Find Academic Supervisors

Michael FelsbergView profile

Professor

• Artificial Visual Systems
• Computer Vision
• Robot Vision
• +4 more

Michael Felsberg is a Professor and Head of Division at the Department of Electrical Engineering (ISY) at Linköping University, leading the Computer Vision Laboratory (CVL). His research focuses on artificial visual systems (AVS), including 3D computer vision, computational imaging, object tracking, and autonomous systems. He emphasizes HVS-inspired approaches to bridge the gap between human and machine vision capabilities. Notable achievements include over 20,000 citations (h-index 47), leadership roles in the Wallenberg AI, Autonomous Systems and Software Program (WASP), and recognition as Sweden’s top AI researcher by Vinnova. His work spans academic contributions, industry collaborations, and interdisciplinary projects like climate science applications of machine learning. Positions : WASP Executive Committee Member, WASP Area Cluster Leader for Machine Learning, and Vice-Head of Department (Electrical Engineering). Education : Extensive academic background in electrical engineering and computer vision (details not explicitly stated). Research trends in his articles reflect advancements in autonomous systems, multimodal AI, and robust vision models. His teams address challenges like object tracking, generative models for 3D simulation, and culturally diverse AI systems. Awards : Tracking Challenge Winner (OpenCV, 2015) Best Paper Awards (ICPR 2016, VISAPP 2021) Vinnova’s Highest-Ranked Swedish AI Researcher (2018) He advises numerous PhD students and oversees grants in WASP-funded initiatives. CVL collaborates on projects like disaster-response robotics and Berzelius supercomputer utilization for AI.

Saleh JavadiView profile

Senior Lecturer

• Systems engineering
• Signal processing
• Machine learning
• +1 more

Saleh Javadi is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He is actively engaged in research and teaching within the field of systems engineering. His educational background includes: B.Sc. in Electrical-Control Engineering from Amirkabir University of Technology (2009) M.Sc. in Electrical, Electronic and Systems Engineering from The National University of Malaysia (2013) Ph.D. in Systems Engineering from Blekinge Institute of Technology (BTH) (2021) Saleh Javadi's research focuses on signal processing, machine learning, and computer vision , with applications spanning remote sensing, intelligent transportation systems, and AI-driven industrial optimization. His work bridges theoretical advancements with practical implementations, particularly in SAR imagery analysis, drone-based agricultural monitoring, and traffic surveillance systems. His recent publications demonstrate a strong focus on remote sensing technologies, particularly Synthetic Aperture Radar (SAR) image processing and analysis. There's a clear trend toward applying machine learning techniques to solve complex problems in aerial and satellite imagery, traffic monitoring, and agricultural applications. His research shows interdisciplinary connections between computer vision, signal processing, and practical engineering applications. Saleh Javadi has received significant recognition for his innovative work: Innovator of the Year award (SKAPA – Innovation Prize in Memory of Alfred Nobel) in Blekinge for innovative efforts in optimizing and reducing energy consumption in industries by using artificial intelligence ÅForsk Entrepreneur's prize at the Swedish Innovation Council Day – Swedish Incubators & Science Park's annual conference in May 2019 Dr. Javadi is involved in practical applications of his research through projects such as "Artificiell intelligens AI kan reducera ogräsfrön i utsäde" (ongoing) and "Bekämpa Renkavle med hjälp av drönare och Artificiell Intelligens (AI)" (completed). His work demonstrates a strong commitment to translating academic research into real-world solutions that address industrial and environmental challenges. His research appears to be conducted within a collaborative framework, working with colleagues on drone technology, SAR image analysis, and AI applications across multiple domains including agriculture, maritime monitoring, and transportation systems.

Damir IsovicView profile

Associate Professor

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +3 more

Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.

• Quality of Experience
• Video Quality Assessment
• Augmented Reality
• +7 more

Kjell Brunnström serves as an Adjunct Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, based in Sundsvall. He maintains active affiliation with the STC Research Centre, contributing to cutting-edge research in multimedia quality assessment and immersive technologies through extensive international collaborations. His research centers on Quality of Experience (QoE) with specialized focus on 3D video systems , virtual reality environments , and augmented teleoperation interfaces . He pioneers methodologies for subjective and objective video quality evaluation, particularly examining human factors in sports broadcasting (Video Assistant Referee systems), mining applications, and automotive displays. His work bridges theoretical psychophysical models with industrial implementations through standards development like ITU-T Rec. P.919. Analysis of his 15 most recent publications reveals consistent investigation into latency effects, distortion impacts, and viewing condition variables across emerging media formats. Key application domains include sports video refereeing, 360-degree video systems, VR simulators for remote machinery control, and adaptive streaming protocols, demonstrating strong industry-academia translation. Scientific Awards: No awards documented in source material. Advising and Grants: Student supervision and grant funding details were not disclosed in available documentation. Labs and Teams: Brunnström operates within Mid Sweden University's STC Research Centre framework, collaborating with global institutions including Royal Institute of Technology (Sweden), University of Surrey (UK), and multiple European research consortia. His work directly informs ITU standardization efforts and industrial implementations in broadcast and automotive sectors.

• Network Performance
• Cybersecurity
• Congestion Control
• +4 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.

Saad MubeenView profile

Professor

• Embedded Systems
• Real-Time Communication
• Predictable Software Development
• +5 more

Saad Mubeen is a Full Professor of Computer Science at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. He holds a Master's in Electrical Engineering (Embedded Systems) and a PhD in Computer Science and Engineering from Mälardalen University (2014), with a Docent title (2018) focused on vehicular embedded systems. His research emphasizes predictable embedded systems, timing analysis for real-time communication, and component-based software design. Key areas include model-driven development for automotive systems, integration of TSN/5G networks, and fault-tolerant industrial architectures. He has led projects on end-to-end timing analysis in distributed systems, ROS 2 verification, and cognitive edge-cloud scheduling. Publications span 2021–2025, focusing on real-time systems, network protocols (TSN, AVB, 5G), and industrial automation. Notable work includes frameworks for TSN configuration, fault diagnosis tools using NETCONF, and scheduling algorithms for heterogeneous edge-cloud environments. His contributions address critical challenges in timing predictability, security, and resource optimization for cyber-physical systems. Education contributions include problem-based learning modules for vehicular software engineering. He is actively involved in bridging academia and industry through collaborative research on next-generation automotive and industrial systems.

• Internet of Things
• Open Source Software
• Program Comprehension
• +6 more

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.

Alireza MohammadinodooshanView profile

Research Fellow

• Social Media Analysis
• User Engagement Dynamics
• Data Mining
• +3 more

Alireza Mohammadinodooshan is a Postdoctoral Fellow at Linköping University's Department of Computer Science (IDA), Sweden, working within the Database and Information Technology (ADIT) research group. He contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP) – Sweden's largest individual research initiative – focusing on data-driven analysis of social media engagement dynamics across Twitter, Facebook, and Instagram platforms. His research centers on quantifying how political bias, news reliability, and content-agnostic factors shape temporal user engagement patterns. Key interests include social media analysis, user engagement dynamics, data mining, information systems, network science, and artificial intelligence, with emphasis on cross-platform comparative studies and algorithmic amplification effects in news consumption. Analysis of his 15 most recent publications reveals consistent focus on temporal modeling of engagement decay, multi-format content interaction (photos/videos/albums), and the interplay between news source characteristics and user behavior. His methodological approach combines large-scale dataset analysis with network theory to identify virality predictors and platform-specific engagement mechanics. No scientific awards were documented in available sources. No advising roles or research grants were referenced in the provided materials. He operates within the ADIT research group at Linköping University, which specializes in advanced database and information systems for the digital society. This group forms part of IDA's broader WASP-affiliated ecosystem focused on AI-driven autonomous systems, enabling interdisciplinary collaboration on large-scale data challenges in social computing.

Haibo LiView profile

Professor

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +5 more

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.

Ali Hassan SodhroView profile

Senior Lecturer

• AI/ML for Edge/Cloud Computing
• Smart and Healthcare Internet of Things (IoT)
• Internet of Medical Things (IoMT)
• +6 more

Ali Hassan Sodhro is a Senior Lecturer in Computer Science at Kristianstad University's Faculty of Natural Science, Department of Computer Science. With over 80 publications in top-tier journals and conferences, Dr. Sodhro has established himself as a prominent researcher in healthcare technology and networking systems. His educational background includes a PhD in Computer Applications Technology from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences (2016), an M.E in Communication Systems and Networks (2011), and a B.E in Telecommunication (2008) from Mehran University of Engineering and Technology in Pakistan. Dr. Sodhro's research focuses on the intersection of AI, healthcare technology, and networking systems. His primary areas include AI/ML for Edge/Cloud Computing, Smart Healthcare IoT, Physical Layer Security in 5G applications, and Energy Harvesting for Healthcare Systems. His work bridges theoretical computer science with practical healthcare applications, particularly in wireless sensor networks and medical data transmission. Analysis of his recent publications reveals a strong trend toward healthcare-focused IoT systems, particularly Internet of Medical Things (IoMT) applications. His research spans from foundational networking technologies like 5G and digital twins to practical implementations in medical equipment and patient monitoring systems, with increasing emphasis on AI integration for cybersecurity and efficiency. IEEE Senior Member Dr. Sodhro serves as Associate Editor for several prestigious journals including IEEE Transactions on Intelligent Transportation Systems (T-ITS) and IEEE Transactions on Industrial Informatics (TII). His research has secured multiple projects including the EU COST Action Project on Physical layer security for 6G systems (6G-PHYSEC), Smart and Pervasive Healthcare for Elderly Patients, and Energy-efficient Video Transmission in Wireless Body Sensor Networks. His collaborative network spans multiple institutions including Luleå University of Technology, Mid Sweden University, Linköping University, Chinese Academy of Sciences, and University of Glasgow, reflecting the international scope of his research in healthcare technology and networking systems.

Kamilla KlefbeckView profile

Senior Lecturer

• Special Education
• Intellectual Disabilities
• Autism Spectrum Disorders
• +6 more

Kamilla Klefbeck serves as a Senior Lecturer in the Department of Special Education at Stockholm University's Faculty of Education, bringing over two decades of practical experience in special education settings to her academic role. Previously on leave from Kristianstad University, she now focuses on advancing teacher professional development in adapted primary education through active research and teaching collaborations. Her academic foundation includes a PhD awarded in June 2022 through the Swedish Research Council-funded Special Education for Teacher Educators (SET) research school, alongside professional qualifications as a social pedagogue, preschool teacher, and special education teacher specialized in intellectual disabilities. This multifaceted background informs her commitment to bridging theory and practice in special education. Klefbeck's research centers on improving educational experiences for students with intellectual disabilities and autism , employing methodologies like lesson study and video feedback to enhance teacher development. Her work emphasizes student engagement , learning outcome optimization , and inclusive pedagogical approaches , with particular attention to collaborative professional development frameworks that transform classroom practices. Analysis of her 25 research outputs (2017-2025) reveals a consistent trajectory toward systematic investigations of lesson study applications, autism-specific interventions, and assessment competency development. Her publications demonstrate increasing international collaboration, methodological sophistication in scoping reviews, and practical translation of research into classroom tools—particularly through video feedback systems and PDSA cycle implementations for neurodiverse learners. She currently leads four research projects including a Japan-Sweden comparative study on lesson study in special education contexts and interventions to enhance teachers' assessment competencies. With 26 documented academic activities including peer-review services for journals like International Journal of Developmental Disabilities and Education Sciences , she actively contributes to scholarly discourse while maintaining strong school partnerships for implementation research. Collaborating closely with local educational institutions and international partners, Klefbeck implements findings through practical frameworks that address student well-being and learning access. Her work directly supports UN Sustainable Development Goal 4 (Quality Education) through evidence-based strategies for inclusive classrooms serving students with complex needs.

Erdal AksoyView profile

Associate Professor

• Semantic Scene Perception
• Action Semantics
• Autonomous Systems
• +9 more

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
• +3 more

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
• +5 more

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.

• Hyperspectral Imaging
• Computer Vision
• Sensor Technology
• +6 more

Benny Thörnberg is an Associate Professor (Docent) at Mid Sweden University, working in the Department of Computer and Electrical Engineering (DET). He is employed in the Electronics subject area and is affiliated with the STC Research Centre. His office is located in room L204b in Sundsvall, and he can be contacted at benny.thornberg@miun.se. Dr. Thörnberg completed his Licentiate thesis in 2004 and his Doctoral thesis in 2006, both from Mid Sweden University, focusing on "Memory modeling and synthesis for real-time video processing systems." His academic journey has centered around hardware and software solutions for imaging and sensor systems. Thörnberg's primary research interests span hyperspectral imaging , computer vision , sensor technology , and FPGA-based real-time processing systems . His work bridges theoretical development with practical applications, particularly in material analysis, environmental monitoring, and transportation safety. He has made significant contributions to short-wave infrared imaging, 3D reconstruction techniques, and specialized instrumentation for challenging environments like icing conditions on wind turbines and roads. His recent publications demonstrate a clear trend toward practical applications of advanced imaging technologies, with focus areas including material classification, atmospheric icing measurement, anti-icing agent detection, and autonomous systems. Thörnberg has developed innovative solutions such as the Material Imaging Analyzer (MIA) and cost-optimized multi-camera dome systems for volumetric surveillance, showing his ability to translate theoretical concepts into deployable technologies. Thörnberg leads or contributes to several research projects including MEQAL (Detect methane leaks with new technology) and SENAVIS (Sensor technology for smart de-icing). His completed projects span diverse areas from autonomous friction measurement and airport monitoring to wood disintegration processes and rail pollution detection. These projects highlight his interdisciplinary approach and ability to address real-world challenges through advanced engineering solutions. As part of the STC Research Centre, Thörnberg collaborates with a multidisciplinary team focused on sensor technology and imaging systems. His work often involves partnerships across different sectors, including transportation, energy, and materials science, demonstrating the broad applicability of his research. His recent patent for an "Imaging Material Analyzer" underscores his commitment to developing practical tools that bridge laboratory research and field applications.