جستجوی استاد راهنمای دانشگاهی

Matteo Magnaniمشاهده پروفایل

استاد

• Network Science
• Social Network Analysis
• Computational Social Science
• +۱۰ مورد دیگر

Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.

• Wireless Communication
• Communication Systems
• Cellular Networks
• +۴ مورد دیگر

Ki Won Sung is an Associate Professor at KTH Royal Institute of Technology, specializing in wireless communication systems and network optimization. His research focuses on 5G/6G technologies, integrated sensing-communication systems, cell-free massive MIMO, and stochastic network modeling. Research interests include: Wireless network architecture design and optimization Resource allocation in multi-user communication systems Integrated sensing and communication (ISAC) Stochastic modeling of ultra-dense networks Energy-efficient communication protocols Millimeter wave and massive MIMO systems His recent publications demonstrate strong emphasis on beyond-5G systems, particularly cell-free massive MIMO deployments and URLLC applications. Research trends show consistent focus on network optimization through advanced signal processing, geometric decomposition methods, and cross-layer protocol design. Teaching activities include course management and examination for multiple degree projects and core courses: Communication Systems (IK2200) Mathematical Statistics (IX1501) Mobile Networks and Services (IK2560) Radio Networks (IK2510) Stochastic Simulation (II2206) Wireless Systems (IK1330)

Markus H. Flierlمشاهده پروفایل

دانشیار

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

• Internet of Things
• Open Source Software
• Program Comprehension
• +۶ مورد دیگر

Simon Butler is a Senior Lecturer in the Department of Information Technology at the University of Skövde's School of Informatics. He is an active member of the Distributed Real-Time Systems research group and contributes significantly to undergraduate computer science education. His academic work bridges theoretical research with practical industry applications in software development. Butler's research interests center on the challenges software developers face in creating and maintaining software systems, with particular focus on program comprehension, software maintenance, and the socio-technical and educational aspects of software development in hybrid environments where open source and proprietary solutions coexist. His work addresses critical issues in Internet of Things (IoT) systems and the practical implementation of open source software solutions in industrial contexts. His publication record from 2018-2023 reveals a strong research trajectory focused on open source software adoption, interoperability challenges, and standardization processes. His work spans diverse venues including Software Quality Journal, Journal of Systems and Software, IEEE Transactions on Software Engineering, and OpenSym proceedings, demonstrating both academic rigor and practical relevance to industry challenges. Butler is actively involved in several significant research projects including SUDO (2020-2024) addressing lock-in effects in IT systems, OSSD (2021-2023) developing educational programs for sustainable digitalization, and LIM-IT (2016-2020) investigating interoperability and long-term maintenance challenges. His teaching portfolio includes core computer science subjects such as Operating Systems, Object Oriented Programming, Algorithms and Data Structures, and Distributed Systems. As a member of the Distributed Real-Time Systems research group, Butler collaborates with industry partners to address real-world challenges in software development practices, particularly focusing on how organizations can effectively integrate open source solutions while avoiding problematic lock-in effects that compromise long-term system sustainability.

Haibo Liمشاهده پروفایل

استاد

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +۵ مورد دیگر

Haibo Li is a Full Professor of Media Technology at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and Digital Futures Faculty. His roles include leading the Media Lab in MID, directing Master’s programs in Media Technology, and teaching courses like Generative AI for Media Technology and Video Technology. He holds a Technical Doctorate from Linköping University (1993) and received the Nordic Best PhD Thesis Award (1994) and Docent title in Image Coding (1997). Research focuses on digital media technologies, including image/video compression, human-computer interaction, and AI-driven media systems. He pioneered the Digital Media Lab at Umeå University and co-founded UCIT. His work spans blind image deblurring, thermal comfort sensing, and multimodal emotion recognition. Over 250 publications and six patents highlight contributions to multimedia, AI, and signal processing. Key projects include developing contactless thermal measurement systems for smart buildings, real-time gesture recognition for wearable devices, and advanced neural networks for image restoration. Awards include the Nordic Best Thesis and leadership in EU projects. He chairs conferences and actively participates in MPEG standardization efforts.

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.

• Machine Learning
• Light Field
• Digital Image Processing
• +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.

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

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.

Piotr Rudolمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Robotics
• Computer Vision
• +۵ مورد دیگر

Piotr Rudol is a Researcher at Linköping University's Department of Computer and Information Science (IDA), part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His work focuses on advancing artificial intelligence, robotics, and their applications in collaborative systems, autonomous navigation, and UAV technology. Research interests include: Robotics mission planning and coordination Unmanned aerial vehicle (UAV) systems for search and rescue RDF graph synchronization for collaborative robotics Computer vision for object detection and geolocation Multi-agent systems integration His recent articles emphasize autonomous systems, sensor fusion, and distributed robotics frameworks. Notable contributions include frameworks for UAV-based human detection and collaborative mission planning systems. Piotr collaborates with senior researchers like Patrick Doherty and Mariusz Wzorek, contributing to projects funded through institutional research programs. He is actively involved in the IDA department's research initiatives, bridging theoretical AI advancements with practical robotic implementations.

• Gated Experts Compression
• Light Field Video
• Computer Engineering
• +۳ مورد دیگر

Yi-Hsin Li is an early-stage researcher affiliated with Mid Sweden University's Department of Computer and Electrical Engineering and collaborates with the Technische Universität Berlin through the PLENOPTIMA project. Her research focuses on light field video compression using gated experts and steered mixture of experts models. Current affiliation: Mid Sweden University Research collaboration: Technische Universität Berlin Her work intersects computer engineering and multimedia systems , emphasizing: Adaptive segmentation techniques Deep learning for image/video regression Model compression optimization Publications demonstrate expertise in machine learning applications for image processing and video coding . Active in the STC Research Centre.

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +۵ مورد دیگر

Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.

Jonas Ungerمشاهده پروفایل

استاد

• Computer Graphics
• Image Processing
• Artificial Intelligence
• +۳ مورد دیگر

Jonas Unger is a Professor at Linköping University's Department of Engineering and Natural Sciences (ITN) and Media and Information Technology (MIT). As a research leader in computer graphics and image processing, he contributes to advanced visualization techniques, GPU computing, and AI applications in healthcare and climate adaptation. Specializes in BRDF modeling, spectral light field imaging, and augmented intelligence Active in AI4Climateadaptation and Wallenberg Autonomous Systems Program (WASP) Recipient of the 2023 Chester Carlson Research Prize Develops AI methods for medical imaging (SCAPIS dataset) and autonomous systems His recent work focuses on deep learning for material acquisition, real-time rendering optimizations, and visual analytics pipelines for disaster response systems. Current projects include multi-agent reinforcement learning for air traffic control and GPU-accelerated signal processing techniques. Scientific awards include: Chester Carlson Research Prize (2023) He collaborates with Visual Sweden's Augmented Intelligence platform, co-leads major visualization conferences, and contributes to Horizon 2020 Marie Skłodowska-Curie funded research. Contact: jonas.unger@liu.se

Ehsan Miandjiمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

Patrick Dohertyمشاهده پروفایل

استاد

• Artificial Intelligence
• Autonomous Systems
• Knowledge Representation
• +۶ مورد دیگر

Patrick Doherty is a Professor and Head of the Artificial Intelligence Laboratory (AILAB) at Linköping University's Department of Computer and Information Science (IDA). His research focuses on knowledge representation, automated planning, and intelligent autonomous systems, with applications in UAV-based emergency rescue and disaster informatics. He leads the AIICS division, emphasizing collaborative robotics and multi-agent systems. He holds a EURAI (ECCAI) fellowship and serves as Editor-in-Chief of the Artificial Intelligence Journal. His work includes developing UAV systems for alpine rescue, real-time mission planning frameworks, and robust robotic collaboration protocols. Notable contributions include the SHERPA project and the RGS RDF graph synchronization system. Doherty's research also addresses human-robot collaboration, safety-critical decision-making, and resilient autonomous systems within the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research group has won international competitions in UAV and automated planning domains, demonstrating advanced UAV deployment in emergency scenarios. Key areas include geolocated object detection, distributed situation awareness, and efficient wireless mesh network configurations for disaster response. Doherty frequently delivers keynote speeches at conferences such as IJCAI, KR, and ECAI, emphasizing practical applications of AI in safety-critical systems. Key Projects: SHERPA (human-robot collaboration in alpine rescue), WASP public safety arena, UAV navigation systems Awards/Fellowships: EURAI Fellow, Editor-in-Chief of Artificial Intelligence Journal Labs/Teams: AILAB, Unmanned Aircraft Systems Technology Laboratory (UASTECH) Publications span foundational AI theory (e.g., forgetting policies, rough set reasoning) to applied robotics (e.g., UAV navigation, real-time search algorithms). His work bridges high-level mission planning with low-level control, ensuring robustness and autonomy in dynamic environments.