جستجوی استاد راهنمای دانشگاهی

• Intelligent Transportation Systems
• Traffic Flow Modeling
• Linear Parameter Varying Systems
• +۷ مورد دیگر

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.

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +۴ مورد دیگر

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

George Baravdishمشاهده پروفایل

دانشیار

• Inverse Problems
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

George Baravdish is a Senior Associate Professor and Head of Unit at the Department of Science and Technology (ITN), Linköping University. He leads the Physics, Electronics and Mathematics (FEM) unit and holds a PhD in Applied Mathematics from Linköping University (1995), focusing on Ill-posed and Inverse Problems under Prof. V. G. Maz'ya. His research spans Inverse Problems , Machine Learning , and Mathematical Oncology , with applications in medical imaging, signal processing, and telecommunications. Notable projects include Mathematics for Machine Learning (focusing on neural ODEs) and Mathematical Oncology (modeling brain tumor growth). Baravdish has supervised two doctoral students and co-supervised two more. He has secured funding as a main or co-applicant for 15 research projects. His work bridges theoretical mathematics and practical applications in healthcare and technology. Current research themes include: Image Processing : Generalizations of the p-Laplace operator for noise reduction Telecommunications : 3D object reconstruction using Wi-Fi signals Inverse Problems : Theoretical advancements and algorithm development He contributes to interdisciplinary collaborations, including AI-driven solutions for healthcare and signal analysis.

Chuan Heمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Science
• +۱۲ مورد دیگر

Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.

Tom Willhammarمشاهده پروفایل

دانشیار

• Electron Microscopy
• Electron Diffraction
• Structural Characterization
• +۶ مورد دیگر

Dr. Tom Willhammar is an Associate Professor at the Department of Materials and Environmental Chemistry, Stockholm University. His research focuses on electron microscopy and diffraction techniques for structural characterization of materials at atomic and nanoscales. Developed 3D electron diffraction and scanning electron diffraction methods Lead projects on cellulose nanostructure , zeolite nanotubes , and MOF-based water remediation His research group includes 5 PhD students and 1 postdoc. Current projects are funded by Swedish Research Council , Formas , and WISE . Recent publications (2020-2024) span Nature , Science , and Nature Water , highlighting zeolite synthesis , cellulose imaging , and pharmaceutical pollutant removal . Outstanding Paper Award 2014-2023, Inorganic Chemistry Frontiers Appointed Docent in Materials Chemistry , 2022 Teaching includes Bachelor's , Master's , and doctoral courses in electron microscopy and crystallography. Collaborations with institutions in Belgium , Singapore , China , and Spain drive innovations in materials science and sustainable chemistry .

Arunselvan Ramaswamyمشاهده پروفایل

مدرس ارشد

• Reinforcement Learning
• Multi-agent Learning
• Explainable Learning
• +۵ مورد دیگر

Arunselvan Ramaswamy is a Senior Lecturer in Data Science, AI, and Machine Learning Research at Karlstad University. His research focuses on the intersection of Computer Science and Mathematics, with primary interests in reinforcement learning, multi-agent systems, stochastic optimization, and control theory. He has held prior roles as Lecturer at Paderborn University and Junior Research Head at the Heinz Nixdorf Institute, Germany. His work spans theoretical foundations of learning algorithms and practical applications in cyber-physical systems, wireless sensor networks, and Industry 4.0. Key contributions include distributed optimization frameworks, reinforcement learning for autonomous systems, and stability analysis of stochastic approximation methods. He has published widely in top venues such as IEEE Transactions on Automatic Control, NeurIPS, and IFAC conferences. Ramaswamy has contributed to interdisciplinary projects including automated side-channel detection in cryptographic protocols and deep reinforcement learning for microgrid control. His teaching includes courses on machine learning fundamentals and project-based ML applications at Karlstad University. Research highlights include developing the DeepCAS algorithm for control-aware scheduling, analyzing convergence properties of distributed optimization under communication constraints, and exploring gradient-based methods with unbounded delays. Current work emphasizes scalable multi-agent learning and explainable AI systems.

• Biomaterials
• Cell-Material Interactions
• Tissue Engineering
• +۳ مورد دیگر

Julie Gold is an Assistant Professor at the Department of Nano- and Biophysics, Chalmers University of Technology. Her research focuses on biomaterials for regenerative medicine, emphasizing cell-surface interactions and material design for applications like cultured meat and tissue engineering. She led the Chalmers Employment Committee (2018-2024) and has contributed to interdisciplinary projects such as the VINNOVA-funded 'Smart mat' project. Her work bridges engineering and biology, with notable contributions to undergraduate research integration and bioethics in food production. Research interests include biomaterial surface engineering, cell fate control via material properties, and sustainable food technologies. Projects include developing vegetable-based scaffolds for cultured meat and studying neural progenitor cell migration using biomaterials. She collaborates with institutions like Mistra for nanotechnology safety and environmental impact assessments. Key areas of exploration are: biomaterial design principles, ethical implications of lab-grown meat, and pedagogical innovation in STEM education. Her 39+ publications span peer-reviewed journals, conference proceedings, and edited volumes, reflecting her dual focus on scientific discovery and educational practice.

Zheng Zhaoمشاهده پروفایل

استادیار

• Computational Statistics
• Machine Learning
• Generative Diffusion Models
• +۴ مورد دیگر

Zheng Zhao is an Assistant Professor in computational statistics and machine learning at Linköping University, leading a research team within the Division of Statistics and Machine Learning (STIMA) under the Department of Computer and Information Science (IDA). His work focuses on developing computationally and statistically efficient models for machine learning applications, particularly generative diffusion models and their use in signal/image processing. He also applies these methods to fields like astronomy (e.g., stellar magnetic field analysis) and climate science. His research team emphasizes practical applications of diffusion models and Bayesian methods, contributing to advancements in image restoration, sensor array analysis, and high-dimensional data modeling. Zhao collaborates with STIMA colleagues on interdisciplinary projects, leveraging statistical theory and computational techniques. Publications highlight his exploration of diffusion models, Bayesian neural networks, and probabilistic methods across domains like astrophysics and environmental science. His work bridges theory and application, addressing challenges in uncertainty quantification and overfitting mitigation. Zhao is actively involved in academic initiatives such as the international master's program in Statistics and Machine Learning (hosted by STIMA). His research has been applied in competitions like the NTIRE challenge, demonstrating real-world impact in image restoration.

• Secure Control Systems
• Scalable Control
• Positive Systems
• +۳ مورد دیگر

Sribalaji Coimbatore Anand is a Researcher at the Department of Intelligent Systems, KTH Royal Institute of Technology, Sweden. Hosted by Professors Karl Henrik Johansson and Henrik Sandberg, his work focuses on secure control systems, scalable control, and applications of convex optimization. He holds an M.Sc. in System and Control from Delft University of Technology (2019) and a Ph.D. in Automatic Control from Uppsala University (2024). His research emphasizes cyber-physical system security, including stealthy attacks, resilient control algorithms, and privacy-preserving mechanisms. Notable grants include the VR International Postdoc grant (2024) for analyzing secure large-scale networked control systems and the STINT grant (2024) exploring total positivity frameworks in secure network control. Education: M.Sc., System and Control, Delft University of Technology (2019) Ph.D., Automatic Control, Uppsala University (2024) He has received awards such as the IEEE Travel Scholarship (2022) and Dean’s List scholarships (2013–2016). His work spans theoretical frameworks for detector placement, optimal security allocation, and game-theoretic approaches to sensor design in cyber-physical systems. Current projects involve collaborations with Prof. George Pappas (University of Pennsylvania) and Christian Grussler (Technion, Israel), focusing on scalable security metrics and resilient control architectures.

Johan Alenlövمشاهده پروفایل

مدرس

• Machine Learning
• Statistical Inference
• Particle Filtering
• +۵ مورد دیگر

Johan Alenlöv is a University Lecturer in the Statistics and Machine Learning (STIMA) group at the Department of Computer Science, Linköping University. His work is situated at the intersection of computational statistics and machine learning, with a focus on sequential Monte Carlo methods and state-space modeling. His research interests include Machine Learning , Statistical Inference , Particle Filtering , Sequential Monte Carlo , Bayesian Computation , and Data Science . These areas are central to modern data analysis and AI-driven modeling in dynamic systems. The recent publications highlight a consistent focus on online smoothing, marginal inference, and Hamiltonian Monte Carlo methods. Collectively, the articles reflect a strong trend in developing scalable, numerically stable algorithms for inference in complex, nonlinear state-space models—critical for applications in signal processing, robotics, and time-series analysis. Johan Alenlöv has not been mentioned as receiving any scientific awards in the provided text. There is no information available about students advised by Johan Alenlöv or any grants he may have received. However, his collaborative work with researchers like Jimmy Olsson and Fredrik Lindsten suggests active participation in the statistical learning research community. He is affiliated with the Statistics and Machine Learning (STIMA) research group at Linköping University, which conducts research in modern data analysis and hosts both a Bachelor's and an international Master's program in Statistics and Machine Learning.

Gabriel Eilertsenمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Associate Professor Gabriel Eilertsen is affiliated with Linköping University's Department of Science and Technology (ITN) and the Media and Information Technology (MIT) research group. He holds a PhD in computer graphics and image processing, with expertise in high dynamic range (HDR) imaging and machine learning. As a core member of the Analytic Imaging Diagnostics Arena (AIDA) and affiliated researcher at CMIV (Center for Medical Image Science and Visualization) , he bridges academia and healthcare innovation. Research focuses on machine learning applications in computer vision , medical imaging diagnostics , and data-centric deep learning . Current projects include synthetic image generation for neural network training, evaluation of HDR reconstruction methods, and enhancing fairness in GANs. He leads a CENIIT-funded project on generative deep learning for medical imaging. Key contributions span synthetic data synthesis techniques, HDR imaging protocols, and AI-driven healthcare solutions. Notable work includes leveraging deepfake-like technology for generating AI-based medical images from the SCAPIS study. His interdisciplinary approach addresses challenges in domain adaptation, uncertainty quantification, and perceptual evaluation metrics for imaging systems. Affiliations include the Wallenberg AI, Autonomous Systems and Software Program (WASP) as an AI/MLX Assistant Professor, reflecting his leadership in national AI initiatives. His research emphasizes practical applications of AI in healthcare diagnostics and fundamental advancements in image generation and processing algorithms.

Henrik Kaatariمشاهده پروفایل

مدرس ارشد

• Corpus Linguistics
• Language Learning
• Grammatical Variation
• +۳ مورد دیگر

Henrik Kaatari is a Senior Lecturer in the Department of English at the University of Gävle . His research focuses on corpus linguistics , language learning , and formality in academic writing , with particular interests in grammatical variation , syntactic complexity , and the impact of extramural English (EE) activities on L2 writing development. Current Project : Principal Investigator of the ComplEExity Project (RJ P23-0437), funded by the Bank of Sweden Tercentenary Foundation, investigating EE's impact on Swedish secondary students' L2 writing. Research Areas : Lexical complexity, register variation, phraseology, and self-initiated language learning. His recent publications analyze the relationship between extramural exposure (e.g., gaming, YouTube) and improvements in lexical diversity and grammatical complexity. Using the Swedish Learner English Corpus , Kaatari employs cross-sectional designs and complexity measures to assess writing development across lexis, phraseology, and grammar. His work has practical implications for English pedagogy in Swedish schools.

• Marine Remote Sensing
• Bio-optics
• Baltic Sea Ecology
• +۴ مورد دیگر

Susanne Kratzer is an Associate Professor in Marine Ecology at Stockholm University's Department of Ecology, Environment and Botany, specializing in marine remote sensing and bio-optics with focus on Baltic Sea ecosystems. She leads the 'Group Scratches' research group and coordinates the Nordic Network for Baltic Remote Sensing. Her work integrates satellite data, field measurements, and modeling for coastal zone management. Her educational background includes a German Diploma, Master's thesis in the School of Ocean Sciences (University of Wales, Bangor), and PhD in Biological Oceanography from the School of Ocean Sciences. She has been conducting marine remote sensing research at Stockholm University since 1997. Her research focuses on developing novel methods for ocean color remote sensing in optically complex waters, with particular emphasis on Baltic Sea applications. She has pioneered algorithms for retrieving suspended matter, Secchi depth, and chlorophyll-a from satellite data, addressing challenges posed by high CDOM concentrations. Her work bridges optical oceanography with practical coastal management needs, especially eutrophication assessment. Analysis of her recent publications reveals strong trends in satellite validation (particularly Sentinel-3 OLCI), integration of multi-source data (satellite, mooring, ship-based), and development of region-specific algorithms for Baltic Sea conditions. Her research increasingly addresses climate change impacts on coastal optical properties and monitoring requirements. National advisor for bio-optics in Swedish monitoring programs (SMHI, GU, UU, SU) Professional trainer for bio-optical measurements funded by Swedish Marine and Water Management Agency Contributor to 'The Colour of the Baltic Sea' film segment She actively mentors students through teaching marine remote sensing and bio-optics at all academic levels, and has coordinated numerous PhD courses through the Nordic Network. Her current projects include developing advanced methods for imaging biomolecules, improving national guidelines for marine bio-optical measurements, and long-term operation of the AERONET-OC station in Lake Vänern. Her research group maintains strong international collaborations with NASA, ESA, and European monitoring networks, contributing significantly to the integration of remote sensing into operational coastal zone management frameworks.

• Vehicle Dynamics
• Powertrain Systems
• Active Safety
• +۴ مورد دیگر

Bengt Jacobson is a Professor and group leader of the Vehicle Dynamics group at Chalmers University of Technology (Department of Vehicle Engineering and Autonomous Systems). He earned his PhD in Machine Elements from Chalmers in 1993 and was appointed Associate Professor in 1998. His industrial experience includes serving as Technical Expert at Volvo Car Corporation (2001-2010) focusing on active safety and vehicle dynamics. Jacobson holds board positions at the Swedish Vehicular Engineering Association (since 2012) and Heavy Vehicle Transport and Technology (since 2023). His research focuses on vehicle dynamics in the ground plane for passenger cars and heavy trucks, including powertrain systems, hybrid vehicles, active safety controls, tire modeling, and aerodynamic interactions. Recent work explores trajectory control, stability algorithms, energy efficiency, and human-vehicle interactions under dynamic conditions. Jacobson's 200+ publications consistently address vehicle dynamics optimization, with recent trends in MPC control validation, safety envelope definition for articulated vehicles, crosswind stability, and high-fidelity tire modeling. His group develops simulation tools like OpenPBS for assessing high-capacity transports. He leads 44+ projects involving industry partnerships (e.g., Volvo) and supervises research on electric propulsion systems, automated driving, and performance-based standards. The Vehicle Dynamics group collaborates extensively with automotive OEMs and infrastructure authorities.

Olga Mulaمشاهده پروفایل

دانشیار

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

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