جست‌وجوی استاد و پژوهشگر

• Intelligence Partitioning
• IoT
• Deep Learning
• +۵ مورد دیگر

Irida Shallari is a Lecturer in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, specializing in Intelligence Partitioning for IoT systems. Her research spans Deep Learning , Embedded Systems , and Data Fusion , with applications in Edge Computing , 6G , and Smart Agriculture . Education: Doctoral Thesis (2021): "Intelligence Partitioning for IoT : Design Space Exploration for a Data Intensive IoT Node" (Mid Sweden University) Licentiate Thesis (2019): "Intelligence Partitioning for IoT : Communication and Processing Inter-Effects for Smart Camera Implementation" (Mid Sweden University) Her research focuses on optimizing IoT node architectures, balancing Communication vs. Computation trade-offs, and deploying Deep Learning in resource-constrained environments. Recent work includes Time-dependent Clustering for neural network quantization, ArUco Marker Calibration , and Generative AI in Education ethics. The 15 most recent publications (2025-2024) highlight trends in Distributed CNN Optimization , Model Evaluation for industrial IoT, and cross-disciplinary applications in Environmental Monitoring and Academic Integrity . No scientific awards are explicitly mentioned in the provided text.

Karl-Ludwig Besserمشاهده پروفایل

استادیار

• Ultra-reliable communications
• Resilience of communication systems
• Copulas
• +۲ مورد دیگر

Karl-Ludwig Besser serves as Assistant Professor in the Division of Communication Systems within Linköping University's Department of Electrical Engineering since January 2025. Previously, he completed a postdoctoral fellowship at Princeton University's Department of Electrical and Computer Engineering (March 2023–December 2024), following his 2022 PhD from Technische Universität Braunschweig. His academic foundation includes: Dipl.-Ing. in Electrical Engineering, Technische Universität Dresden (2018) PhD in Electrical Engineering, Technische Universität Braunschweig (January 2022) Dr. Besser's research centers on ultra-reliable communication systems , physical layer security under resource constraints , and copula-based modeling of channel dependencies . His work bridges theoretical information theory with practical machine learning applications, particularly in reconfigurable intelligent surfaces (RIS) and integrated sensing-communication (ISAC) systems. Recent publications demonstrate expertise in secret-key budget management for resilient networks and UAV swarm coordination using 3D beamforming techniques. Analysis of his 15 most recent publications (2024–2025) reveals three dominant themes: (1) RIS optimization using physics-informed neural networks addressing mutual coupling challenges, (2) fundamental limits of physical layer security in dependent fading channels with secret-key budgets, and (3) machine learning solutions for UAV mobility management and mmWave resource allocation. His work consistently targets ultra-reliable low-latency communication (URLLC) requirements for next-generation networks. As part of Linköping University's Communication Systems division, Dr. Besser contributes to research initiatives including Wireless Communications for Distributed Intelligence and Integrated Sensing and Communications (ISAC) . While specific grant details aren't documented in source materials, his research aligns with European 6G initiatives and national security-focused wireless projects. The division actively supervises 25+ PhD students across topics including drone swarm communication, energy-efficient signal processing, and smart city sensor networks, where Dr. Besser likely participates in student mentorship.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

Luciana Fenoglioمشاهده پروفایل

دانشیار

• Satellite Geodesy
• Sea Level Change
• Satellite Altimetry
• +۴ مورد دیگر

Luciana Fenoglio is an Associate Professor at the Space Geodesy and Geodynamics Unit, Onsala Space Observatory (Chalmers University of Technology) and a Senior Academic Councillor at the Institute for Geodesy and Geoinformation, University of Bonn. She joined the University of Bonn in 2015 and holds a habilitation in Physical Geodesy from the Technical University of Darmstadt (2014). Her research focuses on satellite geodesy, altimetry, and monitoring water cycle dynamics. Research Interests: Dr. Fenoglio investigates small-scale ocean variability, sea level change, satellite altimetry techniques (conventional and delay Doppler), coastal data retracking methods, and height reference systems. Her work bridges geodesy, oceanography, and hydrology, emphasizing high-resolution satellite applications for coastal and inland water monitoring. Publication Focus: Her recent articles (2020-2025) predominantly analyze advanced satellite altimetry methods (SAR, SWOT, Sentinel-3) for coastal sea level monitoring, river discharge estimation, and geodetic model validation. Key themes include radar signal processing, hydrological applications, climate-related ocean dynamics, and international mission collaboration. Awards: Italian Ministry of Education Award for Physics/Astronomy (scholarship abroad) First Prize for Astronomy Research Report (University of Turin) Projects & Leadership: She leads/participates in major initiatives including ESA's Climate Change Initiative, DFG-EDDY, SWOT Science Team projects (CONWEST-DYCO, REFECCT), and chairs the IAG Sub-Commission on High-Resolution Altimetry. She also coordinates validation for Sentinel-3 and Copernicus missions. Affiliations: Dr. Fenoglio directs research at the Astronomical, Physical and Mathematical Geodesy Group (University of Bonn) and collaborates globally through the Ocean Sea Surface Topography Science Team and Coastal Altimetry Working Group.

Hazem Torfahمشاهده پروفایل

استادیار

• cyber-physical systems
• formal verification
• runtime monitoring
• +۵ مورد دیگر

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

Juliette Griffiéمشاهده پروفایل

پژوهشگر ارشد

• Light Microscopy
• Image Analysis
• Immunology
• +۴ مورد دیگر

Juliette Griffié is a DDLS Fellow at Stockholm University, affiliated with the Computational Microscopy for Cell Biology (CMCB) laboratory. Her research focuses on developing advanced image analysis strategies and machine learning tools to address fundamental questions in cell biology using light microscopy. Research Interests include: Advanced light microscopy techniques Machine learning applications in image analysis Immunology, particularly autoimmune diseases and immune cell interactions Generative models for dynamic cellular imaging Bayesian statistical methods in microscopy data analysis Her work spans molecular-level studies, computational modeling, and software development for super-resolution and single-molecule localization microscopy (SMLM). Key publications appear in Nature Methods , Cell Reports , and Nature Protocols . Scientific Awards : Wallenberg National Program for Data-Driven Life Science (DDLS) Fellow

• Control Systems
• Signal Processing
• Sensor Fusion
• +۷ مورد دیگر

Fredrik Gunnarsson is an Adjunct Professor at Linköping University's Department of Electrical Engineering (ISY), specializing in Automatic Control (RT). His research focuses on control systems, signal processing, and sensor fusion, with applications in positioning, navigation, and optimization for autonomous systems. University: Linköping University Department: Department of Electrical Engineering (ISY) Academic Rank: Adjunct Professor Research Interests Fredrik's work spans advanced signal processing, sensor fusion, and control algorithms. He contributes to 5G positioning, Gaussian process state-space models, and indoor localization. His research intersects with optimization-based control for autonomous systems and machine learning techniques in dynamic environments. Article Trends His recent publications emphasize wireless positioning, statistical filtering, and sensor fusion methodologies. Topics include Kalman filtering, Gaussian process modeling, particle methods for indoor navigation, and optimization algorithms for control systems. Keywords span Control Systems , Signal Processing , Machine Learning , and Wireless Communication . Labs and Collaborations LINK-SIC - Linköping Center for Sensor Informatics and Control Automotive Systems research SEDDIT competence center

Marian Codreanuمشاهده پروفایل

دانشیار

• Wireless Networking
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Photonics
• Terahertz Technology
• Semiconductor Devices
• +۳ مورد دیگر

Josip Vukusic is a Research Professor at Chalmers University of Technology, Sweden, where he has been affiliated since 2004. He leads research in the Physical Electronics Laboratory, focusing on THz technology, semiconductor devices, and optoelectronics. His work involves device modeling, fabrication, and characterization of frequency multipliers and photomixers for THz generation. Vukusic holds a diploma and Ph.D. in photonics from Chalmers (1997 and 2003). His research spans vertical-cavity surface-emitting lasers, quantum dots, heterostructure barrier varactors, and graphene-integrated electronics. Vukusic co-founded Wasa Millimeter Wave AB, commercialized semiconductor laser modeling software, and consults for tech firms. His contributions include over 40 journal/conference publications and a US patent in semiconductor laser technology. Vukusic's recent work emphasizes high-power THz sources, metamaterial applications, and biological sensing via millimeter-wave systems. His articles explore non-thermal effects of THz radiation on proteins, tunable frequency multipliers, and graphene-based devices. He collaborates on interdisciplinary projects combining electronics with biomedical and material sciences. Key projects include developing compact 340 GHz receiver front-ends, high-efficiency W-band triplers, and catadioptric lenses for imaging. Vukusic's expertise lies in heterogeneous integration of THz circuits and optimizing diode performance under extreme conditions.

Olof Dahlqvist Leinhardمشاهده پروفایل

دانشیار

• Magnetic Resonance Imaging (MRI)
• Muscle Health
• Metabolic Disorders
• +۵ مورد دیگر

Olof Dahlqvist Leinhard is an Associate Professor at Linköping University, affiliated with the Faculty of Medicine and Health Sciences and the Department of Health, Medicine and Caring Sciences. He is part of the Division of Diagnostics and Specialist Medicine (DISP) and the Unit of Radiological Sciences (RAD). His research focuses on advancing MRI techniques to study fat and muscle tissue in health and disease, particularly examining the impact of metabolic disorders, aging, and treatments on muscle and fat composition. Research interests include the development of MRI-based methods for quantifying skeletal muscle health, adipose tissue distribution, and their associations with cardiometabolic outcomes. He investigates conditions such as type 2 diabetes, muscle diseases (e.g., Duchenne Muscular Dystrophy, FSHD), liver cirrhosis, and the effects of hormone therapy in transgender populations. His work also addresses the role of muscle composition in frailty, physical performance, and mortality risk across diverse patient groups. His publications explore topics like the efficacy of drugs (e.g., tirzepatide, liraglutide) on muscle health, longitudinal changes in body composition after interventions, and imaging applications in neuromuscular and metabolic disorders. He emphasizes translating imaging innovations into clinical practice for better disease assessment and patient management. Scientific awards: None explicitly listed in the provided texts. Advising and grants: No formal advisees or grant details are mentioned. His research collaborations include studies on fibromyalgia, COPD, and liver transplant recipients, reflecting a focus on interdisciplinary projects. Labs/Teams: Active within the Division of Diagnostics and Specialist Medicine (DISP) and the Unit of Radiological Sciences (RAD), contributing to clinical and experimental studies in radiology and metabolic medicine.

• Machine Learning
• Statistical Inference
• Sequential Monte Carlo Methods
• +۳ مورد دیگر

Alessandro Mastrototaro holds a PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology. His research focuses on advancing statistical learning methods, particularly sequential Monte Carlo (SMC) and variational inference techniques for state-space models. He currently teaches Regression Analysis (SF2930) at KTH, serving as course responsible and instructor. His work emphasizes online learning algorithms that enable real-time parameter estimation and particle proposal adaptation in dynamic data environments. Key contributions include the development of the Online Variational Sequential Monte Carlo algorithm and the ALVar estimator for adaptive variance estimation in particle filters. Publications span machine learning conferences (e.g., ICML) and journals like the Journal of the American Statistical Association, showcasing expertise in computational statistics and algorithmic innovation.

• Geodesy
• GPS Technology
• Atmospheric Science
• +۳ مورد دیگر

Anna Jensen is a Professor of Geodesy at KTH Royal Institute of Technology, specializing in atmospheric effects on Global Navigation Satellite Systems (GNSS). Her research focuses on modeling ionospheric and tropospheric influences on GPS signals, particularly how weather parameters and electric currents impact signal transmission. Applications of her work include precision surveying, aviation navigation, and construction site monitoring. She has contributed to projects such as the Fehmarnbelt Fixed Link positioning system and Arctic navigation challenges. Her research interests span geodesy, satellite navigation, and remote sensing, with a strong emphasis on improving GNSS accuracy in challenging environments like urban areas and polar regions. Jensen’s studies address issues like ionospheric scintillation, RFI geolocation with smartphones, and robust positioning in jamming conditions. Jensen has published extensively on topics including network RTK data screening, GNSS network optimization, and ionospheric modeling. Her work often integrates statistical methods and geostatistical techniques to enhance positioning reliability. No scientific awards or grants are explicitly mentioned in the provided texts. She has advised no formal students, though her involvement in the Fehmarnbelt project and other large-scale infrastructure initiatives highlights her role in applied geodetic engineering. No specific labs or teams are detailed, but her collaborations likely involve KTH’s geomatics and geophysics groups.

• Data Science
• Machine Learning
• Machine Vision
• +۸ مورد دیگر

Roger G Nyberg is a Senior Lecturer at Dalarna University's School of Information and Engineering, Department of Information Science. He holds a PhD from Edinburgh Napier University (2016) and teaches courses in artificial intelligence, data science, software engineering, and project management. As head of the Informatics main field of studies, he oversees programs in Systems Science, Graphic Design and Web Development, and Digital Services. His research focuses on Data Science, Machine Learning, Machine Vision, and Monitoring, with applications in transportation, healthcare, and e-commerce. Key areas include automating decision-making processes, analyzing road conditions using acoustic and visual data, and studying hygrothermal transfer in building materials. Recent publications highlight work on gravel road condition evaluation using multimodal deep learning, CO2 emissions in parking systems, and exercise interventions for musculoskeletal pain. Nyberg supervises undergraduate and advanced theses and actively contributes to academic boards and committees at Dalarna University.

Fredrik Johanssonمشاهده پروفایل

مدرس ارشد

• Neurosciences
• Cognitive Science
• Neurobiology
• +۳ مورد دیگر

Fredrik Johansson is an Associate Senior Lecturer at Lund University, affiliated with the Department of Associative Learning Cognitive Science. His research focuses on neurobiological mechanisms underlying learning, memory, and timing, particularly within cerebellar systems. He investigates classical conditioning pathways, alcohol's impact on neural learning processes, and Purkinje cell dynamics using electrophysiological and computational methods. Key research interests include cerebellar circuitry, synaptic plasticity, and the neural substrates of temporal processing. Collaborative work spans neurophysiology experiments and computational modeling to understand how cerebellar neurons encode duration and adapt to behavioral changes. His studies often involve in vivo electrophysiology and analysis of neural engram formation. Publications emphasize cerebellar learning mechanisms, with recent work exploring nucleo-olivary stimulation effects on climbing fibers and alcohol's role in disrupting conditioned responses. No scientific awards are explicitly mentioned, though his work contributes to foundational neuroscience knowledge. Advising and grants: No formal advisees or grant details provided in the text. Research is conducted within collaborative teams focusing on cognitive and neurobiological systems.

Ove Edforsمشاهده پروفایل

استاد

• Radio Systems
• Statistical Signal Processing
• Massive MIMO
• +۵ مورد دیگر

Ove Edfors is a Professor at the Department of Electrical and Information Technology, Lund University, affiliated with the Faculty of Engineering (LTH). His primary research focuses on radio systems, statistical signal processing, and massive MIMO technologies. He is a core member of the LTH Profile Area: AI and Digitalization and the LU Profile Area: Natural and Artificial Cognition. Edfors leads the NEXTG2COM Vinnova Competence Centre and contributes to ELLIIT initiatives. His research spans multi-carrier systems, low-complexity algorithms, and wireless communication applications. Key projects include 6G radio testbed development and millimeter-wave channel characterization. He has co-authored over 225 publications and supervised 22 graduate students. Notable achievements include the IEEE Signal Processing Society Donald G. Fink Award (2023) and the IEEE Communications Society Best Tutorial Paper Award (2018). Recent work emphasizes indoor localization via multi-sensor fusion (LuViRA Dataset) and energy-efficient MIMO processors. Active collaborations involve global institutions and industry partners in 5G/6G infrastructure. Edfors' contributions align with UN SDGs for affordable and clean energy (Goal 7) through energy-efficient wireless systems and innovation (Goal 9).