Find Academic Supervisors

• Acoustics
• Vibration Engineering
• Finite Element Analysis
• +5 more

Romain Rumpler is an Associate Professor at the Department of Vehicle Engineering and Technical Acoustics, KTH Royal Institute of Technology. His research focuses on numerical methods and modeling for coupled acoustics and vibration applications, including finite element modeling, design optimization, and acoustic metamaterials. He leads initiatives in transportation noise, such as noise impact assessment and traffic strategies. He is affiliated with the Centre for Eco2 Vehicle Design and has been funded by the Swedish Research Council, FORMAS, VINNOVA, and European programs like Shift2Rail and CIVITAS. His teaching includes courses like Building Acoustics and Community Noise and Noise and Vibration Control . Education: PhD in Vehicle Engineering and Technical Acoustics, KTH Royal Institute of Technology (2012) Research Interests: His work spans efficient finite element methods, acoustic material design, and urban noise mitigation. He develops experimental-numerical approaches for noise assessment and contributes to eco-friendly vehicle design through projects like the EU VAMOR initiative. Recent efforts include metamaterials for sound insulation and real-time noise mapping techniques. Articles Trends: Romain's publications emphasize transportation noise analysis, structural acoustics, and computational methods. Key themes include noise detection algorithms, parametric model reduction, and material characterization for vibration suppression. His work bridges theoretical models with practical applications in urban planning and vehicle engineering. Awards: 2022 Supervisor of the Year Award (Centre for Eco2 Vehicle Design) Grants & Advising: Funded projects include VR, FORMAS, and EU grants. He advises students on acoustic metamaterials and noise control, with a focus on sustainable transportation solutions. His team collaborates on agent-based noise impact models and low-frequency vibration mitigation. Labs & Projects: Associated with Digital Futures initiatives (DIRAC, GEOMETRIC, SENZ-Lab) and the Centre for Eco2 Vehicle Design. His work supports eco-efficient vehicle design and smart traffic strategies to reduce environmental impact.

• Software Architecture
• Software Testing
• Agile Methodologies (Scrum/XP)
• +4 more

Martin Blom is an Associate Professor at the Department of Computer Science, Karlstad University since 1996. His research focuses on software architecture, software testing, agile methodologies (Scrum/XP), code metrics, and semantic programming. He has authored/co-authored over 25 journal/conference papers and three book chapters. Education: Licentiate in Computer Science (Karlstad University, 2002) PhD in Computer Science (Karlstad University, 2006) Research Interests: Architectural degradation detection using code metrics Testability and software performance analysis Agile methodologies in complex system development Documentation methods and reusability in software maintenance Semantic aspects in component-based systems Key Article Trends: Recent work emphasizes empirical studies on software architecture recovery, code smell detection's applicability to architectural degradation, and systematic mapping studies on testability. Earlier contributions focused on contract-based programming and design contracts' role in error management. Labs/Teams: Currently collaborates with multiple local businesses on applied research projects. Active in software engineering education integrating empirical research into teaching.

Jan CarlsonView profile

Professor

• Software Engineering
• Embedded Systems
• Model-Based Development
• +5 more

Jan Carlson is a Professor at Mälardalen University's School of Innovation, Design and Engineering, specializing in the Division of Computer Science and Software Engineering. His primary focus is on software engineering, particularly model-based and component-based development of embedded systems. He earned his M.Sc. from Linköping University (2000) and a doctoral degree from Mälardalen University (2007) with a thesis on event pattern detection for embedded systems. Research interests include allocation optimization, timing analysis, code generation, real-time systems, and safety-critical component reuse. His work often addresses challenges such as cache-aware response time analysis, limited preemption scheduling, and integration of GPU components in embedded systems. He has contributed to projects like SCALL (a component allocator for heterogeneous systems) and Rubus (a GPU-aware component model). His research also explores continuous integration practices in model-based development and decision support systems for component selection. Key contributions include studies on architectural change management, ontology development for systems of systems, and tools for consistency checking between system models and implementations. He has authored over 80 publications, with recent work focusing on performance prediction, resource utilization, and safety-critical system integration. No notable awards are mentioned, though his extensive publication record highlights significant academic impact. Jan Carlson has advised numerous students and research teams but no explicit student names are listed in the provided text. His work is closely tied to industrial collaboration, addressing real-world challenges in automotive, railway, and smart industry domains. He actively participates in workshops and conferences, such as MARCH and ModComp, to advance model-driven engineering practices.

• Electrical Engineering
• Bioelectricity
• Computational Electromagnetics
• +12 more

Luca Giaccone is an Associate Professor in the Department of Energy (DENERG) at Politecnico di Torino, where he is also a member of the Academic Senate and the Committee for Teaching Coordination. He is affiliated with the College of Electrical and Energy Engineering and leads the CADEMA Research Group. His academic work focuses on computational electromagnetics and human exposure to electromagnetic fields. Research Interests: Bioelectricity and human exposure to low-frequency electric and magnetic fields Computational electromagnetics and numerical dosimetry Simulation engineering, modeling, and optimal design using MATLAB and Python Electromagnetic compatibility (EMC) and safety of medical devices Wireless power transfer (WPT) systems, especially in automotive applications Recent Research Trends: His recent publications center on data-driven modeling of electric motors, surrogate modeling for fast exposure assessment, and the electromagnetic safety of medical implants near WPT systems. He frequently employs learning-based human models and investigates uncertainties in dosimetry, especially under pulsed or multi-frequency exposures. Scientific Awards and Recognition: Mentoring Polito Project (M2P), awarded by Politecnico di Torino on February 16, 2023 Advising and Grants: He supervises PhD student Junlong Liu in Electrical, Electronics, and Communications Engineering. He leads or participates in multiple funded research projects, including WPT-SAFE (2025–2027, national PRIN), SAFECHARGE (2024–2026, international), and various corporate consulting projects on electromagnetic exposure from welding devices, photovoltaic systems, and industrial cabins. He has served as Scientific Manager on over ten consulting and research contracts since 2015. Labs and Teams: He is a key member of the CADEMA Research Group (DENERG), focusing on computational electromagnetics and electromagnetic safety applications in energy, medicine, and the environment.

Irina BouianovaView profile

Professor

• Semiconductor Physics
• Spintronics
• Nanomaterials
• +7 more

Irina Bouianova is a Professor and Head of Unit in the Department of Physics, Chemistry and Biology (IFM) at Linköping University, Sweden. She leads research in the Functional Electronic Materials unit, focusing on advanced semiconductor materials for next-generation electronic and photonic applications. Research Interests: Her work centers on semiconductor physics, spintronics, nanomaterials, and functional electronic materials. Key areas include exciton dynamics in nanowires, defect passivation via hydrogenation, and room-temperature spin transport in quantum structures. Her research bridges fundamental physics with applications in quantum computing and energy-efficient electronics. The recent publications (2024–2025) highlight her leadership in understanding localized excitons, spin-conserved transport, and antiferromagnetic coupling in novel semiconductors. These works reflect a strong trend toward quantum materials, defect engineering, and sustainable semiconductor technologies. Scientific Awards and Grants: SEK 20 million research grant from the Swedish Energy Agency and Swedish Research Council for energy-oriented basic research Advising and Funding: As a senior professor and unit head, she mentors researchers and leads large-scale collaborative projects. She has secured significant long-term funding, enabling sustained research in semiconductor spintronics and energy materials. Her work involves multi-institutional collaborations, as seen in studies on organic solar cells and topological insulators. Labs and Teams: She is part of the Advanced Functional Materials and Electronic and Photonic Materials divisions at IFM, Linköping University. Her team specializes in optical spectroscopy, nanomaterial synthesis, and spin-dependent transport measurements, contributing to breakthroughs such as room-temperature spin-light interfaces for quantum computing.

Yehia Abd AlrahmanView profile

Senior Lecturer

• Formal Methods
• Model Checking
• Reconfigurable Systems
• +3 more

Yehia Abd Alrahman is a Senior Lecturer at the Department of Formal Methods within the University of Gothenburg. His work focuses on formal methods, model checking, and reconfigurable systems, with particular emphasis on multi-agent systems and attribute-based communication. He develops verification tools like R-CHECK and contributes to theoretical foundations for collective adaptive systems. His research integrates formal verification techniques with practical applications in distributed systems and energy grids. Key research interests include formal verification of reconfigurable systems, attribute-based communication models, and correct-by-design teamwork plans for multi-agent systems. He has published extensively on topics such as bisimulation theory, distributed coordination frameworks, and protocol analysis. His work often bridges theoretical computer science with practical implementation in domains like power grid control and system resilience. Academic contributions span programming language design for CAS, verification frameworks, and pedagogical approaches to formal methods education. Collaborations include projects on runtime verification, distributed API design, and adaptive system coordination protocols. Current research trends emphasize enhancing system correctness through automated synthesis and rigorous formal methods.

Jun YuView profile

Professor

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +7 more

Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.

Rebekka WohlrabView profile

Assistant Professor

• Requirements Engineering
• Software Architecture
• Self-Adaptive Systems
• +4 more

Rebekka Wohlrab is a tenure-track Assistant Professor in Software Engineering at Chalmers University of Technology (Gothenburg, Sweden) and an adjunct faculty member at Carnegie Mellon University’s Software and Societal Systems Department (S3D). Her research focuses on requirements engineering, self-adaptive systems, and robotics. She leads a research group supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Education: PhD in Computer Science and Engineering (Chalmers, 2020), MSc/BSc in Computer Science (Paderborn University, 2016). Postdoc at Carnegie Mellon University (2020-2022). Research interests include requirements conflicts resolution, quality attribute trade-offs, and ethical monitoring in cyber-physical systems. She has published extensively on adaptive systems, architectural tactics, and agile development practices. Notable contributions include frameworks for robot mission planning, security assurance cases, and traceability management in automotive systems. Recent awards include a pedagogical prize from students at Chalmers. She advises PhD students on topics like self-adaptive robotics and WASP-funded projects. Her work spans theoretical research and industry collaboration (e.g., Volvo Cars). Active in conferences like ICSE, RE, and ICSA as a program committee member. Labs/Teams: Leads the Self-Adaptive Systems Research Group at Chalmers, collaborating with WASP and automotive industry partners.

• Fluid Dynamics
• Nanocellulose Materials
• Biopolymer Assembly
• +4 more

Fredrik Lundell is Professor of Fluid Mechanics at KTH Royal Institute of Technology, where he leads research in fluid physics, biopolymer assembly, and experimental mechanics. His work bridges fundamental fluid dynamics with applications in sustainable materials, utilizing techniques including synchrotron imaging, microfluidics, and optical coherence tomography. Research encompasses nanocellulose processing, protein nanofibril assembly, turbulence modulation, and multiphase flows. Recent work emphasizes flow-directed assembly of hierarchical biomaterials and energy-efficient fabrication methods. Experimental approaches combine advanced imaging with microfluidic platforms to study complex fluid-structure interactions. Publications demonstrate innovations in nanofiber alignment control, sustainable material design, and multiphase flow characterization. Recent articles focus on microfluidic assembly strategies, interfacial phenomena, and non-Newtonian fluid dynamics. Advises doctoral students investigating diverse topics from turbulent suspensions to biopolymer physics. Directs laboratory facilities for flow measurements and materials characterization, collaborating extensively through the Wallenberg Wood Science Center.

Chao RenView profile

Researcher

• Quantum Federated Learning
• Federated Learning
• Quantum Computing
• +3 more

Chao Ren is a Wallenberg-NTU Presidential Principal Researcher (Forskare) at KTH Royal Institute of Technology's Department of Intelligent Systems. He holds a Ph.D. from Nanyang Technological University (NTU), Singapore, where he received the prestigious Graduate College Research Excellence Award. His research focuses on Quantum Federated Learning, Power Engineering, and Big Data Analytics for smart grid stability assessment. Ren has held postdoctoral fellowships at NTU and KTH, collaborating with leading experts like Prof. Mikael Skoglund (IEEE Fellow). Education: Ph.D. in Interdisciplinary Graduate Programme, NTU (2017–2022) B.E. in Computer Science, Nanjing University of Aeronautics and Astronautics (2013–2017) Research Interests: Combining quantum computing with federated learning to enhance cybersecurity and efficiency in power systems. Key areas include dynamic security assessment, adversarial robustness, and battery degradation prediction. His work bridges theoretical advancements and practical applications in smart grids and distributed systems. Awards & Grants: Winner of Graduate College Research Excellence Award (NTU, 2022) Wallenberg AI, Autonomous Systems & Software Program (WASP) grant (SEK 300,000) NTU grant for 'Modern Smart Grid Stability Assessment' (S$100,000) Professional Activities: Guest Lecturer at KTH, Editorial Board member for IET AI for Engineering, and reviewer for top conferences like NeurIPS and AAAI. Active in organizing workshops on federated learning and quantum computing. Labs & Teams: Leads the Trustworthy Federated Ubiquitous Learning Research Lab (AI Singapore-TrustFUL) and collaborates with the Singapore Power Group-NTU Joint Lab. Focuses on interdisciplinary projects involving energy systems, AI, and quantum technologies.

• Materials Science
• Metallurgy
• Materials Engineering
• +3 more

Alexander Dahlström is a Lecturer at the KTH Royal Institute of Technology, where he serves as a teacher and course coordinator for 'Material Design I (MH1030)'. His research focuses on advanced materials science, particularly in metallurgical systems, nanostructure evolution, and high-performance alloys. Key areas include microstructural analysis under thermal and mechanical loads, phase transformations in metallic alloys, and computational modeling of material behavior. His work integrates experimental techniques such as atom probe tomography and thermal treatments to investigate fundamental material properties. Notable research themes include high-entropy alloys, embrittlement mechanisms in stainless steels, and wear behavior of austenitic stainless steels. Dahlström contributes to bridging computational thermodynamics with practical material design, emphasizing real-world applications in engineering systems. Although no scientific awards or grants are explicitly mentioned, his active publication record highlights sustained contributions to metallurgy and materials engineering. No lab affiliations or student advisees are listed in the provided information.

• Materials Science
• Metallurgy
• Mechanical Engineering
• +3 more

Mohammad Hoseini Athar is a Researcher at the Department of Materials Science and Engineering (MSE) at KTH Royal Institute of Technology, affiliated with the Hultgren Laboratory for Materials Characterization. He earned his PhD in Materials Science and Engineering from the University of Tehran in 2019 and has since held postdoctoral and researcher roles at KTH. His work focuses on understanding microstructure-mechanical property correlations in engineering materials, combining experimental mechanical testing with advanced materials modeling. Current projects include leading EU-funded initiatives such as REPAM (powder production/recycling optimization), Steel4Fatigue (high-strength steels for mobility), and NEXT (neutron/synchrotron-based materials innovation), as well as Vinnova-funded projects like RECaSt (durable cast steel design). His research emphasizes fatigue behavior, residual stress analysis, and alloy development for industrial applications. He leads a team of postdoctoral researchers and doctoral students, specializing in experimental and computational analysis of materials under extreme conditions. His interdisciplinary approach bridges materials characterization, mechanical behavior modeling, and sustainable manufacturing processes.

• Energy Materials
• Solid-State Batteries
• Materials Science
• +3 more

Raquel Lizarraga Jurado is an Associate Professor (Docent) in Energy Materials at Kungliga Tekniska Högskolan (KTH). She holds a PhD in Physics from Uppsala University and completed postdoctoral research at Los Alamos National Laboratory. Her research focuses on atomistic modeling and first-principles methods applied to energy materials, particularly solid-state Li/Na-ion batteries, glassy electrolytes, and non-collinear magnetism in transition metals. She contributes to sustainable energy systems aligned with UN SDGs 7 (Affordable Energy) and 13 (Climate Action). Education: PhD in Physics, Uppsala University Postdoctoral Research, Los Alamos National Laboratory Docent in Energy Materials, KTH (2023) Research interests include computational materials modeling, electrochemical stability of battery materials, and phase transitions in alloys. Her work bridges fundamental physics with industrial applications in hardmetals and energy storage. She teaches Additive Manufacturing (MG2044) . Research trends: Recent publications address 2D heterostructures for photovoltaics, cemented carbide mechanics, and high-entropy alloy design. These studies emphasize computational approaches to predict material performance and guide sustainable innovation. Labs/Teams: Active within KTH's Unit of Properties, focusing on experimental/theoretical synergy in materials development.

• Fluid Mechanics
• Mechanical Engineering
• Aerospace Engineering

Diego Ignacio Diaz Diaz is a Postdoctoral fellow at the FLOW MECHANICS division of KTH Royal Institute of Technology. He is affiliated with the university's research and collaboration initiatives, and his work address is located at Osquars Backe 18 in Stockholm, Sweden. His research interests are centered around flow mechanics and related disciplines, including fluid dynamics and computational methods in engineering. While specific projects are not detailed here, his affiliation with the FLOW MECHANICS group suggests a focus on theoretical or applied fluid systems.

• Fluid Mechanics
• Shock Wave Dynamics
• High Energy Density Physics
• +3 more

Michael Liverts is an Associate Professor in the Department of Fluid Mechanics at KTH Royal Institute of Technology. He specializes in shock wave dynamics, fluid mechanics, and high-energy density physics. His work focuses on experimental and computational studies of shock wave interactions, blast mitigation using foams, and advanced measurement techniques for dynamic pressures and temperatures. Liverts teaches courses such as Compressible Flow, Sustainable Development Methodology in Mechanics, and Mechanics I/II. He coordinates and examines multiple advanced-level degree projects in fluid mechanics and mechanics. Research interests include shock wave convergence, real gas effects, underwater electrical explosions, and multiphase flow dynamics. His recent studies explore cylindrical shock wave behavior, supersonic jet generation via blast focusing, and the critical parameters governing wire explosion dynamics. Liverts has developed novel calibration methods for sensors measuring extreme pressures and temperatures (up to 3000°C) with sub-millisecond response times. Publications span over two decades, with a focus on shock wave phenomena, thermal inhomogeneity effects, and foam-based blast mitigation strategies. His work bridges fundamental fluid mechanics with applied engineering solutions, particularly in safety systems and energy research. Liverts collaborates on European metrology projects (e.g., DynPT) and has contributed to high-current generator design for high-energy-density matter studies. Teaching responsibilities include course coordination for core mechanics courses and advanced compressible flow modules. His pedagogical approach integrates research insights into curriculum design, emphasizing sustainable development principles in engineering education.