Find Academic Supervisors

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +6 more

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.

Xiongfei WangView profile

Visiting Professor

• Power Electronics
• Power System Stability
• Data-Driven Control
• +3 more

Xiongfei Wang is a Visiting Professor at KTH Royal Institute of Technology in Stockholm, Sweden, and a Digital Futures Fellow. He holds academic leadership roles, including Editor-in-Chief of IEEE Transactions on Power Electronics and chairs in IEEE committees. His research focuses on power electronics systems, stability analysis, and data-driven approaches for energy systems. Previously, he served as a Professor at Aalborg University (2009–2022). His work addresses challenges in power system stability, converter dynamics, and renewable integration. He leads projects like 'Machine Learning for Dynamics Analysis of Power Electronic Systems,' aiming to enhance grid resilience through AI-driven methods. Notable awards include IEEE Fellow (2023), Isao Takahashi Award (2022), and multiple best paper awards. Wang collaborates extensively, organizing conferences and editing journals. His research spans power electronics, grid stability, and smart grid technologies, with a focus on practical applications in energy transition.

Vladimir BastidasView profile

Senior Lecturer

• EU Competition Law
• Intellectual Property Law
• Legal Economics
• +2 more

Vladimir Bastidas is a Senior Lecturer in Competition Law and Head of Department at the Department of Law, Uppsala University. He has held this position since 2013 after previously serving as a Senior Lecturer in Law at Örebro University, with expertise spanning EU competition law, intellectual property, and legal economics. Education: PhD in Law, Stockholm University (2011); Thesis: EU competition law rules on technology transfer agreements His research centers on EU competition law, particularly Articles 106-108 TFEU concerning privileged undertakings, while exploring intersections with intellectual property, healthcare, and digital innovation. He examines state aid law dynamics and economic implications of competition frameworks. Recent publications (2021-2023) reveal a pronounced focus on applying competition law to emerging sectors, including digital ecosystems (Google Android), biotechnology (human genome editing), and algorithmic pricing. His work consistently bridges traditional legal principles with novel technological challenges. Scientific awards: No scientific awards mentioned Advising and grants: No information provided on graduate students advised No research grants documented No laboratory or research team affiliations are specified in available materials.

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics (CFD)
• +4 more

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.

• Artificial Intelligence in Healthcare
• Precision Medicine
• Machine Learning Applications in Healthcare
• +5 more

Magnus Boman is a Professor of AI and Health at the Department of Medicine, Solna, Karolinska Institutet (KI), where he leads the AI@KI initiative to support researchers in AI integration. He is affiliated with the Chronic Inflammatory Disease Epidemiology research group under Johan Askling. His research focuses on AI applications in precision medicine, multimodal prediction, ethical norms in AI systems, energy-efficient computing, and quantum sensor data interpretation. Research Interests: Artificial Intelligence in healthcare and precision medicine Multimodal data analysis for disease prediction and treatment Machine learning for clinical decision support systems Ethical and societal implications of AI Grants: Swedish Research Council: Improving breast cancer histology image classification (2024-2026) Scalable Federated Learning (2022-2025) Ai in sustainable cities (VINNOVA, 2019) Advising & Students: Supervised over 50 PhD and Master's students across KI, KTH, and Stockholm University, focusing on AI applications in healthcare, machine learning, and computational epidemiology. Notable projects include predictive modeling for mental health outcomes and variant filtering in genetic data. Labs & Teams: Leads AI@KI, fostering AI adoption in medical research. Collaborates with the Johan Askling group on epidemiology and chronic disease studies.

Elina RönnbergView profile

Professor

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +18 more

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

• Array Antennas
• Millimeter-Wave Communication
• Antenna Integration with Electronics
• +4 more

Marianna Ivashina is a Professor and Head of the Antenna Systems Research Group at Chalmers University of Technology's Department of Electrical Engineering . Her work focuses on array antennas , antenna integration with electronics , optimal beamforming , and over-the-air measurement methods . The group has achieved international recognition for innovations in ultra-wideband (UWB) feeds , Gap waveguide antennas , and Doherty-power-amplifier-integrated antennas for 5G/6G and radio telescope applications. Key projects include the SSF Sweden-Taiwan collaboration , EU Horizon 2020 MyWave , and VINNOVA ENERGETIC initiatives. Her recent publications emphasize millimeter-wave (mmWave) communication and reconfigurable intelligent surfaces (RIS) , with applications in 5G/6G networks , satellite communication (SatCom) , and advanced antenna testing chambers . She explores beamforming optimization , self-interference mitigation , and hybrid OTA environments to enhance wireless system performance. The group's work bridges theoretical advancements with practical implementations, including RFSoC testbeds and high-efficiency antenna arrays . Marianna leads major research programs funded by Ericsson , VINNOVA , and EUREKA EURIPIDES2 , addressing challenges in beamforming , antenna-IC integration , and automated design for 5G/6G . These projects highlight her role in advancing millimeter-wave communication and sensor integration technologies.

Olof SundinView profile

Professor

• Disinformation
• Generative AI
• Search Engines
• +5 more

Olof Sundin is Professor of Information Studies at Lund University's Department of Arts and Cultural Sciences, where he serves as Manager of Research and Research Education at the Division of ALM, Digital Cultures and Publishing Studies. He coordinates the research node Dīcit: Disinformation, Conspiracy Theories, and Infrastructures of Information. His research examines sociomaterial configurations and control mechanisms of digital information, focusing on: Disinformation dynamics and generative AI's impact on expertise/trust Search engine functionality in everyday contexts Media/information literacy as infrastructural meaning-making Evolution of information-seeking behaviors in AI-driven environments Recent publications demonstrate strong focus on AI's transformative impact on information literacy practices, source evaluation frameworks, and emerging digital market behaviors. His work consistently intersects technology, society, and epistemological concerns. Awards include: Best paper award (2025) Källkritikpriset: The golden magnifying glass 2023 (National award from Swedish Internet Foundation) Sundin leads multiple research initiatives: RE-SEARCH project (Swedish Research Council): Analyzing youth information-seeking in AI-infused educational environments 'Who is the Expert?' project (Crafoord Foundation): Investigating generative AI's impact on expertise and evidence Lund lead for 'The Digital Welfare State' project Member of Nordic Disinformation Resilience Network He coordinates the Dīcit research node and leads doctoral education in Information Studies, teaching courses on digital libraries and information institutions.

• X-ray Optics
• Nanoimaging
• Synchrotron Radiation
• +3 more

Ulrich Vogt is a Professor in Applied Physics at Kungliga Tekniska Högskolan (KTH) and leads the X-ray Optics and Nanoimaging group within the Bio-Opto-Nano unit. He serves as Vice-head of the Applied Physics department for undergraduate education. His research focuses on developing advanced X-ray microscopy techniques, particularly at synchrotron facilities like MAX IV’s NanoMAX beamline. He specializes in X-ray optics, nanoimaging, and diffractive optical elements for applications in materials science, biology, and medicine. Key contributions include the design of the NanoMAX beamline, optimization of X-ray zone plates via metal-assisted chemical etching, and advancements in multi-beam ptychography. Vogt has pioneered compact X-ray microscopy systems using laser-plasma sources and liquid-jet targets. His work integrates nanofabrication, computational imaging, and synchrotron instrumentation to achieve sub-100 nm resolution in hard and soft X-ray regimes. Teaching responsibilities include courses on experimental physics, photonics, and X-ray applications. His lab collaborates internationally on projects like the European XFEL, emphasizing high-brightness sources and radiation-resistant optics. Recent innovations include adaptive multi-beam ptychography and stereo X-ray imaging for 3D nanoscale visualization. Research highlights span over 100 peer-reviewed articles, with a focus on coherence characterization, beamline instrumentation, and nanostructured materials. Vogt’s grants include a Röntgen-Ångström Cluster award supporting multi-beam ptychography and cryo-microscopy advancements.

Martin BerggrenView profile

Professor

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

Per EnqvistView profile

Associate Professor

• Optimization
• Operations Research
• Systems Engineering
• +6 more

Per Enqvist is an Associate Professor in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology, Stockholm, Sweden. He has held this position since 2009 after progressing from Assistant Professor (2006-2009) and post-doctoral roles at INRIA France and CNR Italy. His academic background includes: Ph.D. in Optimization and Systems Theory from KTH (2001), supervised by Professor Anders Lindquist M.Sc. in Engineering Physics (Civilingenjör) from KTH (1994) with Applied Mathematics focus Post-doctoral studies at INRIA Sophia-Antipolis (2003-2004) and CNR Padova (2001-2003) Enqvist's research centers on mathematical modeling of stochastic processes, scheduling, and queueing theory with applications across operations research, systems engineering, and signal processing. His principal interests span Optimization, Operations Research, Systems Engineering, Signal Processing, Mathematical Systems Theory, and Modeling and Simulation. He has made significant contributions to spectral estimation, covariance interpolation, and resource allocation frameworks. Publication analysis reveals an evolution from foundational systems theory work (2000s) on spectral estimation and minimal realization toward applied optimization in healthcare operations (2010s-2020s). Recent articles address radiation therapy scheduling and contact center modeling using queueing theory with risk-sensitive measures like CVaR, while earlier work established theoretical frameworks for covariance interpolation and passive system synthesis. No scientific awards are documented in the provided information. He has received funding from Vetenskapsrådet (Swedish Research Council) and led the ACCESS seed project on "Robust Spectral Estimation". Enqvist is course responsible for multiple master's program tracks including Aerospace systems and Industrial Engineering, and oversees the Optimization and Systems Theory seminar series. No student advisement details are provided. He maintains affiliations with the ACCESS Linnaeus centre, Center for Industrial and Applied Mathematics (CIAM), and serves on the Swedish Operations Research Society (SOAF) board.

• Ergonomics
• Digital Human Modeling
• Manufacturing Systems
• +5 more

Lars Hanson is a Professor of Product Design Engineering at the University of Skövde's School of Engineering Science. His research focuses on ergonomics, digital human modeling, and optimizing manufacturing systems with a strong emphasis on human well-being and sustainable production. He leads projects like LITMUS (Industry 4.0 to 5.0 transition) and has contributed to developing tools such as IPS IMMA for ergonomic simulations. Active in virtual verification of human-robot collaboration and smart textile systems for workplace safety Published extensively in journals like International Journal of Human Factors Modelling and Simulation and IEEE Access Editor of conference proceedings and contributor to industry standards in automotive and healthcare sectors Research interests include multi-objective optimization of factory layouts, musculoskeletal risk assessment, and integrating ergonomic evaluations into product design processes. Current projects address Industry 5.0 sustainability challenges through digital twin technologies and smart manufacturing solutions.

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +5 more

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Mathematical Finance
• Probability Theory
• Stochastic Analysis
• +5 more

Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.