جست‌وجوی استاد و پژوهشگر

Sohaib Ayaz Qaziمشاهده پروفایل

پژوهشگر

• Magnetic Resonance Imaging (MRI)
• Medical Image Processing
• Cardiovascular Imaging
• +۳ مورد دیگر

Sohaib Ayaz Qazi is a Principal Research Engineer at Linköping University, Sweden, affiliated with the Department of Health, Medicine and Caring Sciences (HMV) and the Center for Medical Image Science and Visualization (CMIV). His research focuses on accelerating MR turbulence mapping for cardiac and vascular imaging through advanced undersampling and k-space reconstruction techniques. Ph.D. in Medical Image Processing, COMSATS University Islamabad (2018) MS in Communication Electronics, Linköping University (2012) BS in Electrical Engineering, COMSATS-IIT (2009) His work bridges computational methods and clinical imaging, targeting rapid heart and vessel imaging. Recent publications highlight his expertise in dynamic MRI reconstruction using SVD and parallel architectures. Swiss Government Excellence Scholarship Two Student Support Program Awards from ESMRMB HEC IRSIP Fellowship at Linköping University At CMIV, he programs pulse sequences, develops scanner patches, and collaborates with colleagues like Petter Dyverfeldt, Tamara Bianchessi, and Twan Bakker.

Nikolaos Pappasمشاهده پروفایل

دانشیار

• Semantic Wireless Communications
• Age of Information
• Stochastic Modelling
• +۷ مورد دیگر

Nikolaos Pappas is an Associate Professor and Docent at Linköping University's Department of Computer and Information Science (IDA), within the Database and Information Techniques (ADIT) division. His research focuses on semantic wireless communications, age of information, stochastic modeling of communication networks, and wireless energy harvesting systems. He leads the Communications for Networked Intelligent Systems Group and serves as an editor for several IEEE journals, including the IEEE Transactions on Machine Learning in Communications and Networking. Education: B.Sc./M.Sc./Ph.D. in Computer Science and Mathematics from the University of Crete (2005–2012). Postdoctoral roles at Supélec (France) and Linköping University (2012–2016). Marie Curie Fellow (2014–2016). Research Interests: Includes semantic communication, tactile internet, IoT, queueing theory, and 6G network architectures. He is a voting member of the IEEE Tactile Internet Working Group and Principal Investigator for projects like 'Semantics-Empowered Communication for Networked Intelligent Systems' (Swedish Research Council, 2022–2025). Awards: Recognized in the Top 2% Scientists List (2020–2023) for Networking and Telecommunications. Leading Horizon Europe projects such as ROBUST-6G and ETHER, focusing on 6G security and hybrid terrestrial/satellite networks. Teaching: Courses on Age of Information, Statistical Learning, and Network Virtualization. Supervises a large team of PhD students and postdocs in ADIT.

Rikkert Frederixمشاهده پروفایل

مدرس ارشد

• High-Energy Particle Physics
• LHC Phenomenology
• Quantum Chromodynamics (QCD)
• +۳ مورد دیگر

Rikkert Frederix is a Senior Lecturer at Lund University's Department of Physics, Faculty of Science. His research focuses on high-energy particle physics phenomenology, particularly at the Large Hadron Collider (LHC). He specializes in improving theoretical predictions for LHC processes using advanced event generators like MadGraph5_aMC@NLO, aiming to resolve discrepancies between data and theory, enhance prediction accuracy, and explore beyond the Standard Model physics. Research interests include precision calculations of top quark pair production, electroweak corrections, and Monte Carlo methods for event generation. His work addresses algorithmic challenges such as negative weight reduction in NLO matching and optimization for high-luminosity LHC conditions. Publications span topics like tt̄ production dynamics, Z-boson angular coefficients, and software frameworks for collider physics. Collaborations emphasize interdisciplinary approaches between theory and experimental data analysis.

Flavius Gruianمشاهده پروفایل

مدرس ارشد

• Embedded Systems
• Real-Time Systems
• Hardware-Software Co-Design
• +۵ مورد دیگر

Flavius Gruian is a Senior Lecturer and Reader Project Manager at the Department of Computer Science, Lund University (LTH). He holds affiliations with ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the LTH Profile Area: AI and Digitalization. His research focuses on resource-constrained embedded systems, real-time scheduling, and energy-efficient hardware-software co-design, with applications in IoT, radar systems, and radio astronomy. Education and Academic Roles: No explicit education details provided, but his roles include senior lecturer, project manager, and principal investigator (PI) in multiple research projects. He advises PhD students (Boulasikis, Callanan, Waldemarson) and contributes to initiatives like GPAI (AI hardware for computing) and NordicIoT (Nordic IoT hub). Research Interests: His work spans embedded systems modeling, real-time algorithms, custom hardware architectures, Java in embedded systems, and machine learning for industrial applications. He explores energy-efficient designs and reliability in distributed embedded systems. Key Projects: Active projects include GPAI (2021–2026), TaLMiRRA (machine learning in radar/astronomy, 2019–2022), and NordicIoT (2018–2023). He also leads research on scalable actor networks and GPU programming for visual systems. Grants and Funding: PI roles in TaLMiRRA (STINT-funded) and NordicIoT (Nordic collaboration). Projects involve collaboration with international teams and industry partners. Labs/Teams: Affiliated with ELLIIT and the LTH AI profile area. Collaborates on hardware-software co-design and embedded systems optimization.

Jose Nunez-Yanezمشاهده پروفایل

استاد

• Hardware Architectures
• Heterogeneous Systems
• Machine Learning Hardware
• +۱۲ مورد دیگر

Jose Nunez-Yanez is a Professor in hardware architectures for Machine Learning at Linköping University, affiliated with the Department of Electrical Engineering and the Division of Electronics and Computer Engineering (ELDA). He has over 20 years of experience in high-performance embedded hardware design and is associated with the Wallenberg Autonomous Systems Program (WASP). His research focuses on energy-efficient, adaptive, and parallel hardware systems for machine learning and signal processing. Key areas include FPGA-based acceleration, run-time reconfiguration, and edge AI. He has led significant research projects with industry and academic partners such as ARM, ST Microelectronics, ESA, and Intel. The recent publications highlight a strong trend toward hardware-aware optimization of Graph Neural Networks for edge deployment, emphasizing quantization, dynamic range analysis, and heterogeneous computing. This reflects a broader shift toward efficient deep learning on resource-constrained platforms. Best Paper Award, Adaptive Hardware Systems (AHS) Conference, ESA/NASA, 2014 Best Paper Award, Adaptive Reconfigurable Computing (ARC) Conference, 2016 He has supervised 20 PhD students and served as the program director of the Advanced Microelectronics Systems Engineering (AMSE) program at the University of Bristol, significantly expanding its reach. He has secured substantial funding as Principal Investigator and Co-Investigator with partners including Sensata Systems, DSTL, Qioptiq, and Xilinx. His industry fellowships include Royal Society and Marie Curie awards. He is a member of the Royal Society Grants Board, the EPSRC College, and the Marie Curie Alumni Association. His work bridges academia and industry, with active consultancy in FPGA optimization and lossless data compression.

• Computer Science
• High Performance Computing
• Parallel Computing
• +۳ مورد دیگر

Mikael Runnar is a Lecturer at the Department of Computing Science at Umeå University, actively contributing to academic and research activities. He is affiliated with the High Performance Computing Center North (HPC2N) , focusing on computational infrastructure development and optimization. Research Interests : His work spans High-performance computing Parallel processing Distributed system architectures Scientific application optimization Computational resource management Education in computer science . Affiliations : Umeå University (Department of Computing Science) HPC2N (High Performance Computing Center North)

Paolo Bientinesiمشاهده پروفایل

استاد

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +۸ مورد دیگر

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

Roman Iakymchukمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Parallel Programming
• High Performance Computing
• +۳ مورد دیگر

Roman Iakymchuk is an Associate Professor at the Department of Computing Science within Umeå University, Sweden. His work focuses on numerically reliable and sustainable algorithmic solutions, emphasizing accuracy, reproducibility, and energy efficiency in high-performance computing environments. Research Interests: Numerical linear algebra, parallel programming models, sustainable computing, and optimization of distributed systems. Teaching: Courses on scientific computing, CUDA/OpenMP/MPI-based parallel programming, and floating-point error correction. The articles highlight his contributions to mixed-precision computing, numerical reliability in Krylov solvers, and energy-efficient practices in HPC systems. His work spans algorithm design, tool-assisted implementation, and European collaborative projects like CEEC.

Beata Megyesiمشاهده پروفایل

استاد

• Computational Linguistics
• Historical Cryptology
• Artificial Intelligence in Humanities
• +۳ مورد دیگر

Beata Megyesi is a Professor of Computational Linguistics at Stockholm University , leading groundbreaking work in Historical Cryptology and Digital Humanities . Her research bridges Artificial Intelligence with Philology to decode secret historical documents through projects like DESCRYPT (2025-2032) and DECRYPT (2018-2024), funded by Riksbankens Jubileumsfond and Vetenskapsrådet . Current Chair of Swedish Research Council's Linguistics Review Group (2024-2025) Director of the international Master's Program in AI and Language Advisor to PhD candidates Micaella Bruton and Crina Tudor Research Focus includes: Automatic analysis of 17th-19th century ciphers with AI Development of Linked Open Data infrastructure for cryptology Decryption of papal and diplomatic correspondences from 1500-1965 Creation of HistCorp multilingual historical corpus collection Key Collaborations span institutions in Sweden, Norway, Spain, Germany, Hungary, and the USA , with notable projects like DECODE2LOD and Swe-CLARIN . Her lab has pioneered automated key extraction and neural network-based alignment of encrypted manuscripts to plaintext.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Statistics
• Machine Learning
• +۴ مورد دیگر

Mattias Villani is a Professor of Statistics at Stockholm University, where he has been employed since 2018. Previously, he was a professor at Linköping University and worked as a researcher at Sveriges Riksbank (the central bank of Sweden). His office is located in Room A 4626 at Albanovägen 12, Building 4, floor 6. Dr. Villani's research focuses on developing computationally efficient Bayesian methods for inference, prediction, and decision making with flexible probability models. His work spans multiple domains including econometrics, machine learning, and neuroimaging. He has made significant contributions to Markov Chain Monte Carlo methods, particularly in developing subsampling techniques to make Bayesian computation feasible for large datasets. His recent publications demonstrate a strong trend toward developing scalable Bayesian methods for big data applications. Villani's work on subsampling MCMC methods represents a significant advancement in computational statistics, allowing for efficient Bayesian inference with massive datasets. His research bridges theoretical statistics with practical applications across economics, neuroscience, and machine learning. Dr. Villani currently teaches the master's courses "Statistical Theory and Modeling" (7.5 credits) and "Bayesian Learning" (7.5 credits). Every other year, he also teaches the doctoral course "Advanced Bayesian Learning" (8 credits).

• Coefficient inverse problems (CIPs)
• Adaptive finite-element methods
• Globally convergent numerical methods
• +۵ مورد دیگر

Larisa Beilina is Full Professor at the Department of Applied Mathematics and Statistics, Chalmers University of Technology, Sweden. Her work sits at the intersection of numerical analysis, inverse problems, and computational electromagnetics, with recent extensions to biomedical modeling. Education and Career Milestones: PhD awarded in 2003 for pioneering an adaptive finite-element method for hyperbolic coefficient inverse problems. In 2008, co-developed—with Prof. M. V. Klibanov (UNC Charlotte)—a globally convergent numerical method for coefficient inverse problems. 2011: experimentally validated the globally convergent algorithm using blind field data from the US Army Research Laboratory’s Forward-Looking Radar. Research Interests Beilina’s research focuses on ill-posed and coefficient inverse problems governed by PDEs. She designs adaptive finite-element algorithms that deliver reliable reconstructions from limited, noisy data, and couples these with globally convergent optimization techniques to escape local minima. Application domains span microwave thermometry for hyperthermia monitoring , early-stage melanoma detection via RF/microwave imaging , subsurface imaging using ground-penetrating radar , and parameter identification in HIV dynamics models . Recent Publication Trends Over 2022-2024 her articles emphasize four themes: (1) stabilized and explicit finite-element schemes for Maxwell’s equations and coupled wave systems; (2) hybrid FEM/FDM domain-decomposition solvers for large-scale 3D electromagnetic inverse problems; (3) adaptive error control and a posteriori estimates ensuring quantitative accuracy in conductivity and permittivity reconstruction; and (4) biomedical applications —ranging from microwave melanoma detection to time-adaptive determination of drug efficacy in HIV infection models. Scientific Awards & Recognition Keynote speaker and session chair at multiple international conferences on inverse problems and computational electromagnetics. Serves on editorial boards and as guest editor for proceedings volumes such as Springer Proceedings in Mathematics & Statistics and Journal of Inverse and Ill-Posed Problems . Research Teams & Grants Leads a multidisciplinary group comprising graduate researchers and post-docs collaborating with partners including the US Army Research Laboratory, University of North Carolina at Charlotte, and medical institutions in Sweden. Funding sources encompass Swedish Research Council grants, EU Horizon programs, and industry contracts focused on microwave diagnostics and multiphase flow measurements. Laboratory & Infrastructure Heads the Computational Inverse Problems Laboratory at Chalmers, equipped with high-performance GPU clusters, anechoic chamber facilities for microwave experiments, and access to clinical data sets for melanoma and HIV projects.

Johan Wallمشاهده پروفایل

مدرس ارشد

• Mechanical Engineering
• Simulation-Driven Product Development
• Decision Support Systems
• +۴ مورد دیگر

Johan Wall is a Senior Lecturer and Head of the Department of Mechanical Engineering at Blekinge Institute of Technology (BTH). He received his PhD from BTH in 2007 in simulation-driven product development and maintains an active research and teaching career. His academic profile combines leadership in department administration with significant contributions to mechanical engineering research and education. Dr. Wall's research centers on digitalization and model-driven approaches, specifically focusing on capabilities, methods, and tools for decision support in product development. His work explores how support mechanisms can be integrated into innovation processes to radically change experimentation capabilities, generating data essential for well-informed decisions. His research trajectory shows an evolution from traditional mechanical engineering toward increasingly sophisticated digital and AI-integrated solutions for product development challenges. Analysis of his recent publications reveals a strong trend toward AI applications in product-service systems (PSS), with particular emphasis on value co-creation, predictive maintenance, and changeability assessment. His 2025 publication "AI-Powered Value Co-Creation" represents the cutting edge of his current research direction, building on earlier work in simulation-driven development and decision support systems. As an educator, Dr. Wall teaches analysis-focused courses including machine elements, modeling, simulation, and optimization at both basic and advanced levels. He is a dedicated "maker" who views physical prototyping as an essential pedagogical tool and maintains strong involvement with the Karlskrona Makerspace community. His 2023 publication "Creating interaction effects between parallel campus and online courses" demonstrates his innovative approach to engineering education, blending traditional and digital teaching methods. Dr. Wall collaborates extensively with colleagues including Tobias Larsson, Alessandro Bertoni, and Marco Bertoni, indicating strong research partnerships within BTH. His ongoing project MD3S+ (Model Driven Development and Decision Support) continues his long-standing research focus on model-driven approaches to engineering design and decision making.

• Computer Architecture
• High-Performance Computing
• Memory Systems
• +۳ مورد دیگر

Per Stenstrom is a Professor of Computer Engineering at Chalmers University of Technology, Sweden since 1995. His research focuses on computer architecture, particularly high-performance memory systems and energy-efficient computing. He has authored/co-authored four textbooks, over 200 publications, 20 patents, and supervised approximately 25 PhD students. Awarded ACM Fellow and IEEE Fellow. Member of Academia Europaea and the Royal Swedish Academy of Engineering Sciences. Co-founder of the HiPEAC Network of Excellence. Served as Editor or Associate Editor for journals like ACM Transactions on Architecture and Code Optimization and IEEE Transactions on Computers . Program Chair for major conferences including ISCA, HPCA, IPDPS, and ACM International Conference on Supercomputing. His research innovations include cache compression techniques, memory optimization strategies, and energy-aware resource management in multicore systems. Ongoing work emphasizes secure and scalable cache partitioning, hybrid memory systems, and defense mechanisms against microarchitectural attacks.

Peter Schulteمشاهده پروفایل

دانشیار

• Philosophy of Mind
• Philosophy of Cognitive Science
• Metaphysics
• +۷ مورد دیگر

Peter Schulte is an Associate Professor in the Department of Historical, Philosophical and Religious Studies at Umeå University. His primary research focuses on the philosophy of mind, philosophy of cognitive science, and metaphysics, with specializations in mental representation, perception, animal minds, grounding, truthmakers, and free will. Research Areas : Philosophy of Mind, Philosophy of Cognitive Science, Teleosemantics, Plant Cognition, AI Ethics Recent Publications (2024-2025) explore: Teleosemantic theories of mental content AI moral responsibility and blameworthiness Interdisciplinary perspectives on plant cognition Philosophical debates on biological function and distal content Descartes' metaphysical dualism

Artur Podobasمشاهده پروفایل

دانشیار

• Computer Architecture
• Embedded Systems
• Edge Computing
• +۳ مورد دیگر

Artur Podobas is an Associate Professor at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. He specializes in computer hardware engineering and embedded systems, with active involvement in research projects such as the Lego-inspired design framework for Edge AI systems. His teaching roles include course responsibilities for undergraduate and graduate courses in computer engineering, systems architecture, and embedded systems. Research Interests: His work focuses on portable and predictable performance in heterogeneous embedded systems, as evidenced by projects like PaPP (Portable and Predictable Performance) and ENCORE (ENabling technologies for a programmable many-CORE). These initiatives aim to improve scalability and efficiency in embedded manycore architectures. Advising & Grants: While specific student advisees are not listed, he oversees multiple degree projects in computer science, electrical engineering, and information technology at both undergraduate and graduate levels. His current project (2024–2026) on Edge AI systems highlights collaborative research efforts in accessible automated design frameworks. Labs/Teams: His research is tied to the PaPP and ENCORE initiatives, which likely involve interdisciplinary teams focused on embedded systems optimization and hardware/software co-design methodologies.