جست‌وجوی استاد و پژوهشگر

Zhonghai Luمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics (CFD)
• +۴ مورد دیگر

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +۵ مورد دیگر

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

Ahmed Hemaniمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Hardware Acceleration
• +۵ مورد دیگر

Professor Ahmed Hemani is a faculty member at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He holds the role of PI for the project 'New Chip Architectures for Industrial Vision' and leads research in reconfigurable computing, memristor-based systems, and hardware acceleration for AI and edge computing. His work bridges theoretical computer science with practical VLSI design and embedded systems development. He actively contributes to cross-disciplinary initiatives at Digital Futures, a joint center with Stockholm University and RISE Research Institutes of Sweden focused on digital innovation. His research emphasizes scalable FPGA/HPC architectures, low-power neuromorphic systems, and optimization techniques for custom silicon solutions. Current projects include a Lego-inspired edge AI framework and memristor-driven MIMO acceleration. Teaching responsibilities span advanced courses in SOC design, digital system verification, and embedded systems. He supervises advanced-level degree projects across computer engineering and ICT innovation specializations, emphasizing hands-on hardware-software co-design methodologies. Recent publications highlight innovations in memristor applications, FPGA-based acceleration, and reconfigurable architectures for neural networks and bioinformatics. His work addresses challenges in dark silicon utilization, energy-efficient computation, and high-performance embedded systems.

Kevin Kammمشاهده پروفایل

دانشیار

• Financial Mathematics
• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• +۸ مورد دیگر

Kevin Kamm is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research spans stochastic analysis , financial mathematics , commodities , and machine learning , with a focus on optimal strategies and SPDEs. He has developed innovative approaches using Deep Learning and stochastic Magnus expansion for financial models. Education : Mathematics studies at Technische Universität Berlin; Ph.D. in financial mathematics at the University of Bologna under the ABC-EU-XVA project. Research : Specializes in negative interest rate frameworks, rating triggers for XVA adjustments, and HPC applications in SPDEs. Collaborates on aquaculture valuation models incorporating biological and feeding cost risks. Publications : 15+ recent works cover CIR model extensions, XVA calibration, SPDE numerical methods, and machine learning applications in financial mathematics and commodities. Teaching : Instructs courses in Financial Mathematics and Stochastic Differential Equations , supervising Master's theses in areas like stress testing and mortgage-backed securities. Affiliations : Member of Umeå University's Mathematical Finance and Economics and Mathematical Modeling and Analysis research groups.

• Scientific Computing
• Computational Fluid Dynamics
• Finite Element Methods
• +۳ مورد دیگر

Johan Jansson is an Associate Professor in Scientific Computing at KTH Royal Institute of Technology and BCAM (Basque Center for Applied Mathematics). He leads research in predictive Direct FEM Simulation (DFS) for aerodynamics and multiphase flows, and co-founded Icarus Digital Math as CEO. His work includes the FEniCS open-source finite element software project and MOOC-HPFEM educational initiatives. He holds roles as Director of the Center for Digital Math and collaborates internationally in computational science. Research focuses on high-performance computing (HPC), fluid-structure interaction (FSI), biomedical modeling, and renewable energy systems. Notable contributions include adaptive FEM frameworks for turbulent flow, vocal fold simulations, and wave energy converter modeling. His work bridges academic research with industrial applications, leveraging FEniCS-HPC and Unicorn solvers. Key achievements include election to the IVA Royal Swedish Academy of Sciences 100-list and securing the Severo Ochoa Center of Excellence Award. He has pioneered open-source tools like SimTek and contributed to major projects like the Salter Sink and vocal production modeling. Teaching responsibilities include courses on database technology, computational fluid mechanics, and research methodology. He actively engages in large-scale simulation projects involving marine energy, cardiac ablation protocols, and aerodynamic optimization.

• High-Performance Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +۹ مورد دیگر

Niclas Jansson is a researcher at the PDC Center for High Performance Computing at KTH Royal Institute of Technology. He holds an M.S. in Computer Science (2008) and a Ph.D. in Numerical Analysis (2013) from KTH. His career spans roles such as postdoctoral researcher at RIKEN Advanced Institute for Computational Science (2013-2016) and visiting scientist at RIKEN (2018-2021), where he contributed to the Japanese exascale program Flagship 2020. A core focus of his research involves extreme-scale computing and numerical method development. He is a key developer of RIKEN's multiphysics framework CUBE , the HPC branch of FEniCS , and the spectral element flow solver Neko . His work is currently supported by a Swedish Research Council Starting Grant aimed at enhancing high-order spectral element methods for exascale fluid simulations. Niclas has published extensively on topics such as GPU acceleration , adaptive finite element methods , in situ visualization , and extreme-scale turbulence modeling . He also teaches Computational Fluid Dynamics (SG2212) at KTH.

Christoph Kesslerمشاهده پروفایل

استاد

• Parallel Programming
• Compiler Technology
• Optimization Algorithms
• +۳ مورد دیگر

Christoph Kessler is a Professor and Head of the Software and Systems (SAS) division at the Department of Computer and Information Science (IDA), Linköping University, Sweden. He leads the Programming Environment Laboratory’s research group focusing on compiler technology, parallel computing, and heterogeneous systems. His work includes the development of tools like OPTIMIST, PARAMAT, and SkePU, and he has contributed over 100 publications in journals and conferences. He holds a PhD from the University of Saarbrücken and a Habilitation from the University of Trier. Research interests span parallel programming, compiler optimization, and energy-efficient scheduling for heterogeneous systems. He has secured a 30M SEK grant from SSF for the ASTECC project, advancing adaptive software for edge-cloud computing. Notable contributions include frameworks for GPU-based systems and methodologies for optimizing resource allocation on many-core architectures. His team’s work emphasizes practical applications in high-performance computing, including tools for course management (StASy) and energy-aware scheduling algorithms. The SAS division, under his leadership, focuses on software engineering and computer systems research with strong industry collaboration.

David Black-Schafferمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Parallel Programming
• +۴ مورد دیگر

David Black-Schaffer is a Professor at Uppsala University's Department of Information Technology, specializing in computer systems research. As of 2023, he serves as Dean of Research for the Faculty of Science and Technology. His work bridges software and hardware innovations to enhance data movement efficiency in computer systems, with applications commercialized through a startup and integrated into industry standards like OpenCL. Black-Schaffer earned his PhD in Electrical Engineering from Stanford University in 2008, focusing on many-core processor programming. His career spans roles at Apple Inc. (contributing to OpenCL standards), postdoctoral research at Uppsala University, and academic progression from assistant to full professor (2010–2017). He has held leadership roles including Head of the Division of Computer Systems (2022) and department representative on the faculty Advisory Committee for Research (2021). His research spans computer architecture, memory systems, parallel programming, and simulation techniques. Recent publications (2024–2020) explore garbage collection, cache optimization, memory contention, NUMA systems, and instruction scheduling. Key trends include software-hardware co-design for power efficiency, reuse-aware data placement, and machine learning for performance modeling. Knut & Alice Wallenberg Foundation: Wallenberg Academy Fellowship Prolongation (2020–2025), Wallenberg Academy Fellow (2016–2021) Swedish Research Council (VR): Project Grant (2019–2024), Young Researcher Grant (2015–2018), Framework Grant (2012–2017) European Research Council: ERC Starting Grant (2017–2022) Teaching Awards: Uppsala Engineering and Science Student Union Pedagogical Prize (2012), Uppsala University Pedagogical Prize (2016), Uppsala Technical Physics Students' Teaching Award (2019) Other Grants: ScalableLearning flipped classroom project (2012–2020), Arm Ltd. collaborations on memory system designs He pioneered flipped-classroom teaching through the ScalableLearning project, impacting over 80,000 students. His research is conducted in collaboration with institutions like Arm Ltd., with past contributions to Apple's OpenCL implementation and UPMARC research center.

• Computational Fluid Dynamics
• High-Performance Computing
• Turbulence Simulation
• +۳ مورد دیگر

Martin Karp is a Research Fellow and postdoctoral researcher at KTH Royal Institute of Technology's Department of Engineering Mechanics, working under Dan Henningson. His research focuses on high-fidelity numerical simulations of turbulence and transition, with a specialization in high-performance computing (HPC) and supercomputing architectures. He holds a PhD in computer science from KTH and an MSc in Engineering Physics from Lund University, complemented by studies at ETH Zürich's computer science department. His research interests explore computational limits in nonlinear chaotic systems and future computational advancements. He leads the development of the Neko framework, a scalable simulation tool for extreme-scale CFD with extensive accelerator support. Karp's work emphasizes GPU and FPGA acceleration, parallel computing, and optimizing algorithms for heterogeneous architectures. Key contributions include large-scale turbulence simulations using GPUs, reducing communication in conjugate gradient methods, and evaluating FPGA-based flow solvers. His publications span journals like Concurrency and Computation and Scientific Reports , with conference presentations at IEEE Cluster, PASC, and HPCAsia. Karp's research bridges theoretical computational limits and practical HPC implementation, addressing challenges in precision, scalability, and hardware utilization. His educational background combines engineering physics with computer science, enabling interdisciplinary approaches to fluid dynamics and high-performance simulation. Current projects aim to push the boundaries of computational fluid dynamics through novel algorithm design and leveraging emerging hardware capabilities.

Seyedshahabaddin Mirjaliliمشاهده پروفایل

استادیار

• Fluid Mechanics
• Scientific Computing
• Machine Learning
• +۷ مورد دیگر

Seyedshahabaddin Mirjalili is an Assistant Professor in Fluid Mechanics at the Department of Engineering Mechanics, KTH Royal Institute of Technology, Sweden. He is affiliated with the Swedish e-Science Research Center (SeRC) and Digital Futures at KTH. Education: BS in Mechanical Engineering from Sharif University of Technology, MS and PhD in Mechanical Engineering from Stanford University. Former Positions: Research Associate (2022–2024) and Postdoctoral Fellow (2019–2022) at Stanford University. His research spans fluid mechanics, scientific computing, and machine learning, focusing on computational methods for multi-physics, multi-phase, and multi-scale flows. Applications include propulsion systems, additive manufacturing, biophysical systems, and environmental flows. He develops conservative phase field models, energy-preserving numerical schemes, and physics-informed machine learning frameworks for high-fidelity simulations and reduced-order modeling. Recent work involves inverse asymptotic treatments for discontinuities, microbubble dynamics in breaking waves, and energy-conserving momentum transport in two-phase flows. His methods emphasize boundedness, conservation properties, and reduced spurious currents in numerical simulations. Scientific Awards: Gallery of Fluid Motion Award (2018) from the American Physical Society Division of Fluid Dynamics He contributes to software initiatives under SeRC and collaborates on high-performance computing (HPC) applications. His teaching includes courses like Particle Dynamics (SG1115).

Evelyn Otero Solaمشاهده پروفایل

دانشیار

• Sustainable Aviation
• Air Traffic Management
• Emissions Reduction
• +۵ مورد دیگر

Dr. Evelyn Otero Sola is an Associate Professor in Aeronautical Engineering at KTH Royal Institute of Technology, serving as Vice-Director of the Centre for Sustainable Aviation (CSA) and a member of the Executive Board of the Environmentally Compatible Air Transport System (ECATS) International Association. Her research focuses on sustainable aviation, particularly optimizing air traffic management (ATM) to reduce emissions (CO2 and non-CO2) and noise, with projects involving sustainable aviation fuels (SAF), hydrogen, AI, and drones. She leads the doctoral course FSD3831 Future Sustainable Aviation and the master’s course SD2830 Aircraft Performance and Air Traffic Management , emphasizing interdisciplinary approaches to sustainability. Key collaborations include the Swedish Aerospace Research Center (SARC) and initiatives like TREVOL, TRACE, CIDER, and KLIMAFLY. Her work bridges research, education, and industry to drive sustainable aviation solutions globally. Education details are not explicitly provided, but her academic contributions include over 15 publications since 2009, spanning CFD optimization, climate modeling, and aviation environmental impact. She advises 11 doctoral students in the first iteration of her course and plans to expand access internationally. Her initiatives aim to create a skilled workforce for sustainable aviation through holistic education and cross-sector networks.

• Theoretical Physics
• Scanning Tunneling Microscopy (STM)
• Density Functional Theory (DFT)
• +۵ مورد دیگر

Alexander Gustafsson is a researcher affiliated with Linnaeus University, specifically the Faculty of Technology and the Department of Computer Science and Media Technology . His work bridges theoretical physics and computational methods, focusing on scanning tunneling microscopy (STM), density functional theory (DFT), and high-performance computing (HPC) applications. Research projects: Climate neutral Växjö, Alvesta 2030 (sustainability) and HPC for SME (data-driven AI/HPC for small/medium enterprises). Research Interests : Gustafsson specializes in STM imaging , inelastic tunneling spectroscopy , and quantum transport modeling . His theoretical work involves density functional theory and wave-function analysis to study surface phenomena and molecular interactions at the atomic scale. Publications Trends : His recent work emphasizes atomic-scale imaging , non-equilibrium scanning probe microscopy , and vibrationally assisted tunneling , with applications in material science and nanotechnology . He also contributes to electromagnetic simulations in engineering contexts. Education : Gustafsson earned a Doctoral Thesis (2017) and Licentiate Thesis (2015), both from Linnaeus University, focusing on theoretical modeling of scanning tunneling microscopy and non-equilibrium scanning probe microscopy .

Patrick Normanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Molecular Response Theory
• Computational Spectroscopy
• +۵ مورد دیگر

Patrick Norman is a Professor and Head of the Division of Theoretical Chemistry and Biology at KTH Royal Institute of Technology, Stockholm, Sweden. He also serves as Director of the PDC Center for High Performance Computing. His research focuses on advanced time-dependent molecular response theory methods, with applications in optical and X-ray spectroscopy. Coordinates VeloxChem and Gator HPC software projects Leads EU-EIC project OMICSENS (2023–2026) for lung cancer biosensors Principal Investigator in EU-ITN COSINE (2018–2021) and KAW network CoTXS (2014–2019) His theoretical work includes resonance convergent response theory via Liouville equations and Ehrenfest theorem, implemented in quantum chemistry software for linear/nonlinear spectroscopy. He co-authored the graduate textbook Principles and Practices of Molecular Properties (Wiley, 2018) and organizes international PhD schools on molecular response properties. Recent publications emphasize fragment state optimization, solvation effects, chiral luminescent materials, and amyloid ligand binding simulations. His work bridges quantum chemistry, HPC, and biomedical applications through tools like Gator and VeloxChem .

• Computational Algebra
• Dynamical Systems
• Artificial Intelligence
• +۷ مورد دیگر

Björn Lindenberg is a researcher at the Department of Mathematics, Faculty of Technology, Linnaeus University. His work bridges computational algebra, dynamical systems, and artificial intelligence, with a focus on reinforcement learning and mathematical modeling. Research interests: computational algebra, dynamical systems, AI, and reinforcement learning. Key projects: Algorithms for reinforcement learning, HPC for SMEs, optimization for sustainable energy supply, and reliability optimization. Teaching portfolio: Discrete mathematics, machine learning, mathematical modeling, cryptography, and database theory. His research explores reinforcement learning through tabula rasa strategies, applying dynamical systems to optimize AI convergence. Recent work includes predictive digital twins and high-performance computing applications. Publications highlight trends in reinforcement learning for AI, stabilization in dynamical systems, and optimization techniques for energy and rural development. Notable outputs include a 2023 doctoral thesis on reinforcement learning and dynamical systems, a 2022 AAAI conference paper, and a 2020 journal article on ensemble distributional reinforcement learning.