جست‌وجوی استاد و پژوهشگر

Tobias Neckelمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +۴ مورد دیگر

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

Thomas Ludwigمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +۳ مورد دیگر

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +۳ مورد دیگر

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

Benjamin Stammمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +۷ مورد دیگر

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Andrea Beckمشاهده پروفایل

استاد

• Numerical Methods
• Computational Fluid Dynamics (CFD)
• Fluid Mechanics
• +۷ مورد دیگر

Prof. Dr.-Ing. Andrea Beck is a faculty member and Managing Director of the Institute of Aerodynamics and Gas Dynamics (IAG) at the University of Stuttgart. She leads the Numerical Methods in Fluid Mechanics working group, focusing on high-precision numerical methods for supercomputers, particularly discontinuous Galerkin (DG) methods. Her research spans fluid mechanics, aeroacoustics, plasma physics, and multiphase flows, with applications in wind energy, helicopter systems, and environmental aerodynamics. Role: Professor and Managing Director, IAG Committees: Member of the DFG Review Board, Strategy Committee for National HPC, and steering committee of High Performance Center Stuttgart. Her research emphasizes high-order methods, turbulence modeling, and data-driven approaches. She teaches courses such as 'Numerical Methods in Fluid Mechanics' and 'CFD Programming Projects', and has developed open-source software like FLEXI and HOPR for high-performance computing. Recent articles highlight advancements in entropy-stable DG methods, turbulence simulation using graph neural networks, and multiphase flow modeling. Her work integrates machine learning with CFD to enhance simulation accuracy and efficiency.

Sandra Diaz Pierمشاهده پروفایل

پژوهشگر

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.

• Parallel File and Storage Systems
• Modular Supercomputing
• Resource Disaggregation and Virtualization
• +۶ مورد دیگر

Sarah Neuwirth is a tenured Professor for Computer Science at Johannes Gutenberg University Mainz (JGU) and a Visiting Researcher at the Jülich Supercomputing Centre. She manages JGU's High Performance Computing (HPC) division, coordinates regional/national HPC activities, and represents JGU in NHR, Gauss-Allianz, and HPC committees. Education : PhD (Dr. rer. nat.) in Computer Science (2018), Heidelberg University Diplom in Computer Science (2012), University of Mannheim Bachelor of Science in Computer Science (2010), University of Mannheim Research Interests : Parallel File and Storage Systems Modular Supercomputing (resource disaggregation/virtualization) Performance Engineering High Performance Computing Networking Reproducible Benchmarking Parallel I/O Publications Trends : Her work focuses on HPC performance modeling, parallel I/O optimization, modular supercomputing, network characterization, and reproducible benchmarks. Key themes include resource disaggregation, automated workflows, and data-intensive distributed applications. Scientific Awards : 2023 PRACE Ada Lovelace Award for HPC ZONTA Science Award 2019 Grants & Leadership : She leads the High Performance Computing division at JGU, participated in European DEEP projects, and serves on SC conference committees.

• High-Performance Computing (HPC)
• Computational Fluid Dynamics
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.

Timo Hönigمشاهده پروفایل

استاد

• Operating Systems
• System Software
• Energy-Aware Computing Systems
• +۳ مورد دیگر

Prof. Timo Hönig is a Professor leading the Bochum Operating Systems and System Software (BOSS) Research Group at Ruhr-Universität Bochum (RUB). Previously, he served as an Assistant Professor at Friedrich-Alexander-University Erlangen-Nürnberg (FAU), where he was part of Department of Computer Science 4. His research focuses on Energy-Aware Computing Systems, Operating Systems, and System Software design with applications in embedded and real-time systems. Key research projects include the DFG Collaborative Research Center/TR 89 (Invasive Computing) and the DFG SPP 1914 (Latency- and Resilience-Aware Networking). He has received notable awards such as the SOSP SRC Gold Medal (2019) and the ISORC Best Paper Award (2017). He actively contributes to conferences like ACM EuroSys and USENIX ATC, and has led initiatives like the Albatross runtime system for energy-efficient HPC clusters. Teaching includes courses on Energy-Aware Computing and Operating Systems Technology. His work bridges theoretical system software design with practical applications in energy efficiency and heterogeneous architectures. The BOSS group explores future system software challenges for many-core and NVM-based systems.

Kristel Michielsenمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Annealing
• Quantum Simulation
• +۳ مورد دیگر

Prof. Dr. Kristel Michielsen is a Professor of Quantum Information Processing at RWTH Aachen University and holds leadership roles at Forschungszentrum Jülich. She leads the division HPC for Quantum Systems and heads the Jülich UNified Infrastructure for Quantum computing (JUNIQ). As Group Leader of the Research Group Quantum Information Processing, her work focuses on quantum annealing, quantum simulation, and modular hybrid HPC-quantum computing. She is also Spokesperson of the Helmholtz Information Program 1 and a Principal Investigator in Topics 1 and 2. Affiliations: RWTH Aachen University, Forschungszentrum Jülich (IAS/JSC), JUNIQ Key Roles: Head of HPC for Quantum Systems, JUNIQ Director, Quantum Information Processing Group Leader Her research emphasizes quantum computing applications in optimization, benchmarking, and hybrid supercomputing-quantum systems. She pioneers quantum-annealing solutions for real-world problems like power grid partitioning and transportation logistics. Recent work explores error mitigation, noise modeling in D-Wave systems, and quantum-classical workflows.

• Molecular Biomechanics
• Soft Matter Physics
• Artificial Intelligence
• +۵ مورد دیگر

Prof. Frauke Gräter is the newly appointed Director at the Max Planck Institute for Polymer Research (MPI-P), effective July 2024. She holds a professorship in Molecular Biomechanics at Heidelberg University and previously led the Molecular Biomechanics group at the Heidelberg Institute for Theoretical Studies (HITS). Her research focuses on mechanical forces in biochemical processes, combining AI/ML with experimental and computational methods. She earned her doctorate at the Max Planck Institute for Biophysical Chemistry and conducted postdoctoral work at Columbia University and the Max Planck Partner Institute in Shanghai. Education: Studied chemistry at Universities of Tübingen, Kyoto, and Heidelberg; PhD at MPI Göttingen; postdoctoral training at Columbia University and Shanghai's MPG-CAS Partner Institute. Research Interests: Molecular biomechanics, soft matter, AI-driven material design, and biomimetic systems. Specific projects include studies on blood coagulation, spider silk mechanics, and collagen structures. Her interdisciplinary methods integrate high-performance computing, molecular simulations, and AI to explore non-equilibrium material systems. Awards: PRACE Ada Lovelace Award for HPC ERC Consolidator Grant (European Research Council) Advising & Grants: Led the 'Protein Mechanics and Evolution' group in Shanghai and the HITS 'Molecular Biomechanics' team. Current focus includes collaborative projects at MPI-P to develop AI-predicted materials with bio-responsive properties. Labs/Teams: Heads the newly established Department Graeter at MPI-P, fostering interdisciplinary research with the Institute's five existing departments. Active in international collaborations to advance computational biology and smart material systems.

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +۱۲ مورد دیگر

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

Hannah Elfnerمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Heavy-Ion Collisions
• Quark-Gluon Plasma
• +۵ مورد دیگر

Hannah Elfner is a Professor for Theoretical Physics at the Goethe University Frankfurt and Head of Department 'Hot and Dense QCD Matter' at GSI Helmholtzzentrum. She coordinates the Theory Pillar at GSI and leads the SMASH hadronic transport code development. Her work bridges FAIR experiments and CERN's LHC with Neutron Star mergers through QCD phase diagram exploration. Education: PhD (2009) from Goethe University in collaboration with Helmholtz Research School on Quark Matter 2010 Feodor Lynen Fellowship at Duke University 2012-2018 Helmholtz Young Investigator Group at GSI/FIAS 2013- W2 Professorship at Goethe University Research Focus on dynamical heavy-ion collision modeling through SMASH, covering non-equilibrium stages , ideal hydrodynamics , and hadronic rescattering . Key areas include QCD critical point detection , viscosity constraints , and machine learning applications in Au+Au collisions at FAIR (up to 12 GeV/nucleon) and neutron-rich systems for nuclear symmetry energy studies. Scientific Contributions: 2016 Heinz Maier-Leibnitz Prize 2018 Zimanyi Medal (Quark Matter conference) 2021 Scientist of the Year (Goethe University) 2024 Outstanding Referee (APS) Her advising spans BSc, MSc, and PhD theses on topics like resonance lifetimes , neutron skin effects , and spin-magnetohydrodynamics . Grants include JETSCAPE (2020-2025) , CRC-TR-211 (2017-) , and PUNCH4NFDI (2021-) for national research data infrastructure . Laboratory leads the international SMASH developer team with members across Germany, Italy, and China. Collaborations with Duke University's QCD group , Central China Normal University , and McGill University on transport coefficients and photon production mechanisms.

• Scientific Software Development
• High Performance Computing
• Molecular Dynamics
• +۳ مورد دیگر

Dr. Torsten Stuehn serves as IT Group Leader at the Max Planck Institute for Polymer Research (MPI-P) in Mainz, Germany, leading scientific software development and HPC infrastructure since joining in 2003. He oversees the ESPResSo++ simulation package and collaborates with the University of Mainz and Max Planck Compute and Data Facility (MPCDF). Education: Diploma in Physics, University of Mainz, 1999 Doctorate in Physics, University of Mainz, 2005 His research focuses on scientific software engineering for exascale computing, developing neural network-based force fields, adaptive resolution methods, and load balancing algorithms to advance molecular simulation capabilities. This work addresses critical challenges in maintaining computational leadership for soft matter physics. Recent publications reveal a clear evolution in ESPResSo++ toward exascale readiness, integrating machine learning with multiscale modeling and parallel computing innovations. The software's progression reflects broader trends in computational physics where AI-driven methods and heterogeneous architecture optimization are becoming indispensable. Stuehn directs MPI-P's computational infrastructure team and contributes to major initiatives including Transregio SFB 146 and the European E-CAM project, driving open-source scientific software development for the global research community.