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Anna LukinaView profile

Assistant Professor

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +4 more

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

Daniel AxehillView profile

Professor

• Autonomous Systems
• Optimization-Based Control
• Sensor Fusion
• +4 more

Professor Daniel Axehill is affiliated with the Department of Electrical Engineering (ISY) at Linköping University, specializing in planning and optimization-based control for autonomous systems. His research bridges theoretical developments and industrial applications. His recent work focuses on robust motion planning for autonomous vehicles, optimal task and motion planning algorithms, execution-time analysis for model predictive control, and high-performance solvers for multi-parametric quadratic programming. Key methods include lattice-based planning, disturbance estimation, and real-time optimization. He contributes to the Wallenberg Autonomous Systems Program (WASP), collaborating on advancements in robotics, sensor fusion, and complex network control systems.

Zhenxia LiuView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Large Deviations
• +3 more

Zhenxia Liu is an Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). Her work contributes to theoretical and applied probability, with a focus on stochastic processes and statistical modeling. Her research interests lie at the intersection of mathematical statistics and probability theory. Key areas include large deviations , longest runs in Markov chains , and Monte Carlo methods . These topics are central to understanding rare events, sequential dependencies, and numerical estimation techniques in complex systems. The recent publications demonstrate a consistent focus on probabilistic analysis of dependent structures, particularly through Markov models. Her work combines theoretical rigor with applications in computational statistics, showing trends toward improving bounds and simulation efficiency in stochastic modeling. Mathematical Statistics Probability Theory Large Deviations Markov Chains Monte Carlo Methods Computational Mathematics Zhenxia Liu has actively contributed to high-quality journals such as Statistics and Probability Letters , Results in Applied Mathematics , and Probability and Mathematical Statistics . While no formal advising or grant information is available in the provided text, her collaborative publications suggest engagement in research networks within applied mathematics. She is part of the research environment in Applied Mathematics at Linköping University, which focuses on computational mathematics, optimization, and mathematical modeling across science and engineering disciplines.

Andrew WintersView profile

Associate Professor

• Numerical Analysis
• Computational Mathematics
• Partial Differential Equations
• +12 more

Andrew Winters is a Senior Associate Professor in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where he conducts research in computational mathematics and numerical methods for partial differential equations. His research focuses on the design and analysis of high-order numerical schemes, particularly nodal discontinuous Galerkin (DG) methods with summation-by-parts (SBP) properties, for solving hyperbolic and mixed hyperbolic-parabolic PDEs such as shallow water, Euler, Navier-Stokes, and magnetohydrodynamic (MHD) equations. His work emphasizes conservation, entropy stability, and thermodynamic consistency in numerical approximations. The recent publications highlight a strong trend in developing robust, high-order, entropy-stable methods for nonlinear conservation laws, with applications in fluid dynamics and geophysical modeling. His work integrates theoretical analysis with high-performance computing, particularly through the development of the FLUXO and Trixi.jl simulation frameworks. Energy Bounds for Discontinuous Galerkin Spectral Element Approximations Entropy Stable Hydrostatic Reconstruction Efficient Implementation of Entropy Stable DG Methods Adaptive Simulations with Trixi.jl Subcell Finite Volume Shock Capturing Andrew Winters is actively involved in software development and scientific computing education, including an introductory Fortran course for MATLAB users. He contributes to international collaborations, such as a four-way research and exchange program between Linköping University and Washington State University. He has no listed scientific awards in the provided text. He advises students in computational mathematics, though specific names are not mentioned. He is a core developer of the FLUXO (Fortran/MPI), Trixi.jl (Julia), and HOHQMesh.jl projects, which support high-order simulations and mesh generation.

• Electric Vehicle Engineering
• Battery System Design
• Autonomous Vehicle Optimization
• +5 more

Jelena Andric is an Associate Professor in the Vehicle Engineering and Autonomous Systems department at Chalmers University of Technology, working within the Mechanics and Maritime Sciences school. Her research spans multiple aspects of vehicle engineering with a particular focus on electric and autonomous vehicle technologies. Her research interests primarily center around electric vehicle engineering, battery system design, autonomous vehicle optimization, thermal management systems, computational fluid dynamics, reinforcement learning applications, and hardware-in-the-loop simulation. She has developed expertise in applying advanced computational methods to solve complex vehicle engineering problems, particularly in the areas of battery management and energy optimization. Analysis of her recent publications reveals a strong trend toward optimizing electric vehicle performance, particularly focusing on battery degradation mitigation, thermal management, and energy efficiency. Her work increasingly incorporates artificial intelligence techniques, especially reinforcement learning, to develop sophisticated optimization approaches for electric trucks and autonomous vehicles. The research spans from fundamental fluid dynamics studies to applied engineering solutions for real-world transportation challenges. Dr. Andric has been actively involved in numerous research projects funded by organizations including VINNOVA, the Swedish Energy Agency, CHAIR, and ÅForsk. These projects cover diverse areas such as battery aging prediction, vehicle management strategies for electrified vehicles, electric public transit systems, and virtual engine calibration. Her current research portfolio includes leadership in projects related to simulation-based evaluation of connected vehicles, battery aging prediction for electric autonomous vehicles, AI-cloud-based vehicle management strategies, and transition to electric public transit systems. These projects demonstrate her commitment to addressing critical challenges in sustainable transportation through advanced engineering solutions.

• System Identification
• Signal Processing
• Machine Learning
• +6 more

Cristian R. Rojas is a Professor at the Division of Decision and Control Systems within the School of Electrical Engineering and Computer Science at Kungliga Tekniska Högskolan (KTH Royal Institute of Technology) in Stockholm, Sweden. He has been affiliated with KTH since October 2008, advancing from his initial position to his current professorship. His academic career focuses on control theory, system identification, and related fields. Dr. Rojas received his M.S. degree in electronics engineering from the Universidad Técnica Federico Santa María in Valparaíso, Chile, in 2004, followed by his Ph.D. in electrical engineering from The University of Newcastle, NSW, Australia, in 2008. Professor Rojas's research spans system identification, signal processing, and machine learning, with particular emphasis on developing methods for optimal input design, sparse system identification, and continuous-time system modeling. His work bridges theoretical foundations with practical applications in control systems engineering, focusing on creating efficient algorithms for system identification that balance computational complexity with estimation accuracy. He has made significant contributions to understanding coherence properties in system identification and developing methods for unstable system identification in closed-loop configurations. An analysis of Professor Rojas's recent publications reveals a strong focus on sparse system identification techniques, continuous-time system modeling, and application-oriented input design. His work consistently addresses the challenge of balancing theoretical rigor with practical implementation constraints, particularly in the areas of coherence minimization, computational efficiency, and closed-loop system identification. The research demonstrates a clear evolution toward increasingly sophisticated methods for handling nonlinear systems and unstable dynamics while maintaining statistical consistency. Associate Editor for IFAC journal Automatica Associate Editor for IEEE Control Systems Letters (L-CSS) Member of IEEE Technical Committee on System Identification and Adaptive Processing (since 2013) Member of IFAC Technical Committee TC1.1. on Modelling, Identification, and Signal Processing (since 2013) As an educator, Professor Rojas supervises numerous degree projects across various specializations including Machine Learning, Systems Control and Robotics, and ICT Innovation. He teaches core courses such as Machine Learning Theory (EL2810) and Modelling of Dynamical Systems (EL2820), demonstrating his commitment to both theoretical foundations and practical applications in control systems education. His academic leadership extends to course development and examination responsibilities across multiple engineering programs. Professor Rojas is embedded within the Division of Decision and Control Systems at KTH, a research environment dedicated to advancing the theoretical and practical aspects of control theory, system identification, and decision-making systems. His collaborative work with researchers like Håkan Hjalmarsson, James S. Welsh, and others has established him as a key contributor to the international control systems community.

Mats G LarsonView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +3 more

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.

• Computational Fluid Dynamics
• High-Performance Computing
• Turbulence Simulation
• +3 more

Martin Karp is a Research Fellow and postdoctoral researcher at KTH Royal Institute of Technology's Department of Engineering Mechanics, working under Dan Henningson. His research focuses on high-fidelity numerical simulations of turbulence and transition, with a specialization in high-performance computing (HPC) and supercomputing architectures. He holds a PhD in computer science from KTH and an MSc in Engineering Physics from Lund University, complemented by studies at ETH Zürich's computer science department. His research interests explore computational limits in nonlinear chaotic systems and future computational advancements. He leads the development of the Neko framework, a scalable simulation tool for extreme-scale CFD with extensive accelerator support. Karp's work emphasizes GPU and FPGA acceleration, parallel computing, and optimizing algorithms for heterogeneous architectures. Key contributions include large-scale turbulence simulations using GPUs, reducing communication in conjugate gradient methods, and evaluating FPGA-based flow solvers. His publications span journals like Concurrency and Computation and Scientific Reports , with conference presentations at IEEE Cluster, PASC, and HPCAsia. Karp's research bridges theoretical computational limits and practical HPC implementation, addressing challenges in precision, scalability, and hardware utilization. His educational background combines engineering physics with computer science, enabling interdisciplinary approaches to fluid dynamics and high-performance simulation. Current projects aim to push the boundaries of computational fluid dynamics through novel algorithm design and leveraging emerging hardware capabilities.

Ricardo VinuesaView profile

Associate Professor

• Artificial Intelligence
• Fluid Mechanics
• Sustainability
• +7 more

Ricardo Vinuesa is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mechanics within the School of Engineering Sciences. He serves as the Principal Investigator (PI) for projects such as 'An AI-based framework for harmonizing climate policies and projects with the SDGs' and co-leads the 'Faster-than-real-time and high-resolution simulation of fluid flow in engineering applications' initiative. His work integrates artificial intelligence with fluid mechanics, sustainability, and climate policy. Education and Academic Roles: Vinuesa teaches courses including Data-driven methods in engineering mechanics, Mechanics I, and Turbulence, holding roles as examiner, course manager, and teacher. He is part of the Digital Futures Faculty and actively contributes to academic programs at KTH. Research Interests: His research focuses on AI-driven solutions for fluid mechanics challenges, turbulence modeling, sustainable development goals (SDGs), and climate policy alignment. He develops machine learning frameworks for flow control, sensor optimization, and climate risk assessment, emphasizing interdisciplinary applications. Projects and Applications: Key projects include AI-based frameworks for SDG harmonization, high-resolution fluid simulations for indoor climate, and turbulence control via reinforcement learning. His work bridges computational fluid dynamics with real-world sustainability challenges, leveraging deep learning and data-driven methodologies. Labs and Collaborations: Vinuesa leads the Artificial Intelligence Group, with a lab website at vinuesalab.com . His research is supported by collaborations with industry and academic partners, focusing on advancing fluid mechanics and environmental engineering through AI innovation.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +1 more

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

• Intelligent Transportation Systems
• Traffic Flow Modeling
• Linear Parameter Varying Systems
• +7 more

Professor Balázs Adam Kulcsár is a faculty member in the Automatic Control research group at the School of Electrical Engineering and Computer Science, Chalmers University of Technology. With 104 publications and involvement in 34 research projects, he is a prominent researcher in intelligent transportation systems. His work spans multiple domains within transportation engineering and control theory, with significant contributions to traffic flow modeling, electric vehicle routing, and advanced control systems. Professor Kulcsár's research primarily focuses on intelligent transportation systems design, traffic flow modeling for control, Linear Parameter Varying systems, and failure diagnostics. His work demonstrates a strong integration of control theory with practical transportation challenges, particularly in the context of electric mobility and sustainable transportation. Recent research shows a growing emphasis on machine learning applications for transportation optimization, electric vehicle infrastructure, and urban traffic management. Analysis of his recent publications reveals a clear trajectory toward sustainable transportation solutions, with electric vehicle charging infrastructure, fleet management, and public transit optimization as dominant themes. His work increasingly incorporates machine learning techniques, particularly graph neural networks and reinforcement learning, to address complex transportation challenges. The research demonstrates strong interdisciplinary collaboration across engineering disciplines, with a focus on practical implementation of theoretical advances. Professor Kulcsár leads and participates in numerous research projects focused on future transportation systems, including projects on electric mobility, traffic optimization, and intelligent transportation infrastructure. His research group collaborates extensively with industry partners like Volvo and Heart Aerospace, as well as with other academic institutions. Current projects include Rethinking the Sustainability of V2G, Quantum computing for future mobility solutions, and Digital Twin for Energy Prediction. His research group maintains strong connections with transportation industry stakeholders and contributes to major initiatives such as the Transport Area on Advance project, which aims to achieve leading competence in future green, safe, and efficient transport systems. The team operates at the intersection of theoretical control systems and practical transportation applications, with particular expertise in modeling complex traffic phenomena and developing implementable control solutions.

• Wireless Communication
• 5G/6G Technologies
• Radio Networks
• +5 more

Slimane Ben Slimane is an Associate Professor and University Lecturer at the Royal Institute of Technology (KTH) in Stockholm, Sweden, where he is affiliated with the School of Electrical Engineering and Computer Science. His academic position combines teaching responsibilities with active research in communication systems. Dr. Ben Slimane's research focuses on critical areas of modern wireless communications: Wireless Communication Systems and 5G/6G Technologies Radio Network Design and Optimization Signal Processing for Communication Systems Network Coding and Cooperative Communications IoT Security and Industrial Wireless Networks Cognitive Radio and Spectrum Management His publication record demonstrates a strong progression from fundamental wireless communication research to addressing contemporary challenges in next-generation networks. Recent work focuses on mmWave communications, beam alignment issues in 3GPP standards, C-RAN architectures for beyond 5G, and security solutions for industrial IoT deployments. His research consistently bridges theoretical frameworks with practical implementation challenges faced by industry. At KTH, Dr. Ben Slimane serves as examiner and course coordinator for numerous advanced courses including Wireless Communication Systems (IK2507), Radio Networks (IK2510), and Signal Processing (II1303). He also oversees degree projects across multiple programs in computer science, electrical engineering, and information technology. Dr. Ben Slimane actively contributes to academic development through examination of bachelor's and master's theses, with particular focus on communication systems, embedded systems, and ICT innovation specializations. His teaching portfolio reflects the interdisciplinary nature of modern communication engineering education at KTH.

• Pumped Hydro Energy Storage
• Contra-Rotating Pump-Turbines
• Fluid Dynamics
• +5 more

Jonathan Fahlbeck is a Visiting Researcher in the Fluid Dynamics department within the Mechanics and Maritime Sciences school at Chalmers University of Technology. His research focuses on low-head pumped hydro energy storage systems, particularly examining the operational characteristics of contra-rotating pump-turbines through both experimental and computational approaches. Dr. Fahlbeck completed his Doctoral thesis in 2024 titled Flow in contra-rotating pump-turbines at stationary, transient, and cavitating conditions , following a Licentiate thesis in 2022 titled Low head pumped hydro storage with contra-rotating pump-turbines . His educational background demonstrates a specialized focus on renewable energy systems and fluid machinery. His research interests center on the development and optimization of contra-rotating pump-turbines for low-head applications, with particular emphasis on transient operational behaviors including startup and shutdown sequences, cavitation effects, and variable speed control. His work bridges experimental fluid dynamics with computational modeling to address practical challenges in energy storage systems. Analysis of his recent publications reveals a clear progression toward increasingly sophisticated investigations of pump-turbine dynamics, with growing emphasis on control strategies, efficiency optimization, and grid integration aspects of low-head pumped hydro storage. His research shows strong alignment with renewable energy integration challenges and grid stability requirements. Dr. Fahlbeck is a key contributor to the ALPHEUS project (2020-2024), which aims to enhance grid stability through low-head pumped hydro energy utilization and storage, funded by the European Commission. His collaborative work primarily involves researchers Håkan Nilsson and Saeed Salehi within the Fluid Dynamics department at Chalmers.

• Geophysics
• Electromagnetic Methods
• Numerical Modeling
• +7 more

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.

• Randomized algorithms
• Machine learning
• Big Data
• +3 more

Devdatt Dubhashi is a Professor at the Data Science and AI 3 department at Chalmers University of Technology. His research spans multiple domains within computer science and data science, with a focus on theoretical foundations and practical applications of algorithms and machine learning. Professor Dubhashi's primary research interests include the design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. His work demonstrates a strong interdisciplinary approach, connecting theoretical computer science with practical applications in diverse fields such as quantum computing, transportation modeling, genomics, and social sciences. His research often bridges the gap between theoretical foundations and real-world implementations, as evidenced by his numerous collaborations across different domains. Dubhashi's recent publications reveal a consistent research trajectory focusing on algorithmic foundations of machine learning, with increasing emphasis on interdisciplinary applications. His work shows significant contributions to bandit algorithms, kernel methods, graph-based learning, and the theoretical understanding of deep learning models. There's also a growing focus on societal implications of AI, as seen in his publications addressing responsible AI development and policy considerations. Among his notable scientific contributions are publications in prestigious venues including ACM, IEEE, and Nature journals, covering topics from fundamental algorithm design to applications in computational biology and social sciences. His work has been cited extensively across multiple disciplines, reflecting its broad impact. Professor Dubhashi actively supervises PhD students and collaborates with researchers across Chalmers and internationally. His research group appears to focus on the intersection of theoretical computer science and practical machine learning applications, with projects spanning quantum computing, transportation modeling, and biological applications.