جست‌وجوی استاد و پژوهشگر

Hayden Liu Wengمشاهده پروفایل

پژوهشگر

• Numerical Linear Algebra
• Iterative Linear Solvers
• Preconditioning for large sparse systems
• +۸ مورد دیگر

Hayden Liu Weng is a Research Associate at the Chair of Scientific Computing in Computer Science (SCCS) at Technische Universität München (TUM), part of the TUM School of CIT and Department of Computer Science. He holds an M.Sc. in Computational Mechanics (with Honors from BGCE, TUM, 2019) and dual B.Sc. degrees in Mechanical Engineering and Mathematics from Universidad de los Andes (Colombia, 2015), with a minor in Education. He has served as the former Secretary of the M.Sc. Computational Science and Engineering Examination Board and is a doctoral student at the TUM Graduate Center CIT. His research focuses on numerical linear algebra, iterative solvers, preconditioning, machine learning applications, HPC performance engineering, and GPU computing. He actively supervises student projects in solver optimization, numerical frameworks, and high-performance computing. He has presented at major conferences like SIAM and organized student seminars at SCCS colloquia. Teaching responsibilities include leading courses like High-Performance Computing, Scientific Computing Lab, and Numerical Programming across multiple semesters. His work bridges algorithm development with practical HPC implementations, emphasizing mixed-precision techniques and solver selection strategies.

Christoph Pflaumمشاهده پروفایل

استاد

• simulation of airships
• computational optics
• high performance computing
• +۲ مورد دیگر

Prof. Christoph Pflaum holds the position of Professor of Computational Engineering at the University of Erlangen-Nürnberg's Department of Computer Science. His academic journey includes a PhD (1996) and Habilitation (1999) from Technical University Munich and University of Würzburg, respectively. His research focuses on computational optics, high-performance computing, numerical analysis, and multigrid methods, with applications in laser simulation and energy-efficient systems. Education : 1992: Diploma in Mathematics, Technical University Munich 1996: Dr. rer. nat., Technical University Munich 1999: Habilitation, University of Würzburg Research Interests : PDE solvers on sparse grids, laser dynamics (thermal lensing, solid-state amplifiers), computational fluid dynamics for airships, and optical system optimization. His work bridges theoretical numerical methods with industrial applications in photonics and renewable energy. Key Contributions : Developed simulation tools for high-power lasers and solar cells, including LASCAD and FDFD methods. Advances in multigrid algorithms and finite element analysis for complex optical systems. Teaching : Courses in theoretical computer science, scientific computing, and computational optics, emphasizing practical implementation using advanced numerical libraries and HPC. Grants & Projects : Led research on laser systems optimization (Innonet SOL), solar cell simulations (Photonics Europe), and high-performance computing initiatives (Par-EXPDE project).

Manish Kumar Mishraمشاهده پروفایل

پژوهشگر

• Energy Efficiency in High Performance Computing
• Dynamic Algorithm Selection
• Molecular Dynamics
• +۳ مورد دیگر

Manish Kumar Mishra is a Researcher at the Chair of Scientific Computing in Computer Science (SCCS) at the Technical University of Munich (TUM). He serves as a PhD candidate at TUM's Graduate Center CIT under Prof. Hans J. Bungartz and holds the position of Secretary for the M.Sc. Computational Science and Engineering Examination Board. Previously, he worked as a Design Engineer for cryogenic engines at the Indian Space Research Organisation (ISRO) and earned a B.Tech. in Aerospace Engineering from IISST and an M.Sc. (Honors) in Computational Science and Engineering from TUM. Research Focus : Energy-efficient High Performance Computing (HPC), dynamic algorithm selection for particle simulations, molecular dynamics, geometric numerical methods, and plasma simulation techniques. His work centers around the AutoPas particle simulation library, focusing on algorithm development, energy optimization, and applications in molecular dynamics and granular particle systems. Teaching : Leads exercise sessions and organizes exams for Scientific Computing II, supervises bachelor/master practical courses on molecular dynamics, and teaches specialized seminars like 'Methods in Molecular Dynamics' and 'Simulated Physics for Games.' Software Contributions : Core contributor to AutoPas, ls1, and mardyn projects. Active in developing HPC tools for particle simulations and optimizing energy efficiency in computational workflows. Advising : Supervises 2-3 theses annually, primarily in AutoPas-related topics like energy performance metrics, dynamic thread adaptation, and hardware-agnostic energy measurement. Recent advisees include studies on granular particle simulation and runtime-energy tradeoffs in molecular dynamics.

Manuel Geigerمشاهده پروفایل

پژوهشگر

• Quantum Compilers
• Quantum Algorithm Optimization
• Quantum Runtime Efficiency
• +۳ مورد دیگر

Manuel Geiger is a doctoral researcher at the Department of Computer Science, Technische Universität München (TUM), affiliated with the TUM School of CIT. His research focuses on quantum compilers, quantum algorithm optimization, and tensor network methods. He holds a dual M.Sc. and B.Sc. in Games Engineering from TUM (2019-2022 and 2016-2019). Education: B.Sc. and M.Sc. in Games Engineering (TUM) His work bridges quantum computing and high-performance computing (HPC), with publications addressing parallelism in heterogeneous quantum architectures and HPC-QC integration challenges. He collaborates on projects like SCIM MILQ, an HPC quantum scheduler, and has advised bachelor theses on tensor network optimizations. Teaching roles include assisting in Numerical Programming courses at TUM.

Marc Marot-Lassauzaieمشاهده پروفایل

پژوهشگر

• HPC Software Engineering
• Algorithm Efficiency
• Large-Scale Simulations
• +۲ مورد دیگر

Marc Marot-Lassauzaie is a Research Associate at the Chair of Scientific Computing in Computer Science (SCCS), Technical University of Munich (TUM). He holds an M.Sc. in Computational Science and Engineering (2020) and a B.Sc. in Engineering Sciences (2018), both from TUM. His research focuses on HPC software engineering, algorithm efficiency optimization, and large-scale simulations. He currently contributes to the Exahype software project and explores mixed-precision algorithmics for high-order methods like the ADER-DG algorithm. His work emphasizes improving computational performance and numerical stability in scientific simulations. Teaching activities include leading the Master-Praktikum on Scientific Computing and High-Performance Computing in Winter 2021. He has presented research at conferences such as PASC24, focusing on the impact of numerical precision in high-order methods. His technical expertise includes software development for large-scale simulations and collaboration on exascale computing initiatives.

Mario Willeمشاهده پروفایل

مدرس

• High Performance Computing
• Parallel Algorithms
• Parallelization of HPC Applications
• +۳ مورد دیگر

Mario Wille is a Lecturer at the Department of Computer Science within the TUM School of CIT at Technische Universität München. His research focuses on High Performance Computing (HPC), Parallel Algorithms, and GPU Programming. He is actively involved in projects such as ChEESE-2P (Exascale in Solid Earth), targetDART, and Invasive Computing. Wille teaches courses like Algorithms for Scientific Computing and Practical High-Performance Computing, and supervises student projects related to ExaHyPE engine development. His work includes advancing GPU offloading techniques and dynamic mesh adaptation for extreme-scale simulations. Office: Leibniz Supercomputing Centre, Boltzmannstr. 1, Room E.2.048 Projects: ChEESE-2P, SFB/TRR 89 Subproject A4 Research interests span computational seismology, parallelization strategies, and extreme-scale simulation methodologies. He has advised over 15 student theses since 2021, focusing on topics like Kokkos integration, seismic benchmarks, and volcano eruption modeling. Wille’s publications emphasize HPC algorithms and applications, with recent work presented at ISC High Performance 2023.

Gero Lückemeyerمشاهده پروفایل

استاد

• Business Process Management
• Augmented Reality/Virtual Reality
• Digitalization
• +۳ مورد دیگر

Professor Gero Lückemeyer serves as Dean of the Faculty of Surveying, Computer Science and Mathematics at HFT Stuttgart since 2024. Previously, he held the position from 2020–2024 and has been the Dean of Studies for the Bachelor of Computer Science since 2013. He leads the VirtuS Laboratory for Virtualized Systems, established in 2010, and holds a professorship in Business Informatics with a focus on Business Process Management since 2006. His expertise spans agile process management, AR/VR integration in digitalization, and innovative teaching/learning methodologies. Academically, he earned a PhD in Natural Sciences (Dr. rer. nat.) from the University of Cologne in 2000 and a Diploma in Business Information Systems in 1998. His professional background includes IT consulting and project leadership in telecommunications at sd&m AG (later CapGemini sd&m) from 2000–2006. Research interests include cybersecurity compliance (e.g., ISO/IEC 27001), digital teaching tools (e.g., Moodle integrations), and applications of machine learning in smart systems. His work emphasizes AR/VR for education and industry, as seen in projects like SensAR and DigiLab4U. He has received awards for research relevance (Cologne Prize, 2005) and educational excellence (Helmholtz-Gymnasium Award, 1995). Key contributions include developing the VirtuS Lab, leading the MoJEC and Moodle DTT tools for education, and pioneering COaaS frameworks for HPC. His articles focus on optimization using reinforcement learning, cybersecurity standards, and educational technology innovation.

Bernd Mohrمشاهده پروفایل

پژوهشگر

• High-Performance Computing (HPC)
• Parallel Programming
• HPC Performance Analysis
• +۴ مورد دیگر

Bernd Mohr is a senior scientist and Division Head of the 'Application Support' division at the Jülich Supercomputing Centre (JSC) , part of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich. He leads the Scalasca performance tools project in collaboration with Prof. Felix Wolf (TU Darmstadt) and oversees user support, performance analysis, and training at JSC. Previously, he served as deputy head of the division for 15 years and held a postdoctoral position at the University of Oregon, where he developed the foundational TAU performance analysis framework . Education: Diploma thesis and Ph.D. at the University of Erlangen (Germany), focusing on performance analysis tools for parallel computing. Research Expertise: Specializes in parallel programming, HPC performance optimization, and benchmarking. His work supports exascale computing initiatives, including leadership roles in the International Exascale Software Project (IESP/BDEC), European Exascale Software Initiative 2 (EESI2), and Jülich’s EIC and ECL projects. He has authored dozens of articles on parallel program performance analysis and tuning. Professional Involvement: Served on steering committees for SC and ISC conference series. Team leader of the 'Programming Environments and Performance Analysis' group from 2000 to 2024. Labs/Teams: Heads the ATML Parallel Performance team and contributes to the Scalasca collaboration. His work bridges tool development, user support, and large-scale computational challenges.

Alexandros Stamatakisمشاهده پروفایل

استاد

• Algorithms
• Parallel Computing
• Parallel Architectures
• +۳ مورد دیگر

Prof. Alexandros Stamatakis is a Professor of High Performance Computing at Karlsruhe Institute of Technology (KIT) and Group Leader of the 'Scientific Computing' group at the Heidelberg Institute for Theoretical Studies (HITS). He holds an adjunct professorship at the University of Arizona. His research focuses on algorithms, parallel computing architectures, and evolutionary bioinformatics. Stamatakis earned his PhD in Computer Science from TU Munich (2004) and has held roles including DFG Emmy-Noether Junior Research Group Leader (2008-2010). He is a member of multiple scientific advisory boards, including Elixir Greece and the Computational Biology Institute in Montpellier. His teaching awards (2015, 2016) highlight his contributions to bioinformatics education. His work spans computational methods for phylogenetic analysis, energy-efficient computing, and genomic data processing, with notable tools like RAxML-NG and EPA-ng. Research interests include parallel computing optimizations, phylogenetic inference algorithms, and applications in evolutionary biology. His recent work addresses challenges in large-scale genomic data analysis, including error detection in DNA barcoding systems and accelerating phylogenetic computations through algorithmic innovations. He leads collaborative projects on avian genome complexity and ancient DNA analysis, contributing to both computational methods and biological discoveries. Stamatakis collaborates internationally, with affiliations spanning institutions in Europe and the U.S. His lab at HITS emphasizes interdisciplinary approaches, merging computer science with biological problems. Key contributions include developing carbon-aware energy management frameworks and predictive models for phylogenetic inference stability.

Michael Lehnمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Numerical Mathematics
• Parallel Computing
• +۳ مورد دیگر

Michael Lehn is a Senior Academic Councillor at the Institute for Numerical Mathematics, University of Ulm, holding his position since 2008. He earned a Diplom in Business Mathematics from the University of Ulm (2002), a Master of Science in Applied Mathematics from the University of Southern California (2001), and a Dr. rer. nat. in Mathematics from the University of Ulm (2008). Education: 1995-2002: Studies at University of Ulm (Diplom) 1999-2001: Master of Science at University of Southern California (Los Angeles) 2002-2008: Doctorate at University of Ulm (Dr. rer. nat.) Research Interests: Focuses on high-performance computing (HPC), numerical methods, and software development for education. His work includes tools like ulm-on-ice (RISC architecture simulator), FLENS (numerical library), and ulmBLAS (high-performance BLAS implementation). He emphasizes practical applications of numerical mathematics through compiler design and educational software. Teaching: Taught advanced mathematics courses for physicists and computer scientists, as well as software basics in HPC (e.g., Summer 2014–2015). Courses included Numerics for Computer Scientists , Mathematical Foundations of Economics , and compiler development. Awards: 2015 Teaching Award (Lehrpreis) from the University of Ulm. Labs/Teams: Managed the Ulm Center for Scientific Computing (UZWR) from 2006–2008. Active in developing open-source tools for HPC education and research.

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Load Balancing
• +۴ مورد دیگر

Florina M. Ciorba is a Professor in the field of High-Performance Computing (HPC), affiliated with the University of Basel. Her research focuses on parallel algorithms, fault-tolerant systems, dynamic load balancing, and energy-efficient computing. She has contributed extensively to HPC workflows, scheduling techniques, and resilient numerical methods for extreme-scale simulations. Key research areas include: Parallel and distributed systems Load balancing and scheduling algorithms Fault tolerance mechanisms GPU and heterogeneous computing Energy-aware HPC applications Recent work emphasizes scalable particle simulations, application classification using ML, and optimizing astrophysics workflows. She collaborates with international teams on standards like SPEChpc 2021 benchmarks and HPC operational autonomy loops. Publications span top venues including IEEE Transactions on Parallel and Distributed Systems, Euro-Par, and Cluster conferences. Her work addresses both algorithmic innovations and software implementations for next-generation HPC platforms.

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +۴ مورد دیگر

George Bosilca is a Professor at the University of Tennessee, Knoxville, specializing in high-performance computing and parallel systems. With over two decades of research contributions, he has established himself as a leading expert in task-based runtime systems, distributed computing, and MPI implementations. His research focuses on developing and optimizing task-based runtime systems for extreme-scale computing environments, with particular emphasis on fault tolerance, performance optimization, and scalability. Bosilca's work spans multiple domains including scientific computing, climate modeling, and deep learning applications. He has made significant contributions to the PaRSEC runtime system and has extensively researched MPI optimization techniques for modern HPC architectures. The trend in Bosilca's recent publications demonstrates a strong focus on addressing challenges in exascale computing, including fault tolerance in distributed systems, GPU acceleration for scientific workloads, and energy-efficient computing techniques. His work bridges theoretical computer science with practical implementations for real-world scientific applications across various domains. Bosilca maintains extensive collaborations with leading researchers in the HPC community, most notably with Jack J. Dongarra (103 co-publications), Aurelien Bouteiller (60 co-publications), and Thomas Hérault (54 co-publications). These collaborations have resulted in numerous publications at top-tier conferences including SC, IPDPS, and EuroMPI, as well as in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems and the International Journal of High Performance Computing Applications.

• High Performance Computing
• Fault Tolerant Systems
• Quantum Computing Integration
• +۷ مورد دیگر

Thomas J. Naughton is a researcher affiliated with the University of Reading and Oak Ridge National Laboratory. He specializes in High Performance Computing (HPC), focusing on fault tolerance, quantum computing integration, and distributed systems. Research Interests: His work bridges HPC and quantum computing, develops fault-tolerant systems, and explores computational models through optical computing. Recent Publications: His 2026-2024 papers address quantum-HPC convergence software stacks, virtualization performance, and fault injection frameworks. Educational Contributions: He co-developed Bebras-inspired computational thinking resources for K-12 education, emphasizing task-based learning.

• High-Performance Computing
• Energy Efficiency
• Parallel Computing
• +۳ مورد دیگر

Ananta Tiwari is a researcher specializing in High-Performance Computing (HPC), energy efficiency, and parallel system optimization. His work focuses on optimizing HPC applications, workload management, and resource allocation strategies to enhance both performance and energy efficiency. Tiwari has collaborated extensively with institutions like the University of Maryland, UC San Diego, and Lawrence Livermore National Laboratory through his research activities. Education: PhD in Computer Science, University of Maryland, College Park (2011) Research Interests: Energy-efficient HPC systems Parallel application auto-tuning frameworks Workload characterization and extrapolation Node-sharing and resource pricing models ARM architecture optimization for HPC Key Contributions: Tiwari's research spans energy optimization techniques for large-scale MPI applications, colocation strategies for HPC workloads, and binary instrumentation tools for program analysis. His work on auto-tuning frameworks and multi-objective modeling with machine learning addresses critical challenges in balancing performance, power consumption, and scalability in modern HPC environments.

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

Henry M. Tufo is a Researcher affiliated with the University of Colorado, USA . His work spans High-Performance Computing (HPC) , Cloud Computing , and Computational Fluid Dynamics , with a focus on climate modeling, grid systems, and scalable algorithms. Tufo has collaborated extensively with institutions like IBM, Argonne National Laboratory, and researchers such as Paul Fischer, Kate Keahey, and Paul Marshall. His research interests include: Developing scalable HPC systems for climate and astrophysical simulations Integrating cloud computing with scientific workflows Optimizing spectral element methods for atmospheric models Trends in his publications highlight expertise in parallel computing , secure execution environments , and numerical methods for fluid dynamics. Tufo has contributed to frameworks like the FLASH code and GraphBLAS for large-scale simulations.