جست‌وجوی استاد و پژوهشگر

Rodrigo Caballero Augiمشاهده پروفایل

استاد

• Atmospheric Dynamics
• Climate Dynamics
• Arctic Climate
• +۵ مورد دیگر

Rodrigo Caballero Augi is Professor of Dynamic Meteorology at Stockholm University's Department of Meteorology (MISU), where he leads research on atmospheric dynamics and climate systems. His work spans from planetary-scale waves to cloud droplet motion, examining how these processes shape past, present, and future climates. He maintains strong affiliations with the Bolin Center for Climate Research and the Swedish e-Science Research Centre. Caballero received his Laurea in Physics (1994) and PhD in Physics (1998) from La Sapienza University in Rome, Italy, under the guidance of Alfonso Sutera. His academic journey continued with postdoctoral positions at the University of Copenhagen and University of Chicago, followed by faculty roles at University College Dublin before joining Stockholm University in 2011. His research focuses on understanding atmospheric dynamics in the context of climate change, with particular emphasis on Arctic climate where warming occurs at more than twice the global average rate. His group investigates circulation systems that co-evolve with ice sheets, mountain ranges, and carbon redistributions between climate system compartments. Using a combination of simple models, general circulation models, observational data analysis, and statistical modeling, his team explores how atmospheric dynamics influences energy and material exchanges across the climate system. Recent publications reveal a strong focus on Arctic climate processes, particularly moist intrusions, cloud feedbacks, and polar amplification mechanisms. His work spans multiple disciplines including atmospheric physics, climate dynamics, and polar meteorology, with significant contributions to understanding storm tracks, cloud albedo asymmetries, and energy transport mechanisms in changing climates. Caballero has mentored numerous PhD students and postdocs who have gone on to successful careers at institutions worldwide, including University of Arizona, Uppsala University, Chalmers University of Technology, and UQAM in Montreal. His research group continues to investigate critical climate questions through projects like Arctic Climate Across Scales, North Atlantic Deep-Water formation, and storm track-cloud feedback relationships.

Praveen Kumar Dontaمشاهده پروفایل

دانشیار

• Distributed Computing Continuum Systems
• Learning Techniques in IoT
• AI/ML for Computing Systems
• +۵ مورد دیگر

Praveen Kumar Donta is an Associate Professor (Docent) and Senior Lecturer at the Department of Computer and Systems Sciences, Stockholm University, Sweden. His research focuses on distributed computing continuum systems, learning-driven approaches for IoT and edge computing, and intelligent data protocols. He leads the Distributed Immersive Participation research group which investigates how humans and things can be more connected and exchange information in real and virtual societies. Education: Ph.D. in Computer Science & Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad (2021) Visiting Ph.D. Fellow at Mobile&Cloud Lab, University of Tartu, Estonia (2019-2020) Master in Technology from JNTUA, Ananthapur (2014) Bachelor in Technology from JNTUA, Ananthapur (2012) Dr. Donta's research centers on distributed computing continuum systems that integrate cloud, edge, and IoT devices to deliver scalable and low-latency computing resources. His work explores learning techniques in IoT, AI/ML for computing systems, cognition and causality in computing systems, and cyber-physical continuum applications. He investigates how human body analogies can inform the design of more resilient and efficient distributed systems, as well as developing frameworks for privacy enforcement, equilibrium in computing continuum systems, and energy-efficient user interactions with smart environments. His research has significant applications in smart city management, satellite services, and intelligent transportation systems. Dr. Donta's publication record demonstrates a strong focus on the intersection of distributed systems, machine learning, and privacy-preserving technologies. His recent work shows an increasing emphasis on human-inspired approaches to distributed computing, with particular attention to making these systems more interpretable, efficient, and adaptable. His research spans theoretical foundations of computing continuum systems to practical implementations in areas like satellite services, smart environments, and anomaly detection. Scientific Awards and Recognition: IEEE Senior Member ACM Professional Member Dr. Donta serves as an editorial board member for several prestigious journals including IEEE Internet of Things Journal, Computing (Springer), Transactions on Emerging Telecommunications Technologies (Wiley), Measurement, and Computer Communications (Elsevier). He actively mentors the next generation of researchers, currently supervising PhD student Alfreds Lapkovskis and co-supervising Shubham Vaishnav. His research is supported by projects such as the Heterogeneous Computing Continuum for a Sustainable Smart City Management (HCSCM), which aims to develop scalable, secure solutions for urban environments by integrating IoT, edge, and cloud computing. As part of the Distributed Immersive Participation research group, Dr. Donta collaborates with researchers across disciplines to explore how technological advances enable humans and things to be more connected. The group focuses on application areas such as culture, transport, intelligent vehicles and e-health, developing solutions that enhance participation in both real and virtual societies.

Jan Engvallمشاهده پروفایل

استاد مهمان

• Cardiovascular Imaging
• Cardiac MRI
• Patient-Based Diagnostics
• +۳ مورد دیگر

Jan Engvall is a Visiting Professor at Linköping University, working within the Department of Health, Medicine and Care and the Department of Diagnostics and Specialist Medicine. He divides his time between research in patient-based diagnostics and clinical work in cardiac imaging. His educational background includes a Medical degree from the University of Gothenburg (1979), a Dissertation from Linköping University (1993), and a Postdoctoral fellowship at Stanford University (2001). He achieved Associate Professor status in 2002 and was appointed Adjunct Professor in 2011. Dr. Engvall's research focuses on cardiovascular imaging, particularly using MRI techniques to develop more accurate and less stressful examination methods for patients. His work is closely tied to the Swedish Cardiopulmonary BioImage Study (SCAPIS), a large population-based study examining cardiovascular and pulmonary health in 30,000 individuals aged 50-65. He also contributes to the CARDIPP study, which investigates cardiovascular risk factors in patients with type 2 diabetes, and the Cardiovascular Imaging and Modeling (CIM) research cluster, which develops new image-based methods for assessing heart function and hemodynamics. Analysis of his recent publications reveals a strong focus on advanced cardiovascular imaging techniques, particularly 4D Flow MRI applications. His research spans multiple areas including valvular heart disease, atherosclerosis detection, cardiac remodeling, and the relationship between metabolic factors and cardiovascular health. Much of his work leverages data from the SCAPIS cohort, examining subclinical manifestations of cardiovascular disease in the general population. Dr. Engvall serves on the local steering group for SCAPIS in Linköping and acts as a technical assessor for Swedac regarding the quality standard ISO 17025. His educational contributions include teaching functional anatomy of thoracic organs in the medical program and pressure and flow courses in the Master of Science in Medical Technology program. He collaborates extensively with colleagues including Professor Tino Ebbers, Professor Carl-Johan Carlhäll, and Adjunct University Lecturer David Kylhammar within the Cardiovascular Sciences Unit at Linköping University.

Ingela Nyströmمشاهده پروفایل

استاد

• Medical image analysis
• 3D visualization
• Haptics in surgery planning
• +۳ مورد دیگر

Ingela Nyström is a Professor in Visualization at Uppsala University's Department of Information Technology. She serves as the node coordinator for InfraVis and as Director of Postgraduate Studies at the Department of Information Technology. Her interdisciplinary work bridges Uppsala University's three academic domains through collaborations with the Centre for Image Analysis (CBA), Centre for Women's Mental Health (WOMHER), Uppsala Centre for Digital Humanities (CDHU), and Medtech Science & Innovation (MTSI). Medical image analysis 3D visualization Haptics in surgery planning Interactive segmentation Digital geometry Biomedical engineering Her recent research focuses on rotation-equivariant neural networks for biomedical image classification, interactive segmentation tools for cranio-maxillofacial surgery planning, and precision evaluation of intraoral scanning technologies. Publications since 2023 demonstrate continued work on 3D imaging protocols for implant dentistry and surgical applications. 2024: Equivariant CNNs for rotation-invariant biomedical imaging 2023: In vivo precision studies of full-arch implant scans 2021: Virtual surgical planning with haptic assistance 2016-2017: Multimodal robotics perception and 3D segmentation tools 2014: Orbital morphology analysis in craniosynostoses 2005-2011: Foundational work in 3D skeletons and fuzzy object measurements

• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +۵ مورد دیگر

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.

Håkan Grahnمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Systems
• GPU Computing
• +۷ مورد دیگر

Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.

• Image Processing
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Shafiullah Soomro serves as Associate Professor in the Department of Artificial Intelligence at Quaid-e-Awam University of Engineering Science and Technology, Pakistan. He completed his Ph.D. in Application Software from Chung-Ang University, South Korea (2018), where he was honored as Best PhD Graduate. Ph.D., Application Software, Chung-Ang University (2018) His research spans medical image segmentation, computer vision, and AI applications in environmental monitoring. Specializing in automatic segmentation techniques using machine learning, he combines theoretical frameworks with practical biomedical implementations. Recent work integrates Swedish National Forest Inventory data with airborne laser scanning for forest attribute prediction. Current research projects include ForestMap (global forest cartography), AI-driven tree volume measurement (Sweden-Brazil collaboration), and machine learning models for predicting mechanical properties of oxynitride glasses. Best PhD Graduate, Chung-Ang University Dr. Soomro has mentored multiple MS students across Korea and Pakistan while supervising 2 Master's and 1 PhD students currently. His teaching portfolio includes 11 undergraduate, 2 MS, and 1 PhD courses such as Artificial Intelligence, Digital Image Processing, and Advanced Image Processing/Computer Vision. He actively contributes to academic curriculum design and research community development within the Computer Science and Artificial Intelligence department.

Evgeny Osipovمشاهده پروفایل

استاد

• Artificial Intelligence
• Vector Symbolic Architectures
• Hyperdimensional Computing
• +۳ مورد دیگر

Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.

Najmeh Abiriمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Generative Models
• +۵ مورد دیگر

Najmeh Abiri serves as a Senior Lecturer in the Department of Computer Science and Media Technology at Malmö University's Faculty of Technology and Society, specializing in Machine Learning research and instruction. Her research spans Deep Learning , Generative Models , and Uncertainty Quantification , with methodological focus on data imputation for noisy or incomplete datasets. She actively bridges theoretical advances with real-world applications through interdisciplinary collaborations. In partnership with the Swedish Veterinary Institute, she develops machine learning solutions for large-scale tick surveillance systems—including automated tick species classification , pathogen distribution modeling , and data quality control protocols —advancing public health monitoring through citizen science frameworks.

• Medical Imaging
• Tomography
• Radiation Physics
• +۵ مورد دیگر

Massimiliano Colarieti Tosti is an Associate Professor at KTH Royal Institute of Technology, working within the Division of Biomedical Imaging. His academic responsibilities include teaching multiple courses in medical imaging, radiation physics, and biomedical engineering. His research focuses on the intersection of medical physics, imaging technology, and computational methods for healthcare applications. Dr. Colarieti Tosti's research interests span several critical areas in biomedical imaging and medical physics. His work primarily focuses on medical imaging systems , particularly in tomographic reconstruction techniques for modalities like PET and SPECT. He has made significant contributions to radiation physics and radiation therapy , developing methods for proton range monitoring and gamma imaging. His research also extends to computational hemodynamics , where he applies patient-specific flow simulations to cardiovascular applications. Additional areas of expertise include radiation detection , scintillator development , and contrast agent technology for multimodal imaging. An analysis of his recent publications reveals a strong focus on advancing medical imaging technologies through innovative computational approaches. His work shows a clear trajectory from fundamental research in radiation detection and crystal physics toward increasingly clinical applications. Key trends include the development of advanced reconstruction algorithms for PET imaging, the application of computational fluid dynamics to cardiovascular diagnostics, and the creation of novel imaging hardware for radiation therapy monitoring. His research consistently bridges the gap between theoretical physics and practical medical applications, with increasing emphasis on patient-specific modeling and clinical validation. Dr. Colarieti Tosti has been actively involved in academic teaching and curriculum development. He serves as course responsible and examiner for several key courses including Ionising Radiation Imaging, Radiation Physics and Biology, and Radiation Therapy. His teaching portfolio demonstrates a commitment to training the next generation of medical engineers and physicists through courses covering 3D Image Reconstruction, Medical Imaging Systems, and Simulation Methods in Medical Engineering.

• Contrastive Linguistics
• Typological Linguistics
• Lexical Semantics
• +۳ مورد دیگر

Åke Viberg is a Professor in General Linguistics at the Department of Linguistics and Philology, Uppsala University, specializing in Swedish language studies within global linguistic frameworks. His work bridges theoretical and empirical approaches to semantic analysis across languages. Viberg's research centers on contrastive and typological linguistics, with deep expertise in lexical semantics and corpus-based methodologies. He investigates how languages encode sensory experiences (particularly smell and vision), motion events, and emotional concepts, focusing on Swedish in cross-linguistic comparison. His work reveals systematic patterns in verb semantics across domains like perception, possession, and communication, demonstrating how cultural and cognitive factors shape lexical expression. Analysis of his 2009-2024 publications shows consistent focus on granularity in event verbalization, sensory semantics, and cross-linguistic variation in verb systems. Key trends include typological profiling of Swedish against European languages, corpus-driven studies of 'get' and posture verbs, and theoretical modeling of semantic fields. His research demonstrates how seemingly minor lexical differences reflect fundamental cognitive and cultural distinctions in language organization.

Karl Anderssonمشاهده پروفایل

استاد

• Cybersecurity
• 5G/6G Networks
• Internet of Things
• +۳ مورد دیگر

Professor Karl Andersson serves as Dean for the Faculty of Science and Technology and Head of Subject for Cyber Security at Luleå University of Technology in Skellefteå, Sweden. He holds positions in both the Department of Computer Science and the Department of Systems and Space Engineering, with research focus primarily in cybersecurity and next-generation network technologies. Dr. Andersson earned his Master's degree in Computer Science and Technology from the Royal Institute of Technology in Stockholm before working in industry with Capgemini Group as a consultant, project manager, business developer, and branch manager. He returned to academia to complete his PhD with a dissertation titled "On Access Network Selection Models and Mobility Support in Heterogeneous Wireless Networks." His postdoctoral work included research appointments at Columbia University in New York and as a JSPS Fellow at the National Institute of Information and Communications Technology in Tokyo. His research interests span cybersecurity, 5G/6G networks, Internet of Things, wireless network mobility, and explainable artificial intelligence. From 2017-2023, he led the Center for Distance-spanning Technology at LTU, focusing on fifth and sixth generation mobile networks, IoT, and data centers. His recent publications demonstrate a growing interest in applying machine learning to cybersecurity challenges and medical diagnostics. Professor Andersson's publication record shows a strategic evolution from wireless networking fundamentals to applied cybersecurity and machine learning solutions. His recent 2025 publications indicate active research in explainable AI frameworks, malicious URL detection, and medical applications of machine learning, suggesting an interdisciplinary approach that bridges cybersecurity with healthcare applications. Senior Member of IEEE Senior Member of ACM General Chair for IEEE Conference on Local Computer Networks (LCN) 2020-2021 Reviewer for numerous prestigious journals including IEEE Transactions on Mobile Computing and IEEE Transactions on Computers Member of editorial boards for Journal of Internet Services and Information Security and Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications As Dean for the Faculty of Science and Technology since January 2022, Professor Andersson oversees significant research funding and academic programs. His service as a reviewer for major conferences (ICC, Globecom, VTC, WCNC) and journals demonstrates his standing in the academic community. The Center for Distance-spanning Technology that he led from 2017-2023 likely secured substantial research grants in 5G/6G and IoT domains. Based in Campus Skellefteå at Forskargatan 1, Professor Andersson's work through the Department of Computer Science and Department of Systems and Space Engineering connects theoretical research with practical applications in next-generation networking and security. His leadership of the Center for Distance-spanning Technology positioned LTU as a significant player in 5G/6G research, while his recent publications suggest expanding collaborations with medical researchers applying AI to healthcare challenges.