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Magnus HanssonView profile

Assistant Professor

• Market Microstructure
• Decentralized Finance (DeFi)
• Blockchain Technology
• +3 more

Magnus Hansson is an Assistant Professor at Stockholm School of Economics (visiting from Stockholm University) and an affiliate researcher at the Swedish House of Finance. He holds a PhD in Economics from the University of Gothenburg and has industry experience in machine learning and data science. His research focuses on the intersection of blockchain technology, decentralized finance (DeFi), and financial economics. Notable areas include market microstructure analysis, algorithmic trading dynamics, and the application of machine learning to financial systems. He also explores sustainable finance and real estate finance. Magnus teaches courses such as Research Frontiers in Financial Economics , covering blockchain technology, DeFi, and market microstructure. His lectures emphasize practical applications of distributed ledger technologies and their implications for traditional financial systems. His publications include influential works on DeFi price impact modeling, arbitrage in crypto markets, and LSTM-based stock return prediction. While no formal awards are listed, his work has contributed significantly to understanding decentralized financial systems and blockchain's role in modern economics.

• Machine Learning
• Information Fusion
• Data Analytics
• +7 more

Alexander Karlsson is an Associate Professor at the Department of Information Technology, School of Informatics, University of Skövde. His research focuses on machine learning, information fusion, and their applications in transportation, healthcare, and industrial systems. He leads projects like I2Connect, developing advanced driver assistance systems using deep learning and situation awareness algorithms. Key research interests include driver intention recognition, predictive maintenance, telecommunication anomaly detection, and biomedical data analysis. He has published extensively in top venues like IEEE Transactions, ACM journals, and conferences like IPMU and MDAI. Education: PhD in Information Fusion (2010) from University of Skövde. Teaching: Coordinates courses in data analytics and AI at graduate and postgraduate levels. Projects: Active in industrial collaborations (e.g., automotive, steel production, telecommunication). His work bridges theoretical advances in machine learning with practical industrial challenges, emphasizing uncertainty quantification and robust decision-making frameworks.

• Numerical Analysis
• High Performance Parallel Computing
• Algorithm Design
• +5 more

Carl C Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University, Sweden. His research focuses on developing robust numerical algorithms that cannot fail, particularly in the context of finite precision arithmetic used by computers. His work bridges theoretical numerical analysis with practical high-performance computing applications. Dr. Mikkelsen's research interests center around numerical analysis and high performance parallel computing, with special emphasis on the worst case analysis of algorithms. He investigates the profound differences between exact arithmetic used by humans and finite precision arithmetic used by computers. His current projects include developing parallel constraint solvers for constrained molecular dynamics and parallel solvers for ultra-sparse linear systems. He is a co-author of the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems in parallel. His recent publications demonstrate a strong focus on accuracy requirements in numerical methods, particularly Newton's method, and the development of robust parallel algorithms for linear algebra problems. His work spans from theoretical analysis of numerical stability to practical implementation in high-performance computing environments. Dr. Mikkelsen teaches numerical analysis and scientific computing, emphasizing algorithm design, analysis, and implementation. He focuses on teaching students the critical differences between exact and finite precision arithmetic, and invests significant time in teaching software development practices and technical writing.

• Formal Language Theory
• Machine Learning
• Semantic Parsing
• +3 more

Johanna Björklund is an associate professor at the Department of Computing Science, Umeå University, Sweden. Her research spans formal language theory, machine learning, and semantic parsing of multimodal data, with a focus on translating complex media (e.g., videos with audio/subtitles) into algorithm-friendly graph-based formats. She leads the Foundations of Language Processing research group. Key Research Projects: WARA Media & Language (2024–2026) Semantic Parsing of Multimodal Data (2021–2024) AI-Driven Contextual Advertising (2020–2023) Her work bridges theoretical computer science (tree automata, graph languages) with applied AI (contextual advertising, sentiment analysis, clinical decision support). She has secured significant grants, including SEK 12 million for AI projects and 4 million for Deep Learning development. Scientific Awards: SEK 12 million grant (2020) 4 million SEK grant for Deep Learning methods (2020)

• Artificial Intelligence
• Machine Learning
• Ethics in AI
• +3 more

Mattias Brännström is a Researcher at the Department of Computing Science, Umeå University. Currently on leave, he pursues doctoral studies while contributing to projects focused on equitable and trustworthy AI systems. Education: Doctoral student in Computing Science Research Focus: His work bridges technical and ethical dimensions of artificial intelligence, emphasizing fairness frameworks, bias mitigation, and socio-technical assessment methodologies. Key areas include: Equitable AI design Trustworthy algorithmic systems Human-AI collaboration Ethical impact assessments Project Highlights: Active in the 'Assessment and Engineering of Equitable, Unbiased, Impartial and Trustworthy AI-systems' (2022-2024) initiative. Collaborates with research groups in Responsible Artificial Intelligence and Deep Data Mining.

Konrad AbramowiczView profile

Associate Professor

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +4 more

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.

Per-Håkan LundowView profile

Associate Professor

• Statistical Mechanics
• Spin Glasses
• Combinatorics
• +3 more

Per-Håkan Lundow serves as an Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, holding the Docent qualification. His office is located at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.325, Umeå (901 87), with contact email per-hakan.lundow@umu.se and phone +46 90 786 79 93. Lundow's research spans: Statistical Mechanics of Ising models and spin glasses across dimensions 2-5 Computational combinatorics and optimization Finite-size scaling and universality classes Applications to Boson sampling and quantum computing His work combines theoretical analysis with advanced computational methods to investigate critical phenomena and phase transitions in complex systems. Publication trends reveal sustained focus on high-dimensional spin systems since 2002, with recent emphasis on five-dimensional Ising model behavior, damage spreading in random cluster models, and efficient permanent computation algorithms. Collaborations with Ian Campbell and Klas Markström dominate his output, appearing consistently across Nuclear Physics B, Physical Review E, and Journal of Computational Physics publications. Lundow participated in the "Three and four-dimensional tera-spin systems" research project (2016-2020) and is an active member of Umeå University's Computational Combinatorics and Optimization research group. No information regarding student advising or additional grant funding appears in the source material.

• Functional Data Analysis
• Spatial Statistics
• Applied Statistics
• +4 more

Sara Sjöstedt de Luna is a Professor in Mathematical Statistics at Umeå University's Department of Mathematics and Mathematical Statistics. She is a board member of The Wallenberg AI, Autonomous Systems & Software Program (WASP) and holds the title of Docent. Her research spans functional data analysis, spatial statistics, and their applications in climatology, forestry, and human motor control. Affiliation : Umeå University Department : Mathematics and Mathematical Statistics Research Focus : Functional data analysis, spatial statistics, and interdisciplinary applications in environmental and biomedical domains Her recent work involves developing statistical tools like the fdaMocca and fiberLD R packages, advancing methodologies for clustering functional data with covariates, and refining kriging approaches for spatiotemporal predictions. She has contributed to climate reconstruction using varved lake sediment data and biomechanical analysis of knee kinematics post-injury. Applications of her research include: Climate modeling using historical sediment records Wood fiber length determination for forestry studies Biostatistical analysis of human motor control Sjöstedt de Luna employs both parametric and nonparametric statistical techniques, with a focus on handling dependent data structures, censoring challenges, and algorithm design for complex datasets.

Signe LundqvistView profile

Researcher

• Discrete Mathematics
• Geometry
• Graph Theory
• +3 more

Signe Lundqvist is a researcher affiliated with the Department of Mathematics and Mathematical Statistics at Umeå University , Sweden. Her work focuses on discrete mathematics and geometric rigidity theory, particularly in planar point-line and rod configurations. Research Highlights : Rigidity under projective transformations, pebble game algorithms for structural analysis, Euclidean and projective hypergraph rigidity. Collaborations : Projects with Klara Stokes, Lars-Daniel Öhman, Tovohery Randrianarisoa, and Bernd Schulze. Her publications in journals like Discrete & Computational Geometry and Discrete Applied Mathematics reflect her contributions to computational and theoretical geometry.

• Machine Learning
• Medical Technology
• Microwave Engineering
• +3 more

Yinan Yu is an Assistant Professor at the Department of Computer Science and Engineering , Chalmers University of Technology. Her research focuses on machine learning , microwave technology , and medical diagnostics . Research Interests Application of machine learning to microwave-based stroke diagnosis Development of ultra-wideband radar systems for healthcare and industrial use Feature reduction techniques in high-dimensional data analysis Scientific Contributions Published extensively on microwave-enabled prehospital stroke treatment Innovations in kernel subspace learning for medical imaging Design of compact UWB radar systems using CMOS technology

• Cryptography
• Security
• Privacy
• +3 more

Elena Pagnin serves as an Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology, where she specializes in cryptography and security research. Her academic profile centers on developing advanced cryptographic protocols that address contemporary security challenges in digital communications and data protection. Her research interests span multiple dimensions of modern cryptography including pairing-based systems, privacy-preserving protocols, and secure communication frameworks. Dr. Pagnin's work particularly focuses on optimizing cryptographic efficiency while maintaining robust security properties, with significant contributions to digital signature schemes, anonymity protocols, and verifiable delegation mechanisms. Her research bridges theoretical cryptography with practical implementations for real-world security applications. Analysis of her recent publications reveals a strong trajectory in developing innovative cryptographic solutions that address emerging security threats. Her work shows particular expertise in balancing efficiency with security guarantees, especially in pairing-based cryptography and privacy-enhancing technologies. The research demonstrates consistent advancement in cryptographic protocol design, with increasing sophistication in handling complex security requirements while maintaining practical implementation considerations. Dr. Pagnin actively contributes to the cryptographic research community through her publications in top security venues, though specific awards or honors were not mentioned in the available materials. Her research program appears well-established with consistent output of high-quality cryptographic research addressing both theoretical foundations and practical applications.

• Plasma Physics
• Fusion Energy
• Laser-Driven Particle Acceleration
• +3 more

Tünde-Maria Fülöp is a Professor at the Department of Subatomic, High Energy and Plasma Physics at Chalmers University of Technology. Her research focuses on theoretical plasma physics, particularly magnetic fusion energy and laser-driven particle acceleration. She leads a research group (http://ft.nephy.chalmers.se) exploring plasma stability, impurity dynamics, and fast particle behavior in fusion devices like tokamaks and laser-driven systems. Expertise: Plasma instability, runaway electron dynamics, and plasma-wall interactions Key Projects: ITER disruption mitigation, SPARC tokamak modeling, and DREAM framework development Her work bridges fundamental plasma physics with applied fusion engineering, addressing challenges like plasma confinement optimization and disruption prevention. Recent studies include magnetic reconnection mechanisms, pellet injection effects, and synchrotron radiation diagnostics for runaway electrons. Publications span plasma physics journals (e.g., Journal of Plasma Physics, Nuclear Fusion) and conferences (EPS, APS). Research emphasizes interdisciplinary approaches combining kinetic modeling, Bayesian optimization, and advanced simulation techniques.

• Microeconomic Theory
• Game Theory
• Mechanism Design
• +3 more

Ludvig Andersson is a Doctoral student in the Department of Economics at Lund University. His research focuses on Microeconomic Theory, with particular emphasis on Game Theory, Market/Mechanism Design, and Information Economics. He has explored topics such as the dissemination of information via matching algorithms and the behavior of boundedly rational agents in frictional search-and-matching markets. Education: Doctoral studies in Economics at Lund University. His work investigates how agent behavior impacts market allocations, contrasting outcomes with optimal allocations. No specific articles are listed in the provided text, but his research trends align with theoretical economic modeling and market design. No scientific awards or grants are mentioned in the available information. He is affiliated with the Department of Economics but no specific labs or collaborative teams are noted.

Pontus GiselssonView profile

Senior Lecturer

• Optimization
• Machine Learning
• Control Engineering
• +3 more

Pontus Giselsson is a Senior Lecturer and Director of Third Cycle Studies at Lund University's Department of Automatic Control (Faculty of Engineering). He also serves as a Senior Lecturer at ELLIIT (Linköping-Lund initiative on IT and mobile communication) and is a Profile Area Member in LTH's AI and Digitalization initiative. His research focuses on optimization algorithms for large-scale problems, particularly operator splitting methods and automated algorithm analysis. He leads projects funded by the Swedish Research Council and collaborates on initiatives like ICARUS for wireless communication systems. His research interests include convex/nonconvex optimization, algorithm convergence analysis, and applications in machine learning, control systems, and statistical estimation. He develops frameworks for unifying operator splitting methods and tools for performance estimation of optimization algorithms. Notable projects include Model Predictive Control Stability Analysis and Bregman Optimization Algorithms. Recent publications emphasize Lyapunov analysis, monotone inclusions, and frugal splitting operators. He has organized conferences such as the LCCC Focus Period on Large-Scale and Distributed Optimization. His work bridges theoretical advancements with practical applications in engineering and computational mathematics.

• Control Engineering
• Robotics and Automation
• Vehicle and Aerospace Engineering
• +3 more

Björn Olofsson is a Senior Lecturer and Docent at the Department of Automatic Control, Lund University, within the Faculty of Engineering (LTH). He serves as Director of First and Second Cycle Studies, Project Manager, and Researcher in ELLIIT and the WASP research program. His research focuses on autonomy of robots and vehicles, motion planning, and optimal control. Education: M.Sc. in Engineering Physics (2010), PhD in Automatic Control (2015), Docent (2020) Roles: Senior Lecturer, Director of Studies, Project Manager Memberships: LTH Profile Areas (AI & Digitalization, Engineering Health), LU Profile Area (Natural and Artificial Cognition) Research Interests: Robotics, autonomous systems, motion planning, optimal control for vehicles and robots. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure (SDG9) and Sustainable Cities and Communities (SDG11). Teaching: Courses include Applied Robotics, Motion Planning and Control, and supervision of over 15 PhD/Master’s students. Active in curriculum development and educational leadership. Lab Affiliation: RobotLab LTH, part of the Center for Construction Robotics.