جستجوی استاد راهنمای دانشگاهی

Christopher Zach is a Research Professor at Chalmers University of Technology, affiliated with the Signal Processing and Medical Technology department within the Digital Image Systems and Image Analysis research group . His work focuses on 3D reconstruction , real-time computer vision , and numerical optimization for machine learning. Develops 3D image understanding techniques Specializes in robust optimization for vision systems Leads research in medical image analysis Recent publications demonstrate expertise in low-light text enhancement , out-of-distribution detection , and domain adaptation for industrial applications. Active in Chalmers' Wallenberg AI and ÅForsk funded projects. Collaborates with researchers from Volvo Group , Volvo Cars , and SAFER Vehicle Safety initiatives.

• Computer Vision
• 3D Reconstruction
• Medical Image Analysis
• +۳ 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.

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

Louise Rixon Fuchs is an Industry doctoral student at KTH Royal Institute of Technology's Division of Robotics, Perception and Learning. She specializes in advanced sonar imaging techniques, machine learning applications, and signal processing for robotics. Her research focuses on enhancing sonar data analysis through deep learning methodologies, optimization algorithms, and compressive sensing models. Her work bridges robotics and computer vision, addressing challenges in low-resolution sonar image processing, object detection, and beamforming optimization. Key contributions include GAN-based sonar image simulations and transfer learning for object recognition in sonar environments. Louise has published extensively on sonar technology advancements, with recent articles emphasizing dense point correspondence algorithms and wide-beam sonar system improvements. She currently holds no recorded scientific awards but is actively contributing to robotics and marine sensing research.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Anders Enocson is an Adjunct Professor of Orthopedic Traumatology at the Karolinska Institutet , leading the Orthopedics Research Group at the Department of Molecular Medicine and Surgery. He combines academic research with clinical practice as a senior physician at Karolinska University Hospital. Specialist in orthopedic surgery since 2007 First professor in orthopedic traumatology in Sweden (2020) Extensive clinical experience across Sweden and UK Research focus: Trauma epidemiology, fracture outcomes, and AI applications His research spans: Trauma Registry Karolinska - Data-driven quality improvement SweTrau - National trauma registry machine learning models Pelvic Fractures - Complication reduction strategies Tendon Repair - Mechanobiology and neuropeptide regulation Medical Imaging - CT motion analysis for fractures Orthopedic Oncology - Sarcoma treatment outcomes Key publication trends show emphasis on Machine learning for mortality prediction 3D CT analysis in fracture follow-up Long-term joint replacement outcomes Postoperative complication risk factors Tendon healing biomarkers Clinical collaborations include AO Foundation courses in Sweden Karolinska Traumacentrum leadership International orthopedic networks Multidisciplinary teams in trauma care

• Orthopedic Traumatology
• Clinical Epidemiology
• Trauma Surgery
• +۷ مورد دیگر

Martin Fagerström is a Professor at the Department of Material and Computational Mechanics, Chalmers University of Technology. His research focuses on computational modeling of damage and fracture in lightweight materials, particularly fiber-reinforced polymers, with applications spanning crashworthiness, sports engineering, and health technology. He serves as Co-director of the Health Engineering Area of Advance and coordinator of Chalmers Sports & Technology. Affiliation: Chalmers University of Technology Department: Material and Computational Mechanics Research Areas: Computational fracture mechanics, damage modeling, composite materials, finite element analysis, sports and health engineering His recent publications emphasize multi-fidelity data fusion, machine learning integration with transfer learning, and advanced finite element techniques for predicting elasto-plasticity and delamination in woven composites. Projects led or co-led include SCALE (circular aluminum alloys), REaL-tIme (anisotropic carbon composites), and LIGHTer Academy (lightweight vehicle technology).

• Computational Fracture Mechanics
• Solid Mechanics
• Damage and Fracture Modeling
• +۳ مورد دیگر

Anatoly Belonoshko is Professor in Theoretical Physics specializing in condensed matter theory at KTH Royal Institute of Technology. His research investigates material behavior under extreme pressures and temperatures, particularly in planetary cores, using advanced supercomputer simulations. Research focuses on: Earth's core composition and dynamics Phase transitions at extreme conditions High-pressure melting behavior Exoplanetary interiors Recent work has resolved longstanding questions about Earth's solid iron core despite extreme heat, while developing new computational methods for materials simulation. Publications utilize cutting-edge facilities like X-ray free-electron lasers to validate theoretical models. Research contributes to climate science through extreme-condition studies and enables new materials development by predicting stability under extraordinary parameters.

• Condensed Matter Physics
• High-Pressure Physics
• Earth's Core Dynamics
• +۲ مورد دیگر

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.

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +۷ مورد دیگر

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.

• Fluid Mechanics
• Shock Wave Dynamics
• High Energy Density Physics
• +۳ مورد دیگر

Malin Wohlert is an Associate Professor at Uppsala University's Department of Materials Science and Engineering; Applied Mechanics. Her research focuses on molecular modeling of biomaterials, particularly wood's structural and hygroscopic properties. She investigates lignin behavior, hygroexpansion mechanisms, and wood mechanics using molecular dynamics simulations and experimental methods. Wohlert is a member of the Biophysics Network and contributes to pedagogical initiatives, including teaching Solid Mechanics and supervising the Supplemental Instruction program at the Construction Engineering Bachelor's program. She also supports educational development through the faculty's Council for Educational Development. Research Focus: Wohlert's work bridges molecular-level phenomena with macroscopic material behavior, emphasizing lignin hydration, wood anisotropy, and hygroelastic properties. Her studies employ advanced techniques like microscopic computed tomography and finite element modeling to analyze wood's structural responses under environmental stresses. Recent projects explore compression/ opposite wood in conifer branches and lignin's role in material degradation processes. Teaching & Pedagogy: She teaches Solid Mechanics courses and mentors students in practical engineering education. Her contributions to educational development include curriculum design and pedagogical strategy implementation. Labs & Collaborations: Active within the Biophysics Network, she collaborates on interdisciplinary projects involving material characterization and biomaterials innovation. Her research group integrates computational modeling with experimental validation to advance understanding of natural materials' complex behaviors.

• Molecular Modeling
• Biomaterials Science
• Materials Science
• +۳ مورد دیگر

Sören Östlund is a Professor in the Department of Material and Structural Mechanics at KTH Royal Institute of Technology. His research focuses on paper mechanics, fracture mechanics of paper and board, deformation and damage mechanisms in wood fiber-based materials, and forming of complex paper structures. He supervises graduate students, including Petri Mäkelä (PhD candidate in Solid Mechanics) and Adam Attemalm (Master’s student in Mechanical Engineering). His educational contributions include teaching courses such as Solid Mechanics, Fibre Technology, and Mechanics of Fiber Networks. He also serves as an examiner and course responsible in multiple engineering programs. His work integrates experimental and computational methods to advance understanding of material behavior under various conditions, particularly in packaging and structural applications. Key research trends in his publications emphasize numerical modeling of material behavior, fracture mechanics in composites, and the influence of environmental factors like moisture on material properties. His studies bridge fundamental mechanics with industrial applications, particularly in sustainable materials and packaging technologies.

• Mechanical Engineering
• Materials Science
• Fracture Mechanics
• +۴ مورد دیگر

Neda Maleki is a Senior Lecturer at the Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, starting in September 2024. Her research focuses on Applied IoT, Edge-Cloud Computing, Distributed Systems (Hadoop/Spark), Artificial Intelligence, and Machine Learning for data analysis. PhD in Computer Engineering (2014–2021), Science and Research Branch of Islamic Azad University, Tehran, Iran Master of Science in Computer Engineering (2009–2013), Ghazvin Islamic Azad University Bachelor of Science in Hardware Engineering (2004–2008), Ghazvin Islamic Azad University Her research explores IoT applications in energy forecasting, environmental conservation, and SME digital transformation. Key contributions include frameworks for power-aware Hadoop acceleration, energy-efficient IoT data formats, and predictive models for fuel consumption and city load forecasting. Recent publications highlight collaborations with industry partners in Sweden and international conferences across Qatar, Italy, Denmark, and the Netherlands. She teaches courses in Data Structures, Algorithms, IoT, and programming at the bachelor's and master's levels. 1DV018: Data Structures and Algorithms 1DV501: Introduction to Programming 4DV119: Applied IoT Competence (Master level) Final Thesis supervision

• Applied IoT
• Edge-Cloud Computing
• Distributed Systems
• +۴ مورد دیگر

Erdal Aksoy is an Associate Professor at Halmstad University 's School of Information Technology , with visiting scholar roles at Volvo Group Trucks Technology and Zenseact AB . His work focuses on semantic scene perception , action semantics , and environment understanding for autonomous systems including robots and unmanned vehicles. Education : PhD from University of Göttingen, postdoctoral research at Karlsruhe Institute of Technology (KIT) and University of Göttingen Research Interests : Semantic scene perception, action semantics, autonomous systems, human-robot interaction, LiDAR processing, and deep learning applications His recent publications demonstrate expertise in LiDAR data processing (SalsaNet, SalsaNext), semantic state estimation for robotic cloth manipulation, and multimodal failure detection systems. He has coordinated the HORIZON Europe ROADVIEW consortium with 15 partners. Scientific recognition includes Best Student Paper Award at IJCAI 2021 AI4AD workshop Best AI Master’s Thesis Award from Swedish AI Society (SAIS) Wimanska Prize for best bachelor thesis Distinguished Service Award as IEEE Robotics and Automation Letters Associate Editor His advising track record shows mentorship of award-winning students across bachelor, master, and PhD levels. Erdal Aksoy maintains active collaborations between academia and industry, particularly with automotive technology companies.

• Semantic Scene Perception
• Action Semantics
• Autonomous Systems
• +۹ مورد دیگر

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +۳ مورد دیگر

Ali Hassan is a Ph.D. student and Early Stage Researcher at Mid Sweden University , affiliated with the Department of Computer and Electrical Engineering (DET) . He is based at the Realistic3D lab under the Information Systems and Technology Department, working under supervisors Prof. Tingting Zhang and Prof. Mårten Sjöström. Education B.Sc. and M.Sc. in Computer Science from COMSATS University, Islamabad Research Interests span Machine Learning , Light Field , Digital Image Processing , Computer Vision , and Deep Learning , with a focus on mobile computational plenoptic and dimension reduction of deep networks . Publications highlight his work on neural architecture search for disparity estimation and light field compression. He contributes to projects like IMMERSE and PLENOPTIMA , exploring augmented reality and immersive imaging.

• Machine Learning
• Light Field
• Digital Image Processing
• +۳ مورد دیگر

Najeem Lawal is a Senior Lecturer at the Department of Computer and Electrical Engineering (DET) at Mid Sweden University. His research focuses on FPGA-based systems, real-time video processing, and wireless vision sensor networks. He is affiliated with the STC Research Centre and has contributed extensively to hardware architecture optimization for smart cameras and volumetric surveillance systems. Doctoral Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2009) Licentiate Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2006) Lawal's research explores cost-effective multi-camera dome designs, real-time component labeling, and energy-efficient wireless vision sensor nodes. His work spans architecture exploration, hardware-software partitioning, and memory optimization for FPGA platforms. He has published in journals like IEEE Sensors Journal and conferences including the International Conference on Distributed Smart Cameras. Recent publications highlight advances in volumetric surveillance coverage, calibration of multi-camera systems, and low-power embedded vision architectures. Lawal's projects emphasize practical deployment of smart camera networks for outdoor monitoring applications. He has collaborated with researchers such as Muhammad Imran, Benny Thörnberg, and Mats O'Nils. His expertise includes designing hardware architectures for vision systems, optimizing processing pipelines, and modeling sensor network configurations.

• FPGA-based Systems
• Real-Time Video Processing
• Wireless Vision Sensor Networks
• +۵ مورد دیگر