جست‌وجوی استاد و پژوهشگر

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.

Prashant Goswamiمشاهده پروفایل

دانشیار

• Computer Graphics
• Virtual Reality
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Prashant Goswami is an Associate Professor in the Department of Computer Science at Blekinge Institute of Technology (BTH), Sweden, within the Faculty of Computing. He also serves as Adjunct Faculty in the CSE Department at IIIT Delhi, India. B.Tech/M.Tech (IIT Delhi) PhD in Computer Graphics (University of Zurich) His research focuses on Computer Graphics , Virtual Reality , AI Integration , and Real-Time Simulations , with recent work bridging Immersive Analytics and Human-Centered Intelligent Systems . He has contributed to snow/ice animation, foveated rendering, and XR ethics. Recent publications highlight trends in GPU-Based Simulation , GAN-Driven Cloud Animation , and AI-Enriched XR Systems . As Guest Editor for the Journal of Imaging (MDPI), he is advancing immersive metaverse experiences. Previously, he worked at gameLab (NTU Singapore) and INRIA's MAVERICK group, focusing on geometric modeling and cloud simulation. His industry experience preceded his PhD at University of Zurich.

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.

Patrik Lundellمشاهده پروفایل

استاد

• Media History
• Political History
• Public Discourse
• +۳ مورد دیگر

Patrik Lundell is a Professor of History at Örebro University, specializing in media history and political history. He previously held professorships at Mid Sweden University (2016–2018) and earned his PhD in History of Sciences and Ideas from Lund University in 2002. His research focuses on the evolution of print media, including their marketing strategies and public discourse dynamics, with particular attention to parallels between historical media phenomena and modern social media. Lundell also explores right-wing extremist advocacy in interwar Europe and WWII, analyzing how intellectual elites engaged with Nazi Germany. He emphasizes historical perspectives to critically evaluate contemporary media environments. Key research areas include the transformation of media systems from the 18th century to present, the role of media in shaping public opinion, and the intersection of media with political movements. His work challenges simplistic narratives about media progress, advocating for nuanced historical analysis to understand modern media dynamics. Lundell has developed databases tracking textual migrations between Sweden and Finland, and his methodologies incorporate digital humanities tools for computational history analysis. His academic trajectory includes a Docent title from Lund University (2009), and his research has been disseminated through numerous articles, books, and collaborative projects. While no specific grants or awards are listed, his contributions to media historiography and critical analysis of far-right media rhetoric have positioned him as a leading scholar in these fields.

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.

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.

Robert Braunمشاهده پروفایل

دانشیار

• Distributed Simulation
• Parallel Computing
• Simulation-Based Optimization
• +۳ مورد دیگر

Robert Braun is an Associate Professor and Docent at Linköping University, affiliated with the Department of Management and Engineering (IEI) within the Fluid and Mechatronic Systems (FLUMES) division. His research focuses on distributed simulation methods, including co-simulation, parallel computing, and simulation-based optimization. He holds a doctoral degree from Linköping University, defended in 2015, and his employment is shared with SICS, East Swedish ICT, and SKF. He teaches undergraduate courses in mechatronics and hydraulics and is a core developer of Hopsan, an open-source simulation tool. Education: Doctoral thesis in 2015 on 'Distributed system simulation methods: For Model-Based Product Development'. Research Interests: Co-simulation tool coupling, parallel simulation optimization, and simulation-based teaching. Active in projects like OpenCPS (EU-funded) for model-driven development. Publications: Recent works address transmission line modeling in co-simulation, morphing wing actuator systems, and interoperability in aircraft system development. His research emphasizes numerical robustness and scalability in multi-domain simulations. Teaching & Labs: Develops Hopsan for fluid and mechatronic systems simulation. Involved in organizing the Scandinavian International Conference on Fluid Power (SICFP’25).

• Scientific Visualization
• Data Visualization
• Sonification
• +۳ مورد دیگر

Alexander Bock is an Assistant Professor at Linköping University's Media and Information Technology (MIT) department within the Department of Science and Technology (ITN). His primary research focuses on scientific visualization, sonification, and immersive technologies, particularly through the OpenSpace software framework. He leads efforts to contextualize complex scientific data (e.g., astrophysics, biology) for public engagement and education. Bock's team develops open-source tools like OpenSpace, which visualize planetary data and NASA missions, emphasizing STEM outreach. Key projects include Birdsongification (sonification for biology visualization) and Parallel Chords (multidimensional data analytics). He organizes major conferences such as Eurographics and Eurovis, which have attracted global participation. His work bridges academia and informal science institutions, enhancing science communication through interactive platforms. Research interests span audiovisual analytics, data-driven modeling, and interdisciplinary collaborations. Bock's publications address visualization techniques, plasma physics simulations, and software frameworks for space weather and molecular biology. His team's OpenSpace project has been adopted worldwide in planetariums and educational settings. Labs/Teams: OpenSpace Development Team, C-research (Visualization Center C collaboration).

• 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.

• Numerical Analysis
• High Performance Parallel Computing
• Algorithm Design
• +۵ مورد دیگر

Carl C Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University, Sweden. His research focuses on developing robust numerical algorithms that cannot fail, particularly in the context of finite precision arithmetic used by computers. His work bridges theoretical numerical analysis with practical high-performance computing applications. Dr. Mikkelsen's research interests center around numerical analysis and high performance parallel computing, with special emphasis on the worst case analysis of algorithms. He investigates the profound differences between exact arithmetic used by humans and finite precision arithmetic used by computers. His current projects include developing parallel constraint solvers for constrained molecular dynamics and parallel solvers for ultra-sparse linear systems. He is a co-author of the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems in parallel. His recent publications demonstrate a strong focus on accuracy requirements in numerical methods, particularly Newton's method, and the development of robust parallel algorithms for linear algebra problems. His work spans from theoretical analysis of numerical stability to practical implementation in high-performance computing environments. Dr. Mikkelsen teaches numerical analysis and scientific computing, emphasizing algorithm design, analysis, and implementation. He focuses on teaching students the critical differences between exact and finite precision arithmetic, and invests significant time in teaching software development practices and technical writing.