Instrumentation Development
• +7 more

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

Louis OhlView profile

Research Fellow

• Machine Learning
• Unsupervised Learning
• Clustering Algorithms
• +3 more

Louis Ohl is a Research Fellow and Postdoc at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on unsupervised learning methods, particularly clustering algorithms and PU learning, with applications to medical data (e.g., aortic stenosis phenogroups) and material science (e.g., MAX-phase to MXen structure changes). He holds a PhD from Côte d'Azur University (France) and Laval University (Canada), where he developed discriminative clustering techniques for cardiac disease analysis. Education: PhD in Computer Science from Côte d'Azur University and Laval University (France/Canada). Research interests include: Unsupervised and semi-supervised machine learning Clustering algorithm development Medical informatics applications in cardiology Material science computational modeling PU learning frameworks His recent work emphasizes bridging theoretical advancements in clustering with practical applications in healthcare and materials research. Collaborations include projects on aortic stenosis progression prediction and computational studies of structural phase transitions in materials. No scientific awards listed. Active in advising and research grants related to his current projects at STIMA. Associated with the STIMA division's international master's program in Statistics and Machine Learning.

• Nuclear Reactor Safety
• Thermal-Hydraulics
• Computational Fluid Dynamics (CFD)
• +3 more

Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.

Olaf HartigView profile

Associate Professor

• Database Systems
• Semantic Web
• Data Management
• +5 more

Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding

Muhammad UsmanView profile

Senior Lecturer

• Cloud Computing
• Edge Computing
• Distributed Systems
• +7 more

Dr. Muhammad Usman is a Senior Lecturer at the Department of Mathematics and Computer Science, Karlstad University, Sweden. His research focuses on cloud and edge computing, distributed systems, performance observability, DevOps automation, and networked systems. He holds a B.S. from NUST (Pakistan), and integrated M.S./Ph.D. from GIST (South Korea). He teaches master's-level courses in Distributed Systems and Cloud Computing. His research interests include optimizing container orchestration for edge-IoT workloads, developing AI-driven frameworks for industrial IoT (e.g., AIDA), and enhancing observability of distributed systems through frameworks like DESK and SmartX. His work often addresses challenges in resource efficiency, scalability, and fault detection in edge and cloud environments. Notable contributions include benchmarking lightweight container orchestration platforms, designing cost-effective testbeds for DevSecOps, and creating visualization frameworks for SDN-enabled clouds. His publications span topics from IIoT lifecycle management to intent-based network control. Usman actively collaborates internationally, with projects involving institutions like GIST, Aalto University, and the IEEE community. His work bridges theoretical research and practical applications in edge computing and cloud-native systems.

Kevin KammView profile

Associate Professor

• Financial Mathematics
• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• +8 more

Kevin Kamm is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research spans stochastic analysis , financial mathematics , commodities , and machine learning , with a focus on optimal strategies and SPDEs. He has developed innovative approaches using Deep Learning and stochastic Magnus expansion for financial models. Education : Mathematics studies at Technische Universität Berlin; Ph.D. in financial mathematics at the University of Bologna under the ABC-EU-XVA project. Research : Specializes in negative interest rate frameworks, rating triggers for XVA adjustments, and HPC applications in SPDEs. Collaborates on aquaculture valuation models incorporating biological and feeding cost risks. Publications : 15+ recent works cover CIR model extensions, XVA calibration, SPDE numerical methods, and machine learning applications in financial mathematics and commodities. Teaching : Instructs courses in Financial Mathematics and Stochastic Differential Equations , supervising Master's theses in areas like stress testing and mortgage-backed securities. Affiliations : Member of Umeå University's Mathematical Finance and Economics and Mathematical Modeling and Analysis research groups.

• Nuclear Materials
• Radiation Damage Physics
• Computational Materials Science
• +4 more

Pär Olsson is a Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on computational and experimental studies of radiation damage in nuclear materials, with particular emphasis on understanding defect dynamics, microstructural evolution under irradiation, and the behavior of advanced nuclear fuels. He leads courses such as Multiscale Modeling of Nuclear Materials and Radiation Damage Physics, emphasizing both theoretical and practical aspects of materials science in nuclear contexts. His research interests include the development of advanced accident-tolerant fuels (e.g., UN-UO2 composites), the computational modeling of radiation effects in metals like Fe and W, and the interplay between solute atoms and defects in irradiated materials. His work combines first-principles calculations, kinetic Monte Carlo simulations, and experimental validation to address challenges in nuclear energy systems. Notable contributions include studies on fission product solubility in nuclear fuels, oxygen’s role in tungsten defect evolution, and the thermal performance of UN-based composites. His team’s projects, such as the M4F initiative (Multiscale Modelling for Fusion and Fission Materials), aim to bridge atomistic and continuum-scale models for material design. Pär Olsson collaborates extensively with international institutions and contributes to nuclear materials education through course coordination and supervision of graduate studies in physics and materials science.