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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.

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

• Structural Graph Theory
• Combinatorics of Posets
• Geometric Intersection Graphs
• +4 more

Piotr Micek is a professor in the Theoretical Computer Science Department at the Faculty of Mathematics and Computer Science, Jagiellonian University , Kraków, Poland. He is an active researcher in combinatorics, particularly in structural graph theory and poset combinatorics, and maintains extensive international collaborations. University: Jagiellonian University School: Faculty of Mathematics and Computer Science Department: Theoretical Computer Science Department Email: firstname.lastname@gmail.com Office: Room 3151, Łojasiewicza Street 6, 30-348 Kraków Phone: +48 12 664 7594 Duty hours: Wednesdays 11:00–13:00 His main research interests include structural graph theory, combinatorics of partially ordered sets (posets), geometric intersection graphs, graph coloring and choosability, and the entropy compression method. He also works on approximation and on-line algorithms and combinatorial geometry. His work often bridges deep theoretical insights with algorithmic applications. The recent publications highlight a strong focus on graph structure and coloring problems. Key themes include product structure of planar graphs, poset dimension and its relation to height and planarity, weak coloring numbers, and adjacency labelling. His work frequently appears in top venues such as Journal of the ACM , Combinatorica , SIAM Journal on Discrete Mathematics , and SODA, indicating sustained high-impact contributions. Associate Editor, SIAM Journal on Discrete Mathematics (2025–) Former Associate Editor, Discrete Mathematics (2010–2022) Former Associate Editor, Discrete Mathematics & Theoretical Computer Science (2012–2020) He has supervised numerous students at all levels, including PhD candidates Jędrzej Hodor , Marcin Briański , and Michał T. Seweryn , and has led major research grants such as OPUS 24 and WEAVE-UNISONO funded by NCN. He has also been involved in significant trilateral projects with researchers from Belgium and Germany. His recent talks include tutorials on product structure theory and centered colorings, reflecting his leadership in these areas. He actively participates in the academic community, having served on program committees (e.g., SODA 2021, WG 2022) and organized workshops such as the Order & Geometry series. His research is supported by substantial funding, including over 900,000 PLN for current projects.

Sriramkrishnan MuralikrishnanView profile

Researcher

• Numerical Analysis
• High Performance Scientific Computing
• Plasma Physics
• +4 more

Sriramkrishnan Muralikrishnan is a Research Staff member at the Department of Mathematics and Education within the Jülich Supercomputing Center (JSC) at Forschungszentrum Jülich, Germany. His work focuses on developing advanced computational methods for high-performance scientific computing, particularly in plasma physics and related multi-physics applications. Dr. Muralikrishnan's research spans several key computational domains: Numerical Analysis and High-Order Methods High Performance Scientific Computing for Exascale Architectures Plasma Physics Simulations Fast Solvers and Preconditioners Parallel-in-Time Integration Techniques Performance Portable Programming His recent publications demonstrate a strong focus on particle-based computational methods, particularly Particle-in-Cell and Particle-in-Fourier techniques. His work consistently addresses challenges in energy conservation, scalability across architectures, and noise reduction in plasma simulations. A significant portion of his research involves developing performance-portable frameworks that can efficiently leverage modern supercomputing hardware from different vendors without code rewrites. Dr. Muralikrishnan is actively involved in open-source scientific software development: Lead developer of IPPL (a performance portable library for grids and particles) Developer of OPAL (an open-source particle accelerator library) His research has direct applications in plasma physics, fusion energy research, and advanced accelerator design, with a strong emphasis on making computational methods accessible through open-source development and advocating for diversity in scientific computing.

• Algorithms and Complexity
• Parameterized Algorithms
• Approximation Algorithms
• +7 more

Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact. His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms , approximation algorithms , combinatorial optimization , scheduling , and algorithmic game theory . His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics. The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms . These works explore foundational problems in vector bin packing , integer programming , graph algorithms , and kernelization , while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering . He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including: STACS 2023 ESA 2024 FOCS 2023 ICALP 2024 IJCAI 2019–2025 AAAI 2018 SWAT 2018 He has successfully supervised several PhD students to completion, including Matthias Kaul , Roland Vincze , and Alexander Göke , many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031) , Hamburg Quantum Computing (2024–2029) , and Kernelization for Big Data , indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11) , fostering a research environment focused on high-impact algorithmic research.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Blockchain Technology
• Internet of Things
• Cybersecurity
• +4 more

Syed Muhammad Danish is a researcher specializing in Blockchain Technology and its applications in Internet of Things (IoT) , Cybersecurity , and Smart Grids . His work focuses on integrating blockchain for security, data privacy, and efficient resource allocation in large-scale systems. Research Interests : Blockchain, IoT, Federated Learning, Electric Vehicles, Cybersecurity, Data Privacy Key Collaborations : Kaiwen Zhang, Hans-Arno Jacobsen, Aroosa Hameed, Ali Ranjha Publication Trends : Recent articles (2025) address privacy-preserving techniques in renewable energy forecasting and electric vehicle charging load optimization using blockchain and federated learning. 2024 studies explore blockchain-as-a-service architectures and energy credit management via distributed ledgers. 2023-2018 works focus on IoT security, blockchain middleware, and defense mechanisms against jamming attacks in LoRaWAN systems.

• Combinatorics
• Discrete Mathematics
• Positional Games
• +7 more

Dennis Clemens is a researcher in the Institute of Mathematics at Hamburg University of Technology, where he leads and contributes to research in discrete mathematics, particularly in the Chair of Discrete Mathematics. His work centers on combinatorial game theory, extremal combinatorics, and probabilistic methods in graph theory. Research Interests: His research spans positional games (Maker-Breaker, Waiter-Client, Walker-Breaker), Ramsey theory , random graphs , and graph packing . He investigates structural properties of games on random and perturbed graphs, size-Ramsey numbers, and extremal problems in hypergraphs. Publications Trends: His recent publications (2021–2024) show a strong focus on game-theoretic models over random structures, with frequent collaborations. Key themes include fast winning strategies, universality, connectivity, and Ramsey-type problems. Scientific Awards: No specific awards mentioned in the provided texts. Advising and Grants: While no explicit list of advisees or grants is given, his role as a research leader suggests involvement in mentoring students and securing research funding, likely supported by institutions such as the Simons Foundation (acknowledged in arXiv). Labs and Teams: He is affiliated with the Discrete Mathematics group at TUHH, contributing to collaborative research in combinatorics. He participates in academic networks such as the SIAM & GAMM Chapter Hamburg and workshops on Operator Theory, indicating active engagement in the mathematical community.

Marcel CampenView profile

Professor

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +4 more

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Michael ObersteinerView profile

Researcher

• Molecular Dynamics
• High-Performance Computing (HPC)
• Sparse Grids Methods
• +3 more

Dr. Michael Obersteiner is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computer Science and Information Technology and the Department of Computer Science. He is part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz. His research focuses on high-dimensional numerical methods, sparse grids, the combination technique, molecular dynamics, and machine learning applications. He has contributed to the development of scalable algorithms for exascale computing, including the DisCoTec and sparseSpACE frameworks. Education: Michael Obersteiner began his studies in Biochemistry in 2010 before switching to Computer Science in 2011. He completed his M.Sc. in Computer Science in 2016 and is currently pursuing his doctoral dissertation with a focus on high-dimensional numerics and the sparse grid combination technique. Research Interests: His work spans molecular dynamics simulations, high-performance computing (HPC), spatially adaptive sparse grid methods, and applications in uncertainty quantification, machine learning, and plasma physics. He has developed open-source software tools like DisCoTec (for exascale PDE simulations) and sparseSpACE (for adaptive sparse grid operations). Teaching: Obersteiner has been involved in teaching numerical programming, scientific computing, and discrete structures at TUM. His roles include leading exercises for courses like 'Algorithms for Scientific Computing' and 'Numerisches Programmieren.' Projects: He leads projects such as DisCoTec and sparseSpACE, which focus on scalable sparse grid techniques for high-dimensional problems. His research also includes applications in plasma turbulence modeling, hydrology simulations, and machine learning with density estimation. Grants and Collaborations: His work has been supported by projects like EXAHD (funded by DFG/SPPEXA). He collaborates with institutions like BGCE, CSE, and IGSSE. Open student projects are available in areas like adaptive quadrature, machine learning with sparse grids, and uncertainty quantification.

Kislaya RaviView profile

Researcher

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +2 more

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +7 more

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.

Bastian BohnView profile

Researcher

• Mathematics of Machine Learning
• Approximation Theory
• High-dimensional Discretizations
• +3 more

Bastian Bohn is a Researcher at the Institute for Numerical Simulation (University of Bonn). He focuses on Mathematics of Machine Learning , Approximation Theory , and High-dimensional Discretizations . His work bridges Sparse Grid Methods Numerical Analysis Scientific Computing with modern ML applications. His recent publication Algorithmic Mathematics in Machine Learning (2024) consolidates his work on algorithmic foundations for high-dimensional data analysis. Earlier studies investigate Deep Kernel Learning Explainable AI Dimensionality Reduction using sparse grid frameworks.

Mengyu ChuView profile

Assistant Professor

• Physics-Enhanced Deep Learning
• Fluid Dynamics Simulation
• Computer Graphics
• +3 more

Mengyu (Rachel) Chu is a tenure-track Assistant Professor at the School of Intelligence Science and Technology, Peking University, affiliated with the Visual Computing and Learning Lab. Previously, she was a Lise Meitner Postdoctoral Research Fellow at the Max Planck Institute for Informatics (2020–2022), and completed her Ph.D. in Computer Science at Technical University of Munich (2014–2020). She holds a B.Eng. in Software Engineering from Southeast University (2007–2011) and an M.Eng. in Computer Science from Zhejiang University (2011–2014). Her research focuses on Physics-Enhanced Deep Learning , combining deep learning with physical simulations to enhance fluid dynamics modeling, video synthesis, and neural network training. Key areas include fluid reconstruction, manipulation, synthesis, and temporally coherent video generation. Her work bridges physics-based principles with modern AI techniques for realistic simulations and generative models. Notable contributions include Physics Informed Neural Fields for sparse data reconstruction (SIGGRAPH 2022), Learning Meaningful Controls for Fluids (SIGGRAPH 2021), and TempoGAN for fluid flow super-resolution (SIGGRAPH 2018). Her research emphasizes real-world applications in graphics, visualization, and computational physics. She has been recognized with the Lise Meitner Postdoctoral Fellowship (2020) and Summa Cum Laude distinction for her Ph.D. (2020). Her work spans academic publications, patents, and open-source projects, with collaborations across institutions like TU Munich and Max Planck Institute.