جست‌وجوی استاد و پژوهشگر

Jan Kronqvistمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Convex MINLP
• +۷ مورد دیگر

Jan Kronqvist is an Assistant Professor at KTH Royal Institute of Technology and a faculty member of Digital Futures, a cross-disciplinary research center focused on digital technologies. His research emphasizes optimization methodologies, including convex MINLP, machine learning integration, and algorithm design for complex systems. He actively contributes to projects like SMART (Predictive Maintenance), SimOPT (Process Design), ADAPCOS (Undersea Swarm Optimization), and cybersecurity in power systems. Key research areas include global optimization for clustering problems, robustness in neural networks, GPU-accelerated machine learning, and ensemble model optimization. He has developed algorithms like the Extended Cutting Plane and SHOT solver for convex MINLP, with applications in chemical engineering, power systems, and control systems. His work bridges theoretical optimization and practical industrial challenges, addressing topics such as cyberattack resilience, multimodal mobility systems, and ensemble learning verification. Projects often involve collaboration with industry partners to translate mathematical models into real-world solutions. Jan has advised initiatives like the SEC scholar program for Calvin Tsay and contributed to research on radiation therapy scheduling and distillation process optimization. His research outputs span over 40 peer-reviewed articles, focusing on advancing optimization algorithms and their applications across engineering and data science domains.

Ivy Pengمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Computing
• +۶ مورد دیگر

Ivy Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology’s School of Electrical Engineering and Computer Science (EECS), where she leads the Scalable Computing Laboratory (ScaLab). She also holds a Docent title in Parallel Computing and is affiliated with the Digital Futures Faculty and the Cooperate Working Group. Prior to Sweden, she worked at the Lawrence Livermore National Laboratory (LLNL) as a Computer Scientist and as a post-doctoral fellow at Oak Ridge National Laboratory (ORNL) in the USA. Her research focuses on large-scale parallel systems, system-level optimization through workload and architecture awareness, performance modeling and analysis, and memory subsystem optimization. Key areas include converged HPC and cloud computing, heterogeneous accelerators (e.g., GPUs, RISC-V Vector units, QPUs, DPUs), and heterogeneous memories (e.g., persistent memory, HBM). She also explores disaggregated resources to enhance computing efficiency and scalability. Ivy Peng coordinates the EU Horizon 2022 Project OpenCUBE, which develops open-source cloud-based services for EPI systems, and leads the Swedish Research Council (VR) Grant on Disaggregated Memory for Emerging Parallel Systems. She actively contributes to technical committees and review boards for journals like IEEE TPDS and conferences including SC, ICS, HPDC, and IPDPS. She advises Ph.D. students Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, and Daniel Medeiros, while co-supervising Jeremy Williams and Måns Andersson. Her academic leadership extends to multiple courses at KTH, including Applied GPU Programming, Introduction to High Performance Computing, and Quantum Computing for Computer Scientists.

• Computational Neuroscience
• Neural Network Modeling
• Locomotion Control
• +۳ مورد دیگر

Alexander Kozlov is a Researcher at KTH Royal Institute of Technology's Division of Computational Science and Technology. His work focuses on computational neuroscience, particularly modeling neural networks and locomotor control systems. He has contributed to understanding spinal cord networks, striatal connectivity, and neuromodulation mechanisms through in silico studies. Kozlov teaches courses in machine learning, artificial neural networks, and mathematical modeling of biological systems. His research integrates AI frameworks with biological data, emphasizing GPU-accelerated simulations and large-scale microcircuit analysis. Key contributions include studies on Parkinson's disease impact on neural connectivity and the role of sensory feedback in locomotion. He collaborates with the Science for Life Laboratory in Stockholm and maintains active roles in both peer-reviewed and conference publications. Education background and affiliations are not explicitly detailed in the provided text, but his extensive publication record reflects a deep engagement with interdisciplinary neuroscience and computational biology. Awards or grants are not listed here.

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Systems Optimization
• +۵ مورد دیگر

Bo Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology in Stockholm, leading the Scalable Computing Laboratory (ScaLab). Previously, he held roles as a Computer Scientist at Lawrence Livermore National Laboratory (LLNL) and a postdoctoral fellow at Oak Ridge National Laboratory (ORNL). He coordinates the EU Horizon 2022 Project OpenCUBE and leads a Swedish Research Council (VR) grant on Disaggregated Memory for Emerging Parallel Systems. His research focuses on optimizing large-scale parallel systems through architecture-aware design, performance modeling, and memory subsystem innovation for heterogeneous computing environments. He teaches courses such as Applied GPU Programming, High-Performance Computing, and Operating Systems. As an academic leader, he supervises multiple PhD students and co-supervises additional researchers. His service includes roles on technical program committees for top conferences like IEEE TPDS, SC, and ICS, and journals like IEEE Transactions on Parallel and Distributed Systems. Bo Peng’s work has been recognized with prestigious awards, including the R&D 100 Award in 2024. His research bridges HPC and cloud computing, emphasizing scalable systems and resource efficiency in next-generation architectures.

• Wireless Sensor Networks
• Industrial IoT
• Vehicular Ad-hoc Networks (VANETs)
• +۳ مورد دیگر

Tingting Zhang is a Professor at Mid Sweden University's Department of Computer and Electrical Engineering (DET), affiliated with the STC Research Centre. Her work focuses on wireless sensor networks, industrial IoT, and privacy-preserving protocols in vehicular and IoT networks. Research spans VANETs, machine learning for anomaly detection, light field imaging, and secure communication systems. Current projects include PLENOPTIMA, COINS, DAWN, and Smart Industry Sweden. Publications emphasize network reliability, security frameworks for IoT/VANETs, and machine learning applications in sensor networks. She has contributed to patents in wireless communication and video noise reduction. Recent work explores cross-layer optimization, privacy-preserving protocols, and GPU-accelerated light field rendering. Collaborations include institutions in Sweden, Finland, and China.

• Wave Energy Conversion
• Marine Technology
• Co-simulation Methods
• +۳ مورد دیگر

Xinyuan Shao is a Researcher at the Marine Technology department, Mechanics and Maritime Sciences , Chalmers University of Technology. Their work focuses on simulation frameworks for wave energy converters (WECs), particularly analyzing power performance, mooring fatigue, and hydrodynamic interactions in wave parks. Projects : ESOMOOR (2024–2027), INTERACT (2020–2024), WAVEMEASURE (2020–2024). Collaborators include Jonas Ringsberg, Huadong Yao, and the Swedish Energy Agency. Research Interests : Integrating power take-off systems into WEC simulations, co-simulation using FMI standards, and studying fatigue damage in mooring lines for floating offshore systems. They also explore near-wall atmospheric modeling for forests via GPU-accelerated lattice Boltzmann methods. Publications span journals like Ocean Engineering , Energy Conversion and Management , and Engineering Applications of Computational Fluid Mechanics . Their work addresses both technical and environmental challenges in marine energy systems.

Jonas Ungerمشاهده پروفایل

استاد

• Computer Graphics
• Image Processing
• Artificial Intelligence
• +۳ مورد دیگر

Jonas Unger is a Professor at Linköping University's Department of Engineering and Natural Sciences (ITN) and Media and Information Technology (MIT). As a research leader in computer graphics and image processing, he contributes to advanced visualization techniques, GPU computing, and AI applications in healthcare and climate adaptation. Specializes in BRDF modeling, spectral light field imaging, and augmented intelligence Active in AI4Climateadaptation and Wallenberg Autonomous Systems Program (WASP) Recipient of the 2023 Chester Carlson Research Prize Develops AI methods for medical imaging (SCAPIS dataset) and autonomous systems His recent work focuses on deep learning for material acquisition, real-time rendering optimizations, and visual analytics pipelines for disaster response systems. Current projects include multi-agent reinforcement learning for air traffic control and GPU-accelerated signal processing techniques. Scientific awards include: Chester Carlson Research Prize (2023) He collaborates with Visual Sweden's Augmented Intelligence platform, co-leads major visualization conferences, and contributes to Horizon 2020 Marie Skłodowska-Curie funded research. Contact: jonas.unger@liu.se

Marcus Larssonمشاهده پروفایل

استادیار

• Biomedical optics
• Microcirculation imaging
• Laser speckle contrast imaging
• +۵ مورد دیگر

Marcus Larsson is Assistant Professor and Head of the Department of Medical Technology at Linköping University, Sweden, where he also serves as Deputy Dean. His research develops optical methods—laser speckle contrast imaging, multispectral imaging and Monte-Carlo modelling—to quantify skin capillary perfusion and oxygenation, work that has continuous funding from national and EU sources and direct translation to cardiovascular risk assessment. Education: MSc in Applied Physics and Electrical Engineering, Linköping University, 1999 PhD in Biomedical Instrumentation, Linköping University, 2006 Marcus leads the Biomedical Optics group that designs real-time, non-invasive systems combining hardware advances with machine-learning algorithms. By merging spectroscopic and laser-Doppler data his team achieves comprehensive microcirculatory maps applicable to diabetes, sepsis and population-based cohorts such as SCAPIS. He is Director of Studies for the Biomedical Engineering programme, teaching five core courses from first-year design to specialised optics and modelling, and acts as assistant supervisor for doctoral student Andisheh Etminan. Collaborative projects involve senior colleagues Göran Salerud, Tomas Strömberg and Ingemar Fredriksson and are conducted within the Department of Medical Technology, a national centre for medical technology research and education founded in 1972.

Ehsan Miandjiمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

August Ernstssonمشاهده پروفایل

مدرس

• Parallel Programming
• Heterogeneous Computing Systems
• High-Performance Computing
• +۳ مورد دیگر

August Ernstsson is a Lecturer at Linköping University’s Department of Computer and Information Science (IDA). He is affiliated with the Graduate School in Computer Science (CUGS) and research groups Programming Environments (PELAB) and Software and Systems (SAS). His research focuses on parallel programming models, heterogeneous computing systems, and optimizing software for FPGA and GPU architectures. Notable contributions include frameworks for high development productivity (EXA2PRO) and deterministic parallel algorithms. Ernstsson’s work emphasizes performance portability, compiler design, and abstraction layers for heterogeneous systems. He collaborates with international teams on projects like pattern-based programming abstractions and FPGA-accelerated systems.

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.

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.

• Programming Languages
• Type Systems
• Compiler Optimization
• +۲ مورد دیگر

Lars Hummelgren is a doctoral student at the Royal Institute of Technology , affiliated with the Division of Software and Computer Systems. His research focuses on programming languages, type systems, and efficient compilation techniques for CPUs and GPUs. He is supervised by David Broman, with co-supervision from Philipp Haller and Matthias Becker. Lars serves as an assistant for courses including Compilers and Execution Environments (ID2202), Computer Technology and Components (IS1500), and Computer Technology, Basic Course (IS1200). Email: larshum@kth.se

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.

• 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.