جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Information Fusion
• Data Analytics
• +۷ مورد دیگر

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.

Adam Faragoمشاهده پروفایل

استاد

• Financial Economics
• Asset Pricing
• Portfolio Choice
• +۳ مورد دیگر

Adam Farago is a Professor of Financial Economics at the University of Gothenburg's Department of Economics. He holds a PhD in Finance from the Stockholm School of Economics (2014) and a Docent degree from the University of Gothenburg (2022). His research focuses on Financial Economics, Asset Pricing, Portfolio Choice, Behavioral Finance, and Financial Econometrics. He serves as Programme Coordinator for the MSc in Finance program and has held visiting lecturer positions at the Stockholm School of Economics. Farago has received several research grants and awards, including the Best Dissertation in Finance at SSE (2014) and multiple grants from institutions like VINNOVA and the Marianne and Marcus Wallenberg Foundation. His work frequently explores long-horizon returns, portfolio rebalancing strategies, and the behavioral aspects of financial decision-making. He has advised numerous PhD candidates and contributes to academic discussions through presentations at global conferences. Education: PhD in Finance (SSE, 2014), MA Economics (Central European University, 2009), MA Economics (University of Debrecen, 2007). Research Interests: Asset Pricing Dynamics Behavioral Portfolio Decisions Long-Term Investment Strategies Financial Market Efficiency Recent Work Trends: Focus on long-horizon stock return distributions, cognitive factors in fund management, and tax implications for wealth portfolios. Articles emphasize empirical methods and policy-relevant findings.

• Formal Language Theory
• Machine Learning
• Semantic Parsing
• +۳ مورد دیگر

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)

Ali Dadrasمشاهده پروفایل

پژوهشگر ارشد

• Federated Learning
• Optimization Algorithms
• Convex Optimization
• +۳ مورد دیگر

Ali Dadras is a Research Fellow at the Department of Mathematics and Mathematical Statistics, Umeå University. His work focuses on federated learning, optimization algorithms, and signal processing. He is affiliated with research groups in Mathematical Programming and Statistical Learning and Inference for Spatio-Temporal Data. Key research interests include communication-efficient federated learning systems, convex optimization techniques, and biomedical signal processing applications. His recent projects explore personalized models in federated frameworks and low-rank matrix factorization for distributed learning. Publications emphasize advancements in federated learning architectures, non-smooth optimization, and medical diagnostics through signal analysis. He contributes to interdisciplinary projects like Compressive Sensing and Statistical Learning with Sparsity (2019–2024). No awards are explicitly listed, but his active participation in international conferences (e.g., ICONIP 2024) reflects scholarly engagement.

Josephine Solowiej-Wedderburnمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Biology
• Mathematical Biology
• Mechanobiology
• +۴ مورد دیگر

Josephine Solowiej-Wedderburn is a multidisciplinary postdoctoral fellow at Umeå University's IceLab and Department of Mathematics and Mathematical Statistics. Supervised by Eric Libby, Peter Lind, Ludvig Lizana, and Björn Schröder, she focuses on predicting co-evolutionary trajectories of microbial communities through mathematical modeling, network science, and computational approaches. Her work bridges theoretical biology with environmental influences on fundamental biological processes. Current affiliation: IceLab, Umeå University Academic rank: Research Fellow Research focus: Co-evolutionary dynamics, microbial interactions, mechanobiology Contact: josephine.solowiej-wedderburn@umu.se Her research integrates network science, mathematical modeling of cellular contractility, and endosymbiosis analysis. Recent projects include modeling bacterial interaction plasticity across environments and exploring how focal adhesion distribution affects cell force generation. Collaborations with Korea-IceLab highlight her international engagement in microbial eco-evolutionary studies. Prior to Umeå, Josephine completed her PhD in mechanobiology at the University of Surrey under Carina Dunlop, investigating cell-substrate adhesion patterns and mechanosensing. Her postdoctoral work there extended to cytoskeletal-nuclear coupling models.

Konrad Abramowiczمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +۴ مورد دیگر

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 Lundowمشاهده پروفایل

دانشیار

• Statistical Mechanics
• Spin Glasses
• Combinatorics
• +۳ مورد دیگر

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.

Signe Lundqvistمشاهده پروفایل

پژوهشگر

• Discrete Mathematics
• Geometry
• Graph Theory
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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 Giselssonمشاهده پروفایل

مدرس ارشد

• Optimization
• Machine Learning
• Control Engineering
• +۳ مورد دیگر

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.

Lara Labanمشاهده پروفایل

پژوهشگر

• Robotics
• Control Engineering
• Unmanned Aerial Vehicles (UAV)
• +۴ مورد دیگر

Lara Laban is a PhD Researcher affiliated with Lund University's School of Aviation (LUSA) and the Department of Automatic Control at Lund University. She is part of the LTH Profile Area: AI and Digitalization and the ELLIIT initiative. Her research focuses on enhancing autonomous UAV mission planning, integrating modified RRT* algorithms with non-linear model predictive control (NMPC) for obstacle avoidance in complex environments. Education and Background: Lara’s academic journey includes studies in automatic control and robotics, though specific degree details are not provided. Collaborations include WARA Public Safety, where she conducted autonomous flight demonstrations in locations like Gränsö Slott and Lund Golf Course. She is also a WASP (Wallenberg AI, Autonomous Systems and Software Program) affiliated PhD student. Research Themes: Her work bridges automatic control and artificial intelligence, addressing challenges in autonomous UAV navigation, beyond visual line-of-sight (BVLOS) operations, and airspace integration with manned/unmanned aircraft. Key technical areas include path planning, nonlinear systems, and real-time optimization. Teaching and Outreach: Lara has been a teaching assistant for courses such as Automatic Control Basics (FRTF05), Non-Linear Systems (FRTF05), and the project course FRTN70. She co-supervised a master’s thesis on minimizing network utilization in multi-agent systems. She contributed to the Robotics Week for Schools 2023 and organized an AI Lund Lunch seminar on UAV path planning. Labs and Projects: Active in the RobotLab LTH and the UAS@LU project (2021–2025), focusing on autonomous flight systems. Her research involves field experiments at LTH Kemicentrum and collaboration with industry partners like WARA Public Safety.

• Control Engineering
• Robotics and Automation
• Vehicle and Aerospace Engineering
• +۳ مورد دیگر

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.