Find Academic Supervisors

• Higher-order finite element methods
• Numerical linear algebra
• Multigrid methods
• +3 more

Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.

Estela SuarezView profile

Professor

• high performance computing (HPC)
• heterogeneous HPC system architectures
• modular supercomputing architecture (MSA)
• +4 more

Prof. Dr. Estela Suarez is a Professor of High Performance Computing at the Institute for Computer Science, University of Bonn (W2 in the Jülich Model) and Joint Lead of the Division "Novel System Architecture Design" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH. She also leads the Research Group "Next Generation Architectures and Prototypes" at JSC and serves as Spokesperson of Helmholtz Information Program 1, Topic 2. Currently on sabbatical during the 2024/2025 and 2025 academic years, she remains active in research leadership roles. 2010: PhD in Physics from University of Geneva, Switzerland 2004: Master in Physics, Specialization in Astrophysics, University Complutense of Madrid, Spain Professor Suarez specializes in high performance computing with particular expertise in heterogeneous HPC system architectures and modular supercomputing architecture (MSA). Her research spans hardware prototyping and evaluation, system software development, operational data analysis, and co-design methodologies. She has pioneered approaches to address hardware heterogeneity through system-wide orchestration of diverse computing resources, enabling more efficient scientific computing across multiple domains. Her work bridges theoretical computer architecture with practical implementation challenges in exascale computing environments, focusing on real-world applications that require specialized hardware configurations. Professor Suarez's publication record shows a clear evolution from foundational work on the DEEP project (2016) through the development of modular supercomputing concepts (2019-2021) to current applications across diverse scientific domains (2022-2024). Her recent publications demonstrate how modular architectures can be effectively applied to climate modeling, neuroscience simulations, quantum chemistry calculations, and other computationally intensive fields. This trend highlights her focus on practical implementation challenges and the growing importance of adaptable computing architectures in modern scientific research. 2023/2024 Lehrpreis der Universität Bonn: UniBonn teaching award Professor Suarez has secured significant research funding through major projects including NUMERIQS (Projects A05, B02, and Z02), European Processor Initiative (EPI), DEEP-SEA (Software for Exascale Architectures), IFCES2 (optimization of simulation algorithms for exascale supercomputers), and AIDAS (virtual laboratory between Forschungszentrum Jülich and CEA on AI and data analytics). While currently not accepting new students due to sabbatical, she has previously mentored graduate students in high performance computing techniques and has delivered numerous invited lectures at international conferences. Professor Suarez leads the "Next Generation Architectures and Prototypes" research group at JSC and serves as Joint Lead of the "Novel System Architecture Design" division. She chairs the Research and Innovation Advisory Group (RIAG) from EuroHPC Joint Undertaking since 2024. Her work involves close collaboration with international research teams on advancing supercomputing architectures, including contributions to the University of Bonn's new HPC system "Marvin" which ranks on both the TOP500 and GREEN500 lists.

Christian PlesslView profile

Professor

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +3 more

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

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.

Muhammad Arslan AnsariView profile

Researcher

• Hybrid Quantum-Classical Computing
• Quantum Computing
• High Performance Computing (HPC)
• +3 more

Muhammad Arslan Ansari is a researcher at the Department of Computer Architecture and Parallel Systems, Technical University of Munich. He contributes to hybrid quantum-classical computing models and high-performance computing (HPC) integration. Research Focus: Hybrid Quantum-Classical Computing, HPC-QC Integration, Compilation Toolchain Teaching: Seminar leadership on quantum-HPC integration and performance modeling in SS2023-SS2025 Projects: Involved in EuroHPC initiatives like SEANERGYS, PlasmaPEPS, and MUNIQC-ATOMS Software: QMPI, LAIK, and other HPC toolchain developments His publications address quantum accelerator integration challenges and programming tool reviews. He participated in conferences like IEEE QCE 2023.

Thomas LudwigView profile

Professor

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +3 more

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.

Boris KausView profile

Professor

• Geodynamics
• Lithospheric Deformation
• Numerical Modeling
• +5 more

Boris Kaus is a Full Professor and Chair of Geophysics and Geodynamics at the Institute of Geosciences, Johannes Gutenberg University Mainz, Germany. His research focuses on understanding geological processes from grain scale to planetary scale using mathematical and numerical models. Funded by the German Research Foundation, European Research Council, and BMBF, his work spans lithospheric deformation, melt migration, fold-and-thrust belts, and high-performance computing applications in geosciences. His research interests center on geodynamic modeling of lithospheric processes, including subduction zones, mantle convection, and crustal deformation. Kaus develops novel numerical approaches to simulate complex geological phenomena, with emphasis on coupling between erosion, lithosphere dynamics, and mantle flow. His group creates specialized software for high-performance computing systems to tackle multi-scale geophysical problems. His scientific awards include the Paul Niggli Medal, EGU Arne Richter Award, multiple ERC grants (Starting, Proof-of-Concept, Consolidator), and the Carl Friedrich Gauss Lecturer honor. He has received recognition for editorial contributions including G-Cubed's Excellence in Refereeing award. ERC Consolidator Grant MAGMA (2018-2023) ERC Proof of Concept Grant SALTED (2016-2017) ERC Starting Grant MODEL (2010-2015) John von Neumann Excellence Project for HPC ETH Medal for Ph.D. thesis Kaus actively supervises graduate students and leads research projects funded by major European and German agencies. His group develops open-source software like GeophysicalModelGenerator.jl and maintains strong collaborations with international institutions including ETH Zürich and USC. Current projects focus on magma dynamics, lithospheric shear localization, and the development of advanced numerical methods for geodynamic simulations. The research group operates within the Geodynamics & Geophysics team at JGU Mainz, utilizing high-performance computing resources and collaborating with multiple European research initiatives including IMPRS and FORTHEM networks. Their laboratory specializes in numerical modeling of Earth systems with applications to tectonics, volcanology, and crustal evolution.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +3 more

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 StammView profile

Professor

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +7 more

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.

Wolfgang BlochingerView profile

Professor

• Cloud Computing
• High Performance Computing
• IT Security
• +3 more

Prof. Wolfgang Blochinger is a Professor in the Department of Computer Science at Reutlingen University, specializing in Services Computing and IT Security. He leads teaching programs in Wirtschaftsinformatik (Business Informatics) at both Bachelor and Master levels, focusing on foundational topics such as Programming Basics, Operating Systems, IT Security, Cloud Computing, and Big Data Technologies. His research emphasizes Cloud Computing and High Performance Computing, particularly in elasticity control, parallel processing, and cloud resource optimization. His research projects include developing elastic parallel systems for HPC applications, cloud migration strategies, and automated cloud service generation. Notable contributions involve frameworks like TASKWORK for elastic task parallelism and the Elasticity Description Language for cloud applications. He has published extensively on serverless computing, cost-efficient cloud resource utilization, and container-based isolation techniques. Prof. Blochinger collaborates with industry partners through the university's labs, including the AI-Reallabor AIDA and Cloud Lab. His work bridges academia and industry, addressing real-world challenges in distributed systems and cloud infrastructure. Current research trends focus on self-tuning cloud services and adaptive parallel algorithms for scalable computing environments.

Dominik HuberView profile

Researcher

• Dynamic Resource Management in High-Performance Computing (HPC)
• Parallel & Distributed Programming Models
• Hardware-aware Programming
• +6 more

Dominik Huber is a Ph.D. candidate and researcher at the Technical University of Munich , affiliated with the Chair of Computer Architecture & Parallel Systems . His work focuses on Dynamic Resource Management in High-Performance Computing (HPC) , with expertise in Parallel & Distributed Programming Models and Hardware-aware programming . He has actively contributed to teaching courses like Parallel Programming Systems and Advanced Computer Architecture . His research emphasizes adaptive resource allocation in hybrid HPC clusters, leveraging technologies such as MPI Sessions , PMIx , and frameworks like LAIK and XBraid . Recent projects include the DynRes software suite for dynamic resource management and collaborations on quantum-HPC integration. Huber has advised students on topics ranging from Dynamic Resource Management in Charm++ to CI Systems for HPC Software , and his publications address challenges in malleability, scheduling, and power-constrained environments. Current affiliations include participation in the SEANERGYS (EuroHPC) and PlasmaPEPS projects.

Heiner GiefersView profile

Professor

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +7 more

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 BeckView profile

Professor

• Numerical Methods
• Computational Fluid Dynamics (CFD)
• Fluid Mechanics
• +7 more

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 PierView profile

Researcher

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +3 more

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
• +6 more

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.