جستجوی استاد راهنمای دانشگاهی

Dimos V. Dimarogonasمشاهده پروفایل

استاد

• Multi-Agent Control Systems
• Robot Navigation
• Task Planning
• +۱۱ مورد دیگر

Dimos V. Dimarogonas is a Professor of Automatic Control at the Division of Decision and Control Systems , School of Electrical Engineering and Computer Science , KTH Royal Institute of Technology . His research focuses on multi-agent control systems, hybrid systems, and formal verification with applications to robotics and autonomous systems. Alumnus of National Technical University of Athens (NTUA) Postdoctoral experience at KTH and MIT Research areas include: Multi-agent coordination under temporal logic specifications Hybrid control and planning for autonomous robots Formal verification of safety-critical systems Networked control with limited communication Robust distributed control protocols Applications to precision agriculture, underwater vehicles, and space robotics Recent publications emphasize stochastic control , funnel-based safety , and transient control for leader-follower networks. Scientific accolades include ERC Consolidator Grant (2019) , Wallenberg Academy Fellow (2015) , and ERC Starting Grant (2014) . Supervises numerous PhD students and postdocs, with former advisees now holding academic positions at institutions like Caltech, MIT, and Oxford.

• 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.

Oskar Kvimanمشاهده پروفایل

پژوهشگر

• Bayesian phylogenetics
• Probabilistic machine learning
• Variational inference
• +۱۵ مورد دیگر

Oskar Kviman is a doctoral student at KTH Royal Institute of Technology working in the Lagergren Lab within the Division of Computational Science and Technology. His research bridges machine learning, statistics, and computational biology with a focus on developing and applying advanced probabilistic methods. His primary research interests include: Bayesian phylogenetics and probabilistic machine learning Variational inference, variational auto-encoders, and sequential Monte Carlo methods Generative AI techniques including flow matching, Schrödinger bridges, and diffusion models Computational cancer research focusing on differential expression testing and spatial transcriptomics Kviman's publication record demonstrates significant contributions to variational inference methodology, particularly in phylogenetics and generative modeling. His work spans top machine learning conferences including ICML, NeurIPS, and AISTATS, showing consistent development of techniques that improve efficiency and accuracy in probabilistic modeling. Recent publications focus on multi-marginal flow matching, variational resampling, and mixture learning in black-box variational inference. He has been recognized for his peer review contributions as a Top reviewer (10%) for AISTATS 2023. Kviman has supervised master's theses for Xindi Liu and Ricky Molén at KTH and serves as a lecturer for 'Statistical Methods in Applied Computer Science' since 2021, while previously working as a teaching assistant for 'Machine Learning, Advanced Course' and 'Deep Learning, Advanced Course'.

• Control Theory
• Embedded Systems
• Distributed Systems
• +۶ مورد دیگر

Alessandro Vittorio Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the University of Málaga's Institute for Software Technology and Software Engineering. His research focuses on modeling and control of interconnected systems under uncertainty, integrating Control Theory with Embedded Systems , Distributed Systems , and Real-Time Systems . He has served as a Scientific Advisor for companies like ABB and Zero Point Technologies. Education: PhD from Politecnico di Milano (2013), supervised by Alberto Leva; Postdoctoral work at Politecnico di Milano and Lund University's Department of Automatic Control under Karl-Erik Årzén. Research Trends: His articles emphasize Swarm Robotics , Stochastic Scheduling , Privacy-Preserving Control , and Edge-Cloud Continuum . Key subfields include Distributed Control , Uncertainty Modeling , Real-Time Optimization , and Secure Communication . Awards: He received the Most Influential Paper Award at SEAMS 2025, QUALIFICA Fellowship , and Outstanding Paper Award at ECRTS 2021, alongside a Best Artefact Award in 2017. Advising: Supervised PhD theses by Anna Friebe and Daniel Bujosa Mateu. Labs & Collaborations: Affiliated with the Lund Center for Control of Complex Engineering Systems (LCCC) and MDU's research groups.

Eva Frühwald Hanssonمشاهده پروفایل

مدرس ارشد

• Timber Structures
• Biobased Materials
• Wood-Moisture Relations
• +۵ مورد دیگر

Eva Frühwald Hansson serves as Senior Lecturer and Head of Office in the Division of Structural Engineering at Lund University's Department of Building and Environmental Technology. She holds additional leadership roles as Deputy Head of Department and contributes to two LTH Profile Areas: Circular Building Sector and The Energy Transition. Lund University, Faculty of Engineering (LTH) Department of Building and Environmental Technology Division of Structural Engineering Her research centers on timber structures and biobased materials, with specialization in wood-moisture dynamics, durability prediction, and sustainable building envelopes. Key focus areas include probabilistic hygrothermal modeling, mold risk assessment, and circular construction methodologies. Her work directly supports UN Sustainable Development Goals through sustainable building technologies. Recent publications demonstrate strong trends in machine learning applications for mold prediction (with Random Forest algorithms), probabilistic service life modeling, and innovative use of hygroscopic materials for energy efficiency. Research spans building physics, structural engineering, and environmental sustainability with emphasis on practical implementation. Scientific recognition includes: Best Research into Practice Paper Prize (2019) She actively supervises doctoral students and leads significant research initiatives, including five major projects funded by Vinnova and industry partners. Her grant portfolio emphasizes biobased construction, industrialized building systems, and service life performance frameworks. Hansson co-manages two critical university infrastructures: the Civil Engineering Laboratory and the Structural Engineering Laboratory, supporting experimental research in timber structures and building materials.

• Machine Learning
• Artificial Intelligence
• Health Informatics
• +۳ مورد دیگر

Jens Lundström is a Senior Lecturer at the School of Information Technology, Halmstad University. His research and teaching focus on Machine Learning and Artificial Intelligence, particularly their applications in healthcare, patient experience, and quality of life improvement. Research Area Leader for AI in CAISR Health Coordinator for Halmstad Professionals network (AI, data analytics, cybersecurity, service design) His research integrates applied and theoretical Machine Learning with healthcare domains, covering synthetic data generation, smart home systems, and data privacy. Recent publications highlight his contributions to Explainable AI, Generative Adversarial Networks, and robustness in Deep Learning for health and traffic data. No specific scientific awards or students are mentioned in the provided texts.

Kateryna Morozovskaمشاهده پروفایل

پژوهشگر

• Computational Methods for Energy Systems
• Physics-Informed Machine Learning
• Renewable Energy Integration
• +۳ مورد دیگر

Kateryna Morozovska is a Researcher at the KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems and the School of Electrical Engineering and Computer Science . Her work bridges computational methods and energy systems development, focusing on physics-informed machine learning for renewable energy integration and power transformer optimization. Education B.S. and M.S. in Electrical Mechanics from Zaporizhzhya National Technical University (2013) European Energy Masters program with mobility at DTU (Denmark), TU Delft (Netherlands), and NTNU (Norway) PhD in Electrical Engineering from KTH (2020) on 'Dynamic rating for applications in renewable energy' Licentiate from KTH (2019) Research Focus Kateryna's research emphasizes Physics-Informed Neural Networks (PINNs) for power system optimization, including transformer thermal modeling, cellulose degradation analysis, and renewable energy integration. Her projects explore dynamic rating techniques to enhance grid efficiency and sustainability, funded by Vinnova and applied in PV-power plants and wind farms. Scientific Contributions She has developed frameworks for transformer cost analysis, wind farm sizing, and sensor placement optimization using PINNs and MILP. Her work addresses environmental trade-offs in wind energy, such as raw material mining impacts, and investigates thermodynamic challenges in nanocellulose and power systems. Affiliations Kateryna collaborates with industry partners through the PINN Summer School and contributes to SweGRIDS and Mendeley communities. Her teaching includes hands-on PINN training and multi-GPU machine learning.

• Quantitative Finance
• Optimal Transport
• Stochastic Control
• +۶ مورد دیگر

Marcel Nutz is a Professor at Columbia University in the Department of Statistics , with secondary affiliations in the Department of Mathematics and the Data Science Institute . He holds a PhD in Mathematics from ETH Zurich and is recognized for his contributions to quantitative finance and optimal transport . His research integrates stochastic control , game theory , and machine learning to address problems in financial markets and probabilistic modeling. Key themes include martingale transport , regularized optimal transport , and mean-field analysis , with applications to market microstructure and risk management . Marcel’s recent work focuses on regularization techniques to mitigate the curse of dimensionality in optimal transport, stability analysis of variational methods, and martingale Schrödinger bridges for portfolio optimization. His publications emphasize numerical algorithms like Sinkhorn’s method and theoretical guarantees for financial models. Scientific Awards: IMS Medallion Award Alfred P. Sloan Fellowship IMS Fellow Marcel acknowledges support from five NSF grants and the Center for Digital Finance and Technologies . He currently serves on the editorial boards of Annals of Applied Probability (AAP), Mathematical Finance (MF), and others, with past roles at Stochastic Processes and their Applications (SPA) and Finance and Mathematics (FMF).

• Control Theory
• Optimal Transport
• Machine Learning
• +۶ مورد دیگر

Anqi Dong is a Postdoctoral Fellow at KTH Royal Institute of Technology. They hold a BSc in Mechanical and Automation Engineering from Harbin Institute of Technology (2017) and a PhD in Mechanical and Aerospace Engineering from the University of California, Irvine (2023), supervised by Prof. Tryphon T. Georgiou. They were a visiting scholar with Prof. Li Qiu from 2023 to 2024 and are now co-supervised by Prof. Karl H. Johansson and Prof. Johan Karlsson at KTH. Education: BSc: Mechanical and Automation Engineering, Harbin Institute of Technology (2017) PhD: Mechanical and Aerospace Engineering, University of California, Irvine (2023) Research Interests: Anqi Dong's work focuses on control theory, optimal transport, and data-driven modeling of dynamical systems. Their research applies advanced mathematical frameworks to problems in multi-agent coordination, 3D scene understanding, and biomedical imaging. Key areas include tensor-based analysis, network learning, and computational methods for system identification. Recent Publications: Their 2025 studies explore dynamic optimal transport, neural network interpretability, and vessel segmentation, while 2024 contributions address temporal hypergraphs and toll station optimization. All work emphasizes interdisciplinary applications of mathematical and computational tools.

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Fredrik Kahl is a Professor at Chalmers University of Technology, leading the Computer Vision Group under the Department of Signal Processing and Medical Technology. His research spans Computer Vision , Machine Learning , and Medical Image Analysis , with a focus on geometric deep learning and 3D reconstruction. University: Chalmers University of Technology Department: Signal Processing and Medical Technology Email: fredrik.kahl@chalmers.se His work addresses rotation equivariance , out-of-distribution detection , and privacy-preserving representations . Recent publications explore Gaussian splatting for 3D edge mapping, semi-supervised learning frameworks, and symmetry encoding in ReLU networks. Projects include collaborations with institutions like Wallenberg AI, Autonomous Systems and Software Program and grants from VINNOVA and Vetenskapsrådet (VR) .

• Machine Learning
• Artificial Intelligence
• Ship Performance Modeling
• +۷ مورد دیگر

Wengang Mao serves as an Assistant Professor in Marine Engineering at Chalmers University of Technology, where his research focuses on enhancing the energy efficiency and safety of shipping operations through advanced computational methods and digitalization. His work bridges traditional marine engineering with cutting-edge artificial intelligence techniques to address contemporary challenges in maritime transportation. Dr. Mao's research interests center on four main areas: (1) Machine learning and AI applications for ship performance and response modeling; (2) Ship weather routing and voyage optimization for energy-efficient shipping; (3) Autonomous shipping technologies including route planning, optimal control, and smart navigation systems; and (4) Statistical modeling of hydrodynamic loads, fatigue safety, and environmental conditions. His interdisciplinary approach combines marine engineering fundamentals with data science to develop practical solutions for the maritime industry. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional marine engineering problems. His work demonstrates increasing focus on autonomous shipping systems, environmental sustainability through optimized routing, and advanced fatigue assessment methods for marine structures. The research spans both theoretical developments and practical applications, with particular emphasis on real-world validation using ship operational data. Dr. Mao has contributed significantly to the field through numerous collaborative projects, though specific grant information isn't detailed in the available text. His research has practical implications for ship operators seeking to reduce fuel consumption and emissions while maintaining safety standards in increasingly complex maritime environments. His work appears to be closely connected with maritime industry applications, particularly in the areas of voyage optimization systems and ship performance monitoring, suggesting strong industry-academia collaboration in his research activities.

Amin Kavehمشاهده پروفایل

پژوهشگر

• artificial intelligence
• data mining
• uncertain networks
• +۱ مورد دیگر

Amin Kaveh is a Postdoctoral Researcher at the Department of Information Technology, Division of Computer Systems , Uppsala University. His work focuses on probabilistic networks, data mining, and artificial intelligence applications in network analysis. Education: Doctoral Thesis (2022) and Licentiate Thesis (2019) in probabilistic network modeling. Research Areas: Uncertain networks, network mining, IoT security, and graph algorithms. Publication Trends : Recent work explores intrusion detection in IoT networks (2024), overlapping clustering (2022), and topological measures in probabilistic networks (2021). His research combines theoretical network analysis with practical applications in cybersecurity and machine learning.

• Theorem proving
• Decision procedures
• SAT/SMT solving
• +۸ مورد دیگر

Philipp Rümmer is a Professor of Theoretical Computer Science at the University of Regensburg and a Senior Lecturer/Associate Professor at the Department of Information Technology , Uppsala University. His career spans over two decades of academic and industrial collaboration in formal verification, SMT solvers, and program analysis. Professor (2022–present), Faculty of Informatics and Data Science, University of Regensburg Senior Lecturer/Associate Professor (2018–present), Department of Information Technology, Uppsala University His research focuses on formal verification of software and embedded systems, SMT solvers (e.g., Princess, Norn), model checking (Eldarica), and integrating machine learning with verification techniques. He has pioneered tools like JayHorn for Java verification and Sloth for string constraint solving. Recent publications emphasize probabilistic verification of parameterized systems, timing analysis in embedded updates, heap invariants via Horn clauses, and graph neural networks in solving word equations. These works reflect his commitment to advancing formal methods through interdisciplinary approaches. Awards include: Oscarspris (2013), Uppsala University SAP Award for top Computer Science graduate (2005) Bosch Telecom Award for natural science excellence (1998) He has secured significant grants from the Swedish Research Council , Knut and Alice Wallenberg Foundation , and Microsoft Research , supporting projects like UPDATE (2020–2024) and WebSec (2018–2023). His leadership in tools such as Princess and Eldarica underscores his influence in automated reasoning and verification.

• Software Engineering
• Software Quality Assurance
• Data-Intensive Methods
• +۳ مورد دیگر

Morgan Ericsson is a professor at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology. He has coordinated the Linnaeus University Centre for Data Intensive Sciences and Applications and leads the research group Data Intensive Software Technologies and Applications (DISTA). His work spans software engineering, quality assessment, and machine learning applications in system design. Research coordinator, DISA Faculty member, Linnaeus University His research focuses on defining and measuring software quality through data-driven approaches, including code metrics, documentation evaluation, and machine learning. He develops methods to balance performance, safety, and functionality in complex systems. Recent publications involve generative adversarial networks for log end identification, copula-based metrics aggregation, and graph convolution networks for code-architecture mapping. Key themes include automated classification, density analysis, and educational software tools. Ericsson has led projects like "HPC for SME" and "In-line Visual Inspection Using Unsupervised Learning," while contributing to software infrastructure for quality assessment and mobile learning frameworks.

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +۷ مورد دیگر

Göran Falkman is an Associate Professor of Computer Science at the University of Skövde, working within the Department of Information Technology at the School of Informatics. His research spans artificial intelligence, machine learning, big data analytics, and human-computer interaction, with applications in transportation, healthcare, and defense domains. His educational background includes a PhD in Computing Science and he holds the Swedish academic title of Docent in Computer Science. He has supervised numerous PhD students including Niclas Ståhl, Rakesh Rana, Ulrika Ohlander, and Maria Riveiro. Falkman's research interests focus on developing AI systems that effectively support human decision-making. His work includes driver intention recognition, uncertainty quantification in deep learning, maritime anomaly detection, and cockpit interface design for fighter pilots. He has developed approaches for interactive clustering, visual data analysis, and information fusion that bridge the gap between complex algorithms and human understanding. His recent publications demonstrate a strong trajectory in applying deep learning to transportation safety, particularly driver intention recognition, while maintaining his longstanding interest in human-centered AI systems. His work shows increasing sophistication in uncertainty modeling and probabilistic approaches to machine learning. Falkman has led major research projects including BISON (Big Data Fusion), BIDAF (Big Data Analytics Framework), and collaborations with Saab Aeronautics on human-machine interfaces for distributed decision-making in aviation contexts. He has supervised numerous PhD students across diverse applications of AI, from drug design to fighter pilot teamwork, demonstrating his ability to guide research at the intersection of theory and practical application. His work consistently addresses the challenge of making complex AI systems transparent and trustworthy for human users.