جستجوی استاد راهنمای دانشگاهی

Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.

• Multi-agent systems
• Nonlinear control systems
• Systems theory
• +۴ مورد دیگر

Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.

• Electromagnetic Inverse Problems
• Material Characterization
• Biomedical Imaging
• +۳ مورد دیگر

Mariana Dalarsson is an Associate Professor in Electromagnetic Theory at the Division of Electromagnetic Engineering and Fusion Science (EMF) within the School of Electrical and Computer Engineering (EECS) at KTH Royal Institute of Technology. She holds an MSc (2010), PhD (2016), and Docent (2019) from KTH, where she is recognized as the (shared) second youngest woman ever to receive a PhD degree from the institution. Her research spans electromagnetic scattering and absorption, inverse problems, electromagnetics of stratified media, double-negative metamaterials, electromagnetics in medicine, antenna theory, and mathematical physics. She has authored approximately 102 peer-reviewed publications, including 51 journal papers, with recent work focusing on gold nanoparticles for biomedical applications, waveguide theory for artificial materials, and plasmonics. Analysis of her recent publications reveals a strong focus on graded metamaterials, electromagnetic wave propagation in complex media, and biomedical applications of electromagnetic theory. Her work bridges fundamental electromagnetic theory with practical applications in medical technology, particularly in the areas of nanoparticle-based treatments and diagnostic systems. Honorary Grant ("Honnörsstipendiet") for best graduate of her program (2011) L'Oréal-Unesco For Women in Science Sweden Prize (2020) Göran Gustafsson Prize for Young Researchers at UU/KTH (2024) Teaching Assistant of the Year from Engineering Physics students (2015) Mariana is highly active in teaching, serving as course responsible and examiner for EI1222 Electromagnetic Theory, EI2405 Classical Electrodynamics, and FEI3304 Integral Equation Methods in Electromagnetics. She also co-teaches several other courses and regularly supervises multiple BSc/MSc theses annually. Her research is primarily funded through her own project grants from the Swedish Research Council, including "Waveguide theory for artificial materials and plasmonics" (2019) and "Gold nanoparticles for high-frequency deep brain stimulation" (2023).

• electromagnetic scattering
• electromagnetic absorption
• inverse problems
• +۵ مورد دیگر

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +۲ مورد دیگر

Jonas Stålhand is a Professor at Linköping University, affiliated with the Department of Management and Engineering (IEI) and the Division of Solid Mechanics (SOLMEK). His research focuses on biomechanics, smart textiles, haptic technologies, and cardiovascular mechanics. He leads interdisciplinary projects such as a study on pain relief using smart textile garments, combining neuroscience, materials science, and biomechanics. His work spans arterial wall mechanics, wearable haptic systems, and biomaterial characterization. Recent projects include parameter identification in arteries and the development of electroactive yarn actuators for wearable applications. Collaborations involve multidisciplinary teams across engineering, medicine, and textile science. Research interests emphasize translating biomechanical insights into clinical and industrial applications. Notable contributions include studies on aortic stress analysis, acetabular cup stability, and electroactive polymer-based actuators. His publications address both fundamental and applied aspects of soft tissue mechanics and medical engineering. No scientific awards are explicitly listed, but his work has been highlighted in university news for its innovative potential in healthcare and technology.

• Biomechanics
• Medical Engineering
• Materials Science
• +۳ مورد دیگر

Philippe Tassin is a Professor of Physics at Chalmers University, specializing in electromagnetic structured media and computational electrodynamics. He teaches optics, quantum mechanics, and computer science courses, earning recognition through the Golden Chalk award and Chalmers' Pedagogical Prize. M.Sc. and Ph.D. (summa cum laude) from Free University of Brussels Postdoctoral work at Iowa State University and Ames Laboratory (US DOE national lab) His research spans metamaterials, plasmonics, and nanophotonics, with significant contributions to inverse design methodologies using machine learning. He has authored influential papers in Science , Nature Photonics , and Physical Review Letters , and frequently presents at international conferences. Recent publications highlight advancements in liquid metal composites for electromagnetic absorption, AI-driven metasurface design, and adaptive meshing for photonic simulations. His work integrates computational physics with experimental validation across multiple electromagnetic domains. KAW Fellowship Swedish Research Council Grant IEEE & SPIE Fellowships BAEF Alumni Award Frans Van Cauwelaert Award (Royal Flemish Academy) As editor of Photonics and Nanostructures , member of the Young Academy of Sweden, and vice-chair of IEEE Photonics Sweden Chapter, he actively contributes to academic leadership and public science communication.

• Electromagnetic Structured Media
• Metamaterials
• Plasmonics
• +۴ مورد دیگر

Torbjörn Larsson is a Professor in the Department of Mathematics at Linköping University, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical and applied optimization with significant impact in healthcare, logistics, and finance. His research interests include Mathematical Optimization , Operations Research , Brachytherapy Treatment Planning , Vehicle Routing , and Portfolio Optimization . He develops advanced algorithms such as Lagrangian heuristics, metaheuristics, and feasible direction methods to solve complex decision problems. The recent publications indicate a strong focus on developing bounding techniques and heuristic frameworks for discrete and multi-objective optimization, with applications ranging from radiation therapy to transportation logistics. His work emphasizes both theoretical rigor and practical implementation. Scientific Contributions: Development of novel optimization methods for brachytherapy treatment planning Advancement of Lagrangian and metaheuristic frameworks Application of optimization in finance (portfolio selection) and scheduling He collaborates extensively on research projects involving mathematical modeling and algorithm design. While specific advising roles are not listed, his co-authorship with junior researchers suggests mentorship activity. He has contributed to projects on decision support systems for scheduling and large-scale optimization in finance. Laboratories and Research Groups: Applied Mathematics (TIMA), Department of Mathematics, Linköping University Research environment focused on optimization and its applications in medicine and logistics

• Mathematical Optimization
• Operations Research
• Linear Programming
• +۱۰ مورد دیگر

Andreea Enache is an Assistant Professor at the Department of Economics, Stockholm School of Economics (SSE), with affiliations to the House of Governance and Public Policy, Center for Resilient Health, and Karl-Adam Bonnier Center for Governance. She holds a PhD in Mathematical Economics and Econometrics from Paris School of Economics (2015) and has held academic positions at institutions like Bocconi University, the European University Institute, and the University of Chicago. Her research focuses on structural econometrics, industrial organization, auctions, health economics, digital economics, and cultural economics. Education: PhD in Mathematical Economics and Econometrics, Paris School of Economics, 2011–2015 MSc in Economics, Toulouse School of Economics, 2010–2011 MSc in Applied Econometrics and Statistics, University of Orléans, 2009–2010 Bachelor’s Degree in Finance, Academy of Economic Studies (Bucharest), 2005–2008 Research Interests: Her work integrates econometric methods with applications in auctions, healthcare markets, digital platforms, and cultural industries. She explores topics like market competition dynamics, pricing strategies, and policy impacts using structural models and advanced statistical techniques. Publications: Recent work includes studies on Swedish primary care ownership structures, mobile app consumer behavior, and auction models. These contributions highlight her expertise in linking theoretical frameworks to real-world economic phenomena. Awards: Recipient of the PhD Thesis Prize 'Agguire-Basualdo' (2015), recognizing her doctoral research on structural econometrics and nonlinear inverse problems. Advisees & Grants: While no formal advisee list is provided, her collaborations span institutions like CEPR and involve co-authored research on healthcare, digital markets, and cultural economics. Active participation in workshops like SweRIE (2025) underscores her engagement with academic communities. Labs/Teams: Affiliated with interdisciplinary centers like the Center for Resilient Health and Karl-Adam Bonnier Center for Governance, focusing on policy-relevant research in health systems and governance structures.

• Structural Econometrics
• Industrial Organization
• Auctions
• +۴ مورد دیگر

Mats G Larson is a Professor at the Department of Mathematics and Mathematical Statistics at Umeå University. He holds the research qualification of Docent and specializes in computational mathematics, numerical analysis, and finite element methods. His work focuses on advancing numerical techniques for partial differential equations, including CutFEM, Isogeometric Analysis (IGA), and hybridized methods for complex geometries and multiphysics problems. Research interests include error estimation, stabilized finite element methods, computational mechanics, and applications in engineering and fluid-structure interaction. Larson leads projects such as the 2022–2025 'Multi-shell CutFEM for problems with mixed dimensions' and the 2017–2021 project on computational methods for elliptic problems. His publications appear in top journals like Computer Methods in Applied Mechanics and Engineering . Key contributions include developments in CutFEM for embedded surfaces, augmented Lagrangian methods for contact problems, and geometric modeling with CAD integration. His research bridges theoretical analysis and practical applications, addressing challenges in mesh generation, stability, and high-performance computing.

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۳ مورد دیگر

Eugene Smolkin is a Lecturer at the University of Gävle , specializing in Electromagnetism and Mathematical Physics . His research focuses on waveguide theory, nonlinear optics, and numerical methods for electromagnetic wave propagation in complex materials. Primary affiliation: University of Gävle Academic rank: Lecturer Research areas: Electromagnetism, Waveguide Theory, Nonlinear Optics Smolkin’s work investigates TE-polarized waves , leaky wave spectra , and graphene-coated structures . He develops numerical methods to analyze electromagnetic modes in inhomogeneous and anisotropic media, with applications in metamaterials and chiral waveguides. His publications (2015–2025) emphasize nonlinear wave propagation , dielectric layers , and inverse problems in open and shielded waveguides. Collaborative efforts with Yury Shestopalov and Yury Smirnov highlight interdisciplinary approaches.

• Electromagnetism
• Waveguide Theory
• Nonlinear Optics
• +۳ مورد دیگر

Natalia Karlsson is an Associate Professor and Lecturer at Södertörn University , specializing in Applied Mathematics and Mathematics Didactics . She serves as a Subject Coordinator and focuses on transforming mathematical concepts into accessible teaching frameworks. PhD in Mathematics and Physics (1997, Russian Academy of Sciences) Master in Applied Mathematics and Educational Sciences Licensed teacher for primary, secondary, and adult education Her research expertise spans two domains: (1) Applied Mathematics , involving mathematical modeling in nonlinear elasticity and fuzzy methods for formalizing strategy maps; and (2) Mathematics Didactics , focusing on how school-level mathematical concepts can evolve into rigorous ones without contradictions. She emphasizes task-oriented learning , diagnostic teaching , and transformative pedagogy to enhance educators and students. The 15 most recent articles highlight trends in pre-service teacher training , algebra instruction , and fractions education . Key areas include conceptual transitions from arithmetic to algebra, syntactic and substantive knowledge development, and inverse operations in teaching. Many works address proportional reasoning and multiplicative structures in classroom settings. As a supervisor , she has guided bachelor’s, master’s, and PhD students and served as an opponent for doctoral theses. She currently acts as a scientific reviewer for international journals but does not participate in active research projects.

• Applied Mathematics
• Mathematics Didactics
• Mathematical Modeling
• +۱۱ مورد دیگر

Jan Rolfes is an Associate Professor in the Department of Mathematics at Linköping University, specializing in Applied Mathematics (TIMA). He also holds concurrent research and teaching roles at RISE - Research Institutes of Sweden and the University of Erlangen-Nuremberg, Germany, where he serves as Interim Professor for Data Driven Design of Dynamical Systems. His academic affiliations span Sweden and Germany, reflecting a transnational research profile in optimization and data science. Research Interests: His work centers on optimization under uncertainty , particularly in systems affected by stochastic fluctuations such as renewable energy grids. He integrates distributional robustness to protect decisions against rare but impactful events and employs data science to model complex dependencies, such as weather impacts on energy production. His research extends into economics, engineering disciplines (electrical, chemical, vehicular), and metric and discrete geometry, using advanced mathematical tools like semidefinite and mixed-integer programming. Recent Research Trends: His latest publications (2024–2025) demonstrate a strong focus on robust optimization frameworks, geometric optimization problems, and data-driven approximations. The articles reveal a consistent methodological thread—using mathematical programming to address uncertainty in both theoretical (e.g., discrete geometry) and applied (e.g., nanotomography, power systems) domains. Keywords across these works include distributional robustness, mixed-integer programming, semidefinite programming, and data-driven modeling. Scientific Awards: No awards or fellowships are mentioned in the provided text. Advising and Grants: While no formal list of advisees is provided, Jan Rolfes has been advised by prominent researchers including Frank Vallentin, Jan Kronqvist, and Michael Jünger, indicating a strong mentorship network. He has collaborated extensively, particularly with Frauke Liers and others in optimization and computer science. No grants are explicitly mentioned, but his positions at RISE and Linköping University suggest involvement in funded research projects. Labs and Teams: He is affiliated with the Division of Applied Mathematics (TIMA) at Linköping University, which conducts research in computational mathematics, optimization, and mathematical statistics. His work at RISE connects him to the Unit of Power Systems, and his role at Erlangen-Nuremberg links him to data science and dynamical systems research teams.

• Optimization under Uncertainty
• Distributional Robustness
• Data Science
• +۷ مورد دیگر

Thomas Kalscheuer is a Senior Lecturer and Associate Professor in Applied Geophysics specializing in electromagnetic methods at Uppsala University's Department of Earth Sciences, Geophysics section. He has been with Uppsala University since 2013 and was awarded Docent (Associate Professor) status in 2017. His work focuses on developing advanced geophysical methods for subsurface imaging and characterization. His educational background includes: PhD in Geophysics from Uppsala University (2008) Diplom-Geophysiker from University of Cologne, Germany (2004) Postdoctoral Fellowship at Swiss Federal Institute of Technology, Zurich (2009-2013) Dr. Kalscheuer's research centers on the development of multidimensional numerical forward modeling and inversion schemes for geophysical electromagnetic and potential field methods. He specializes in joint interpretation of EM, seismic, potential field and borehole data, with applications spanning geotechnical engineering, hardrock geology, tectonics, hydrology, geothermal systems, and volcanology. His current work focuses on 3D inversion algorithms for natural and controlled-source EM methods, building on his earlier contributions to 1D and 2D inversion schemes, model error analysis, and resolution studies. Analysis of his recent publications (2018-2025) reveals a strong focus on advancing 3D electromagnetic inversion techniques, with particular emphasis on transfer function data processing, mesh refinement strategies, and integration of multiple geophysical datasets. His work increasingly addresses practical applications in groundwater surveying, fracture zone detection in hard rock environments, and geothermal system characterization, demonstrating the applied value of his methodological innovations. His notable recognition includes: Distinguished University Teacher award from Uppsala University (2024) Dr. Kalscheuer has supervised numerous research projects, particularly those involving collaboration between Uppsala University and international partners. His work often involves field studies in Sweden (including the Äspö Hard Rock Laboratory site), the Okavango Delta in Botswana, and various locations in Europe. He has secured research funding for projects related to electromagnetic method development and their application to geological and hydrological problems, though specific grant details are not provided in the available information. His research is conducted within the Geophysics section of Uppsala University's Department of Earth Sciences, where he collaborates closely with colleagues specializing in seismic methods, potential field methods, and geological interpretation. His work at the Äspö Hard Rock Laboratory demonstrates involvement with a major international research facility for studying deep geological processes and repository technology.

• Geophysics
• Electromagnetic Methods
• Numerical Modeling
• +۷ مورد دیگر

Emir Konuk is a Postdoctoral Researcher at KTH Royal Institute of Technology's Division of Computational Science and Technology, funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on generative models, inverse problems, and generalization in deep learning, with applications in medical imaging and healthcare. He has contributed to foundational studies on human-AI collaboration and uncertainty estimation in clinical contexts. Additionally, he teaches Applied Programming and Computer Science and Foundations of Machine Learning at KTH. Recent work includes advancements in offline foundation features using tensor augmentations and international validation of AI-driven ultrasound systems for ovarian cancer detection. His publications span top venues like MICCAI and NeurIPS. Konuk holds a Doctoral Thesis (2024) titled "Robust and generalizable AI for medical image processing" , emphasizing translational AI for healthcare challenges. Collaborations include multi-center clinical trials and theoretical contributions to neural network architecture design.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Michael Vynnycky is an Affiliated Professor at KTH Royal Institute of Technology , specializing in mathematical modeling and numerical analysis of industrial metallurgical processes. His research focuses on continuous casting , electromagnetic stirring , and fluid-structure interactions in manufacturing systems. Key Research Areas: Continuous casting of metals, fluid dynamics, heat transfer, computational methods (FEM, CFD), inverse Stefan problems, and oscillation mark formation. Collaborations: Frequent collaboration with researchers like H. Fredriksson, B. Glaser, and A. Safavi Nick. Applications: Steel production, die casting, redox flow batteries, and polymer electrolyte fuel cells. Recent publications highlight work on blast furnace dynamics , muon radiography for structural analysis, and asymptotic modeling of gas-solid flows. His methodologies emphasize mathematical rigor and industrial relevance , as seen in studies on macrosegregation and electromagnetic flow control. Techniques: Leverages asymptotic analysis multiphysics simulation finite element methods computational fluid dynamics boundary reconstruction algorithms experimental validation to solve complex industrial problems. Email Contact: michaelv@kth.se

• Continuous Casting Processes
• Mathematical Modeling of Industrial Systems
• Fluid Dynamics
• +۳ مورد دیگر