جست‌وجوی استاد و پژوهشگر

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.

• 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

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.

• Computer Science
• Artificial Intelligence
• Constraint Satisfaction Problems
• +۳ مورد دیگر

Peter Jonsson is a Professor of Computer Science and Head of Unit at Linköping University's Department of Computer and Information Science (IDA), within the Artificial Intelligence and Integrated Computer Systems (AIICS) division. He holds a PhD in Computer Science from Linköping University (1996) and has been a Professor since 2004. His research focuses on computational complexity, constraint satisfaction problems (CSP), algorithms, and planning. He has advised over 15 PhD students and supervised multiple postdoctoral researchers, contributing significantly to theoretical computer science. His work bridges algorithm design and complexity analysis, with applications in AI and planning systems. Current research interests include parameterized complexity, infinite-domain CSPs, and structural restrictions in planning. Education: PhD (1996), MSc (1993), all from Linköping University. Key publications span CSP theory, planning algorithms, and complexity classification. His advising includes notable students like Biman Roy and Victor Lagerkvist. He has collaborated on projects funded through Swedish research programs and international grants. Labs/teams: Active in the AIICS division, focusing on foundational AI and algorithmic research.

• Fluid Mechanics
• Turbulence
• Combustion Physics
• +۳ مورد دیگر

Geert Brethouwer is a Researcher at KTH Royal Institute of Technology, working within the FLOW MECHANICS group. He focuses on fluid mechanics, turbulence, and computational fluid dynamics, with expertise in reactive flows, atmospheric boundary layers, and large-eddy simulation (LES) modeling. His research emphasizes turbulence modeling, scalar transport in complex flows, and the effects of rotation, stratification, and roughness on flow dynamics. He contributes to courses such as Technical Fluid Mechanics (SG1220) and Thermodynamics (SG1216) as a teaching assistant, reflecting his commitment to both research and education in engineering sciences. His work integrates advanced numerical methods like DNS and LES to study transitional and turbulent flows, with applications in environmental fluid mechanics and combustion physics. Key research interests include Reynolds-stress modeling for stratified flows, passive scalar transport in rotating systems, and improving LES accuracy through subgrid-scale closures. His studies often address anisotropic turbulence, boundary layer transitions, and the interplay between flow instabilities and scalar mixing processes. Brethouwer’s publications span over two decades, with recent work focusing on heat/mass transfer disparities in rotating systems, turbulent combustion physics, and the explicit algebraic modeling of atmospheric boundary layers. He actively engages with computational tools like OpenFOAM to enhance LES fidelity while minimizing numerical dissipation. His research group collaborates on projects involving wall-jet flows, particle-laden turbulent flows, and the statistical analysis of high-order velocity fluctuations. Despite no awards explicitly listed in the provided texts, his extensive publication record underscores significant contributions to fluid dynamics methodologies.

Mihai Mihaescuمشاهده پروفایل

استاد

• Fluid dynamics
• Aeroacoustics
• Flow control
• +۶ مورد دیگر

Mihai Mihaescu is a Professor at KTH Royal Institute of Technology in the Department of Engineering Mechanics, School of Engineering Sciences. He holds a PhD from Lund University and has extensive postdoctoral and research experience at the University of Cincinnati. His research focuses on fluid dynamics, aeroacoustics, turbomachinery, and biofluid dynamics, addressing UN Sustainable Development Goals related to health, energy, and climate. He leads research groups and is affiliated with national competence centers like AdTherM and CCGEx. Education: PhD in Fluid Mechanics (Lund University, 2005), postdoc at UC (2005–2007), prior roles include Researcher (KTH, 2011–2014) and Associate Professor (2014–2020). Research emphasizes high-fidelity simulations for energy systems, medical applications, and noise suppression. Notable projects include H2POWRD (rotating detonation combustion), INSPIRE (hydrogen fuels), and VISION-xEV (electric vehicles). Recent articles explore supersonic jets, turbocharger dynamics, and biomedical flows. Awards include AIAA Associate Fellow (2020), Swedish Research Council grants, and the Göran Gustafsson Young Scientist Award (2012). Supervised over 20 PhD students and postdocs, contributing to 160+ publications. Active in editorial and leadership roles, including Deputy Head for Education and roles in ASME and AIAA committees.

• Fluid Mechanics
• Structural Mechanics
• Nonlinear Dynamics
• +۳ مورد دیگر

Arne Nordmark is a Lecturer at the Department of Mechanics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the FLOW MECHANICS research group and actively involved in course coordination, examination, and teaching roles across multiple mechanics and fluid mechanics programs. His primary responsibilities include managing courses such as Mechanics I with Project (SG1132), Mechanics II (SG1140), and advanced fluid mechanics degree projects. Nordmark’s work focuses on computational mechanics, structural stability, and multiphysics modeling, with significant contributions to the analysis of membrane systems and impact oscillators. His research interests span fluid mechanics, structural mechanics, nonlinear dynamics, and applied mathematics. He has explored topics like turbulence on spherical geometries, stability of hyper-elastic membranes under pressure loads, and bifurcation phenomena in nonsmooth dynamical systems. His recent work emphasizes the development of numerical methods for stability analysis and multiphysics couplings in engineering simulations. Nordmark’s publications from 2020–2024 reflect a strong focus on structural and fluid-structure interaction problems, particularly in modeling membrane behavior under various conditions. He has investigated wrinkling mechanics, parametric instabilities, and the application of finite element methods to complex systems. He has no listed scientific awards or formal advisees. His contributions include curriculum development and experimental studies on mechanical systems with impacts, though specific grant details are not provided in the text.

Johan Karlssonمشاهده پروفایل

استاد

• Optimization and Systems Theory
• Inverse Problems
• Large Scale Optimization
• +۴ مورد دیگر

Johan Karlsson is a Professor in the Department of Mathematics at KTH Royal Institute of Technology, Sweden. He serves as Associate Director Executive Research at Digital Futures, a cross-disciplinary research center focusing on digital technologies for societal challenges. He holds a PhD in Optimization and Systems Theory from KTH (2008) and an MSc in Engineering Physics (2003). His research focuses on inverse problems, optimization, model reduction, and their applications in remote sensing, signal processing, and control theory. He leads the Decision-making in Critical Societal Infrastructures (DEMOCRITUS) project and collaborates on initiatives like the Lindquist Symposium in Systems Theory. Teaching includes advanced courses such as Optimal Control and Convexity and Optimization in Linear Spaces . He supervises PhD students and has authored/co-authored numerous papers in journals like SIAM Journal on Control and Optimization and IEEE Transactions on Automatic Control . His work integrates optimal transport theory, control systems, and computational methods to address complex engineering and environmental challenges. Key affiliations include KTH’s Department of Mathematics and Digital Futures, with collaborations across academia and industry. His research group actively engages in workshops, conferences, and interdisciplinary projects to advance theoretical and applied aspects of optimization and systems theory.

Ingo Sanderمشاهده پروفایل

استاد

• Embedded Systems Design
• Models of Computation (MoCs)
• Mixed-Criticality Systems
• +۳ مورد دیگر

Ingo Sander is a Professor in Electronic Systems Design at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty and the Division of Electronics and Embedded Systems. He joined KTH in 1993 and has held his current professorship since 2018. His research focuses on formal system design methodologies like ForSyDe, emphasizing embedded systems, mixed-criticality applications, and design automation. He co-founded the cross-disciplinary Digital Futures research center, which addresses societal challenges through digital technology innovation. Education: MSc in Electrical Engineering (Technical University of Braunschweig, 1990), PhD and Docent at KTH (2003, 2009). Professional experience includes work at Ericsson (1991–1993). Research Interests: Design methodologies for embedded systems, models of computation (MoCs), formal verification, and cyber-physical systems. Key contributions include the ForSyDe framework and design space exploration techniques for multiprocessor platforms. His work bridges theoretical foundations with practical implementations, targeting safety-critical and high-performance embedded systems. Teaching: Ingo Sander supervises numerous master’s degree projects in computer engineering, electrical engineering, and ICT innovation. He leads courses on embedded software, systems design, and simulation. Labs & Projects: Digital Futures collaborates with Stockholm University and RISE, advancing innovations in digital technologies. Sander’s projects include the SAFEPOWER initiative for energy-efficient mixed-criticality systems and CONTREX for control systems design.

• Autonomy of robots and vehicles
• Motion planning
• Optimal motion control
• +۶ مورد دیگر

Björn Olofsson is an Associate Professor and Senior Lecturer in the Department of Automatic Control at Lund University's Faculty of Engineering. He also serves as the Director of First and Second Cycle Studies and is a Project Manager. He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). His academic affiliations span Lund University and Linköping University, where he was appointed Docent in 2020. He holds an M.Sc. in Engineering Physics and a Ph.D. in Automatic Control, both from Lund University. His academic journey reflects a strong foundation in engineering and control systems. His research focuses on the autonomy of robots and vehicles, with emphasis on motion planning and optimal motion control. He explores applications in ground vehicles, unmanned aerial and surface vehicles, and industrial robotics. His work intersects with key global challenges, including sustainable transport and digitalization, aligning with UN Sustainable Development Goals related to technology and health. The 15 most recent publications analyzed show a consistent trend in autonomous systems, predictive control, and robotics. Topics include uncertainty-aware motion planning, human-robot collaboration, maritime autonomy, and learning-based control. The research integrates AI, machine learning, and advanced control theory, applied across aerial, marine, and terrestrial domains. Björn actively supervises multiple PhD students and has led numerous research projects, such as ELLIIT B14 and the Center for Construction Robotics. He is involved in organizing academic events like Robotics Week for Schools and manages the RobotLab LTH infrastructure. He has taught a range of courses including Applied Robotics, Autonomous Vehicles, and graduate-level courses on motion planning and optimal control. He also supervises Master’s theses in Automatic Control and Vehicular Systems.

• Structural Mechanics
• Materials Science
• Mechanical Engineering
• +۳ مورد دیگر

Magnus Burman is a researcher at the Royal Institute of Technology (KTH) in the Department of Solid Mechanics , focusing on structural mechanics and composite materials. He contributes to teaching and course coordination in disciplines such as Fiber Composites , Strength of Materials , and Vessel Technology . Role: Teacher, Examiner, Course Coordinator Contact: Email mburman@kth.se , Phone +46 70 549 64 50 Research Interests center on lightweight structural design, ice-ship interaction mechanics, and composite material behavior under dynamic loads. His work bridges computational modeling and experimental validation for applications in marine, aerospace, and mechanical systems. Scientific Publications (2019–2023) address topics like finite element analysis of ice loads , sandwich panel design for Arctic conditions , and failure mechanisms in composite joints . Key trends include the integration of machine learning for predictive ship performance modeling and probabilistic methods for ice pressure analysis.

• Railway Energy
• Sustainability
• Traction and Braking Systems
• +۸ مورد دیگر

William Liu is a Researcher at KTH Royal Institute of Technology's Department of Vehicle Engineering and Technical Acoustics since 2020, focusing on railway energy and sustainability. He has extensive experience in railway systems development, having contributed to high-speed rail projects (up to 421 km/h) and over 20 research projects, yielding 60 publications. His work emphasizes energy efficiency, safety, and innovative systems like maglev and hyperloop. Education: He holds a Doctor of Philosophy (2017), Licentiate (2015), and Master's (2009) in Mechanical Engineering from KTH and Tianjin University. He is also a Docent in Rail Vehicle Technology (expected 2025). Research interests include traction systems, electric power supply optimization, pantograph-catenary dynamics, climate adaptation, and circular economy applications in rail transport. He teaches multiple courses, including Rail Vehicle Technology and Energy Technologies for Sustainable Transport at the bachelor and master levels, and advises doctoral students in areas like braking systems and vehicle dynamics under adverse conditions. Recent research trends in his articles focus on smart controllers for decarbonization, pantograph-catenary interaction dynamics, and hyperloop technology. He co-supervises PhD projects and mentors over 20 thesis students, addressing topics such as energy optimization for commuter trains and circular economy quantification. He is a member of the Swedish Electrical Standardization Committee and leads the KTH Delsbo Electric Team, actively shaping industry standards and next-gen rail innovation.

• Railway Engineering
• Vehicle-Track Dynamics
• Transportation Systems
• +۴ مورد دیگر

Professor Mats Berg is a leading academic at KTH Royal Institute of Technology, specializing in railway technology and vehicle dynamics. He holds the position of Professor in Railway Technology since 2003 and has served as Head of the Rail Vehicles unit (2004–2023). His roles include Board membership of the KTH Railway Group and editorial roles in journals like Journal of Rail and Rapid Transit and Vehicle System Dynamics . Education: PhD in Structural Mechanics (Lund University, 1987), MSc in Civil Engineering (Lund University, 1980). Research focuses on vehicle-track dynamic interaction, rail vehicle suspension dynamics, and energy efficiency in rail systems. Key contributions include state-of-the-art papers on rail vehicle dynamics simulation, suspension modeling, and energy-efficient vehicle design. He has authored/co-authored textbooks such as Rail Vehicle Dynamics (2021) and contributed to the Handbook of Railway Vehicle Dynamics . Recent articles explore topics like wheel-rail contact mechanics, smart energy systems in rail transport, and instability detection algorithms. His work has positioned him among the top 2% globally cited researchers in Mechanical Engineering & Transports (2020). Supervised 11 PhD students and actively participates in projects like Shift2Rail, focusing on decarbonization and energy labeling. Leads the Rail Vehicles unit and collaborates with industry partners like ABB Traction. His research integrates computational modeling, experimental validation, and real-world applications to advance sustainable rail technologies.

Olga Mulaمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +۳ مورد دیگر

Olga Mula is an Associate Professor of Mathematics at TU Eindhoven and will transition to a Full Professor position at the University of Vienna starting September 2025. Her research focuses on numerical analysis of Partial Differential Equations (PDEs) combined with data-driven methodologies, including Scientific Machine Learning, High-Dimensional Approximation, and Optimal Transport. She leads the Computational PDEs group and has held roles at institutions like INRIA Paris and Paris Dauphine University. Mula's work addresses challenges in computational science, such as pollution modeling, epidemiology, and nuclear engineering. She currently seeks PhD and Postdoc candidates for projects on Wasserstein Gradient Flows and singularly perturbed PDEs. Education: PhD in Applied Mathematics (Sorbonne University, 2014), French Habilitation (Paris Dauphine University, 2021) Research Interests: Machine Learning for PDEs, Data Assimilation, Numerical Optimal Transport Her contributions include advancements in PINN frameworks, model order reduction, and sensor placement strategies. She teaches courses on Numerical Analysis, Optimization, and Mathematics of Neural Networks at TU Eindhoven.

Eddie Wadbroمشاهده پروفایل

استاد

• Topology Optimization
• Inverse Problems
• Large-Scale Optimization
• +۷ مورد دیگر

Eddie Wadbro is a Professor of Mathematics at Karlstad University , Sweden. His research focuses on design optimization , inverse problems , and large-scale computational methods , with applications in acoustic device design , fluid dynamics , and mechanical engineering . He leads projects in topology optimization of antennas, loudspeakers, and acoustic systems, often involving multi-physics simulations. Key contributions include: Development of minimum-size control techniques for topology optimization Investigation of waveguide acoustic black holes for enhanced wave focusing Optimization of superhydrophobic surfaces to delay turbulent transition in boundary layers Integration of deep reinforcement learning for real-world control systems His work bridges applied mathematics and engineering , with publications in high-impact journals and collaborations across disciplines like computational fluid dynamics and machine learning.