جستجوی استاد راهنمای دانشگاهی

Anders Eklundمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Machine Learning
• Medical Informatics
• +۳ مورد دیگر

Anders Eklund is a Senior Associate Professor at Linköping University, affiliated with both the Department of Biomedical Engineering (IMT) and the Department of Computer and Information Science (IDA). He holds roles in the Center for Medical Image Science and Visualization (CMIV), focusing on developing advanced methods for medical image analysis, including applications in pediatric brain tumor classification, synthetic image generation, and federated learning frameworks. His research bridges biomedical engineering, artificial intelligence, and data science to address clinical challenges such as improving diagnostic accuracy and reducing healthcare resource bottlenecks. Education: Master of Science (2007), PhD in Medical Informatics (2012), Postdoc at Virginia Tech (2012–2014), Docent (2016), Associate Professor (2021). Research Interests: Medical image processing, deep learning for healthcare, federated learning for sensitive data, synthetic medical image generation, and statistical validation of neuroimaging techniques. Notable contributions include exposing flaws in fMRI analysis software and advancing AI-driven solutions for radiation treatment planning. Publications: Recent work emphasizes deep learning applications in medical imaging, including tumor classification, federated learning for privacy-preserving models, and synthetic image techniques to address data scarcity. His articles reflect a focus on interdisciplinary solutions for healthcare challenges, such as reducing queues post-pandemic through automated brain image analysis. Grants & Advising: Secured a 3.6M SEK grant from the Swedish Research Council (2016). Supervised numerous PhD students, including main supervision of Xuan Gu (2019) and co-supervision of Per Sidén (2020). Active in training the next generation of biomedical engineers and AI researchers. Labs & Collaborations: Leads projects within CMIV and collaborates across departments at Linköping University. Involved in initiatives like the ASSIST project, which aims to automate brain image analysis to free clinician time.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۲ مورد دیگر

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Erdal Aksoyمشاهده پروفایل

دانشیار

• Semantic Scene Perception
• Action Semantics
• Autonomous Systems
• +۹ 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.

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

• Condensed Matter Physics
• Electronic Structure Theory
• Many-Body Quantum Theory
• +۶ مورد دیگر

Krister Karlsson is a Professor of Physics at the University of Skövde, affiliated with the School of Engineering Science. His research focuses on theoretical condensed matter physics, particularly in the areas of electronic structure calculations, many-body quantum theory, and density functional theory. With a career spanning several decades, Professor Karlsson has made significant contributions to our understanding of correlated electron systems, magnetic materials, and semiconductor physics. Professor Karlsson's research interests center on the theoretical foundations of condensed matter physics. His work explores the electronic structure of materials using advanced computational methods including GW approximation, dynamical mean field theory, and first-principles calculations. He has made particular contributions to understanding orbital magnetism, electron correlations in cuprates and dilute magnetic semiconductors, and the development of theoretical frameworks for describing complex quantum materials. His research bridges fundamental quantum theory with practical applications in materials science and spintronics. Analysis of Professor Karlsson's publication record reveals a consistent focus on developing and applying advanced theoretical methods to understand complex quantum phenomena in solids. His work spans from fundamental investigations of many-body effects in simple systems to applications in technologically relevant materials like dilute magnetic semiconductors. A notable trend is his long-standing interest in improving the accuracy of electronic structure calculations for strongly correlated systems, with publications spanning from early work on plasmon satellites in the 1990s to recent developments in time-dependent density functional theory. Professor Karlsson has supervised numerous research projects and students throughout his career, contributing to the development of the next generation of theoretical physicists. His collaborative research extends across international boundaries, with frequent co-authorship with researchers from various institutions. Professor Karlsson maintains an active research laboratory focused on computational condensed matter physics. His team utilizes advanced computational methods to investigate electronic structure problems, with particular emphasis on developing more accurate theoretical frameworks for describing correlated electron systems. The research group maintains strong connections with experimental groups to validate theoretical predictions and guide new experimental investigations.

Maria Magnussonمشاهده پروفایل

مدرس

• Computer Vision
• Image Processing
• Scene Flow Estimation
• +۳ مورد دیگر

Maria Magnusson is a Lecturer at the Computer Vision Group within the Department of Systems Engineering at Linköping University . Her work bridges artificial vision systems with practical applications in medical imaging and computational photography . Research Focus : Computer Vision, Scene Flow Estimation, CT Reconstruction, and Machine Learning. Teaching : Courses in Digital Image Processing, Signal and Image Processing, Medical Images, and Computational Photography.

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.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

Alexander Miethingمشاهده پروفایل

پژوهشگر

• Public Health
• Social Determinants of Health
• Health Inequalities
• +۳ مورد دیگر

Alexander Miething is a Researcher at the Department of Public Health Sciences , Stockholm University. His work focuses on social determinants of health , particularly how socioeconomic inequalities affect immigrant populations' health and mortality. He leads the FORTE-funded project on Income Mortality Paradox among Immigrants and collaborates on the Studies of Migration and Social Determinants of Health (SMASH) initiative. Education includes a PhD in Health Equity Studies (2014) from Stockholm University. His research integrates epidemiological methods with social science theories to examine health disparities across generations, migration contexts, and welfare systems. Key projects include investigating reproduction of inequality through linked lives (RELINK) and analyzing peer network effects on health behaviors like alcohol use and dieting. Methodologically, he uses register-based studies and longitudinal data to track health outcomes over time. Publications span topics such as income gradients in mortality , unemployment policy impacts , and psychosocial factors like trust and happiness . He has explored cross-national health trends in Europe and age-related disparities in depressive symptoms. Funding: Swedish Research Council for Health, Working Life, and Welfare (Forte), EU initiatives Teams: SMASH group, RELINK consortium

Gaurav Raiمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Gaurav Rai is a Ph.D. researcher at Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), focusing on computer graphics, computer vision, and GPU computing. He has contributed to real-time animation frameworks and diffusion models for motion transfer. Ph.D. in Computer Science and Engineering (2020–present), IIIT Delhi B.Tech in Computer Science and Engineering (2016–2020), GKV Haridwar His research emphasizes creative algorithms for sketch-based animation and dynamic deformation. Recent work involves diffusion models with temporal consistency and motion-guided frameworks. Gaurav has published in top-tier conferences like SCA, GRAPP, and ICME, with a focus on computer graphics, deep learning, and multimedia. Earlier publications include applications in medical imaging and spam filtering. ACM Student Travel Grant for ICME 2025 CHANAKYA Doctoral Fellowship He has served as a teaching assistant for courses in Computer Graphics, Scientific Computing, and Linear Algebra at IIIT Delhi.

• Computational Mathematics
• Finite Element Methods
• Scientific Computing
• +۳ مورد دیگر

Anders Logg is a Professor in Applied Mathematics and Statistics at the University of Gothenburg, leading the Digital Twin Cities Centre. His work focuses on computational mathematics, finite element methods, and scientific software development. He is a core contributor to the FEniCS Project, an open-source computing platform for solving partial differential equations. Research interests include numerical analysis, multiphysics simulations, and applications in engineering, biology, and physics. Notable contributions include multimesh finite element methods, CutFEM techniques, and real-time simulations in augmented reality. Logg's work bridges theoretical mathematics with practical software implementation, emphasizing reproducibility and accessibility. Publications highlight advancements in mesh adaptation, error analysis, and fluid-structure interaction modeling. His collaborative projects span academia and industry, addressing challenges in computational geometry, urban modeling, and gravitational physics. Current efforts involve developing digital twin technologies for smart cities through the Digital Twin Cities Centre.

Martin Sahlénمشاهده پروفایل

دانشیار

• astrophysics
• astronomy
• cosmology
• +۹ مورد دیگر

Martin Sahlén is an Associate Professor in the Department of Physics and Astronomy at Uppsala University, specializing in astronomy with a focus on astrophysics. His research spans theoretical and observational studies of the large-scale structure of the universe through galaxy clusters, voids, and the universe's first galaxies to understand galaxy physics, star formation, dark energy, dark matter, cosmic inflation, and the reionization process in the early universe. Dr. Sahlén's research interests center on cosmology, particularly the intersection of theoretical models and observational data. He employs Bayesian inference techniques to analyze cosmological datasets, with special attention to the philosophy of cosmology, including model inference, fine-tuning of natural constants, and multiverse explanations. His work bridges observational astronomy with theoretical physics to address fundamental questions about the universe's structure and evolution. His recent publications (2024-2025) predominantly focus on the Euclid space mission, representing a major portion of his scholarly output. These works span instrumentation, data analysis techniques, cosmological simulations, and theoretical interpretations. Key themes include constraints on modified gravity models, galaxy cluster analyses, weak lensing studies, and machine learning applications for extracting physical parameters from astronomical data. His research demonstrates a consistent focus on leveraging next-generation observational data to test fundamental cosmological theories. Dr. Sahlén serves as Principal Investigator for multiple significant research grants, including: (1) 'The Galaxy – Dark Matter Connection with HST, JWST and EUCLID' (2021-2026, 8 MSEK); (2) 'Searching for New Physics in the Early Universe' (2022-2024, 1 MSEK); and (3) 'Astronomical object classification and anomaly detection with complex spectroscopic 4MOST data sets' (2023-2027, 4 MSEK). He actively participates in major scientific collaborations including 4MOST, BUFFALO, Euclid and SKA Observatory, and contributes externally to the Dark Energy Survey. As an educator, Dr. Sahlén is responsible for and teaches the interdisciplinary course 'Three Approaches to Cosmology' (10 credits), which likely reflects his broad expertise spanning observational, theoretical, and philosophical aspects of cosmology.

Rodrigo Morenoمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Rodrigo Moreno is an Associate Professor at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) at KTH Royal Institute of Technology, and a Co-Principal Investigator (Co-PI) in two major research projects: 'Characterization of the mechanical tissue properties of the brain in the developing brain with magnetic resonance elastography' and 'Advanced Magnetic Resonance Elastography for the Brain.' He is also affiliated with Digital Futures, a cross-disciplinary research center focused on developing digital technologies to address societal challenges. His primary research interests include biomedical imaging techniques, biomechanical properties of tissues, and the application of artificial intelligence (AI) in healthcare. Dr. Moreno’s work spans neurodegenerative diseases like Alzheimer’s and Parkinson’s, as well as advancements in medical imaging modalities such as MRI, CT, and ultrasound. His recent projects emphasize precision medicine using AI-powered longitudinal MRI analysis and the mechanical characterization of brain tissue in developmental and disease contexts. He leads collaborations in computational methods like tensor voting and deep learning for medical image processing, segmentation, and generative modeling of biological structures. His contributions include developing algorithms for tractography filtering, synthetic data generation, and robust segmentation of medical images. Dr. Moreno’s research has produced over 50 peer-reviewed articles since 2014, with a focus on advancing imaging technologies and their clinical applications. His work bridges computational engineering, biomedical research, and digital innovation, aiming to improve diagnostics and personalized treatment strategies for complex diseases.

Christopher Petersمشاهده پروفایل

استاد

• Socially Interactive Agents
• Embodied AI
• Human-Robot Interaction
• +۷ مورد دیگر

Christopher Peters is a Professor and Director of the Embodied Social Agents Lab (ESAL) at KTH Royal Institute of Technology. He holds a PhD from Trinity College Dublin (2004) and is affiliated with the Department of Computational Science and Technology (CST) within the School of Electrical Engineering and Computer Science (EECS). His work focuses on Socially Interactive Agents and Embodied AI, exploring computational models of visual attention, affect, memory, and theory of mind. Peters leads projects on human-agent interaction, urban spatial contexts, and multimodal systems involving humanoid agents and robots. Education: PhD in Computer Science, Trinity College Dublin, 2004. Research Interests: Peters investigates embodied AI through socially interactive agents, emphasizing real-time control systems for gaze, emotion, and group dynamics. His studies span virtual characters, physical robots, and VR applications. Key areas include politeness behaviors in human-robot interactions, crowd navigation algorithms, and urban spatial analysis. Recent work includes the CreativeBot project, leveraging LLMs to enhance child creativity through storytelling robots. Projects & Grants: Peters is Principal Investigator for EU Horizon 2020 projects CLIPE (€4M), ANIMATAS (€3.9M), and ProsocialLearn. He leads a Swedish Research Council grant (4 MSEK) on VR applications for human-robot collaboration. His projects address societal challenges in urban planning, digital transportation, and educational robotics. Labs & Teams: Directs ESAL, a lab developing embodied agents for social interaction. Collaborates with industry and academic partners in Europe and globally through projects like METROPOLIS and HUMAINE.

• Information Theory
• Wireless Networks
• Machine Learning
• +۱۲ مورد دیگر

H. Vincent Poor is the Michael Henry Strater University Professor in the Department of Electrical Engineering at Princeton University’s School of Engineering and Applied Science. He holds a Ph.D. in Electrical Engineering and Computer Science from Princeton (1977), and has maintained a long-standing and influential career in academia and research. His work spans multiple domains including wireless networks, information theory, machine learning, and smart grid systems. Ph.D., Electrical Engineering & Computer Science, Princeton University, 1977 M.A., Electrical Engineering, Princeton University, 1976 M.S., Electrical Engineering, Auburn University, 1974 B.E.E., with Highest Honor, Auburn University, 1972 His research interests are centered on the theoretical and practical challenges in modern communication and energy systems. He investigates information-theoretic foundations of wireless networks, including spectrum sharing, semantic communications, and AI-driven network design. His work explores low-latency, high-reliability communications , physical-layer security, and joint communication-sensing systems. In energy, he focuses on smart grid resilience , integration of renewable energy, privacy in energy data, and game-theoretic models for peer-to-peer energy trading. He also collaborates on epidemic modeling and control using network science and machine learning. The recent publications reflect a strong trend toward integrating machine learning with networked systems —whether in wireless communications, power grids, or public health. His work increasingly leverages graph neural networks , diffusion models , and data-driven optimization to solve complex, real-world problems involving uncertainty, delay, and security. The interdisciplinary nature of his research is evident in the breadth of journals, from IEEE Transactions to PNAS. His numerous scientific awards highlight his global impact: Elected Member of the U.S. National Academy of Sciences and National Academy of Engineering Foreign Member of the Royal Society (UK), Chinese Academy of Sciences, and Royal Society of Canada IEEE Alexander Graham Bell Medal, IEEE Marconi Prize Paper Award (twice), IEEE James H. Mulligan Education Medal Multiple honorary doctorates from institutions including Imperial College, University of Waterloo, and Tsinghua University John Fritz Medal, considered the highest honor in engineering Prof. Poor has advised numerous Ph.D. students who have gone on to prominent academic and industrial positions. His research is supported by extensive collaborations and likely major grants from NSF, DOE, and other agencies, though specific grants are not listed. He has held visiting positions at UC Berkeley, Caltech, and Imperial College, reflecting his international stature. He leads a dynamic research group working on the intersection of information theory, AI, and critical infrastructure. His research group is actively engaged in projects involving AI-enabled wireless networks , secure and resilient smart grids , and data-driven epidemic control . The team combines theoretical rigor with practical implementation, often publishing in top IEEE and interdisciplinary journals. They maintain a strong presence on arXiv, indicating ongoing, high-volume research output.