Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.
Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.
André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.
Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.
Florian Schmaus is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-University Erlangen-Nuremberg. He is associated with the Faculty of Engineering and focuses on system software for future many-core architectures, runtime support systems, and concurrent programming platforms. His work is part of the SFB/TRR 89 Invasive Computing project, specifically Project C1 (iRTSS). Education includes a Master's in Computer Science from FAU, with thesis topics such as 'Porting Cilk to OctoPOS' and 'Development of a Customizeable Caching Service in Haskell.' Research interests span distributed systems, operating systems, concurrency platforms, and real-time systems. His work emphasizes invasive computing, runtime systems for many-core architectures, and hardware-software co-design. Recent publications focus on systems like iRTSS, Nowa, and SHARQ, addressing challenges in resource arbitration and parallel processing. Teaching activities include courses on real-time systems and applied system software, with roles in both lectures and practical labs since 2012. He has supervised multiple theses on topics like microparallelism runtimes and kernel-space system calls. Labs/Teams: Active in the ergoo group and the Invasive Computing SFB/TRR 89 project, contributing to the OctoPOS operating system and related research platforms.
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.
Michael Bader is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), part of the TUM School of CIT. He leads the research group on hardware-aware algorithms and software for high-performance computing at the Leibniz Supercomputing Center. His work focuses on developing efficient algorithms and software for supercomputing platforms, particularly in geosciences and simulation of earthquakes and tsunamis. His research interests include high-performance computing, simulation software development (e.g., SeisSol and ExaHyPE), parallel numerical algorithms, adaptive mesh refinement, and large-scale geophysical simulations such as earthquake dynamics and tsunami modeling. He emphasizes optimizing algorithms for modern supercomputing architectures to handle complex computational challenges. Professor Bader has supervised numerous PhD students, including Lukas Krenz, Ravil Dorozhinskii, and Sebastian Wolf, among others. His research has been supported by grants from the EuroHPC JU, BMBF, DFG, and other institutions. Notable projects include ChEESE-2P for exascale computing in solid earth sciences and the targetDART project for adaptive task distribution on exascale systems. He is actively involved in teaching, offering courses such as Numerical Algorithms for High Performance Computing and Scientific Computing 1 . His group collaborates extensively with institutions like the Leibniz Supercomputing Center to advance computational methods for simulating natural disasters and geophysical phenomena.
Prof. Viola Priesemann is a physicist and neuroscientist at the Max Planck Institute for Dynamics and Self-organization and University of Göttingen (W3 Professorship, 20% position). Her research spans self-organization in adaptive networks across neural and societal scales, with applications to neural criticality , information processing , and COVID-19 pandemic dynamics . She leads an independent Max Planck Research Group since 2017 and has held affiliations with Caltech , MPI Brain Research , and Bernstein Center Göttingen . Her research combines statistical physics , information theory , and non-equilibrium systems to study: Self-organized criticality in brain networks Subsampling theory for large-scale systems Neural basis of curiosity and learning Test-trace-isolation strategies for epidemics Information spread in social networks Design of energy-efficient AI architectures Key scientific awards include: Communitas Award (Max Planck Society, 2021) Science award of Lower-Saxony (2021) Medaille für naturwissenschaftliche Publizistik (DPG, 2021) Young Scientist Award (DPG, 2024) Her work on cross-scale information flow has influenced both neuroscience and public health policy , including advisory roles in the German government's COVID-19 expert panel .
Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.
Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.
Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.
Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.
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.
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.
Prof. Marius Pesavento is a Full Professor at the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, leading the Communication Systems Group. His research focuses on sensor array processing, MIMO communication systems, adaptive beamforming, and mathematical optimization in networks. He has held academic and industry roles since 2001, including positions at mimoOn GmbH and FAG Industrial Services. Education: PhD (Doktorate) in Electrical Engineering, Ruhr-Universität Bochum (2001–2005) Master of Engineering, McMaster University (1999–2000) Dipl.-Ing. in Electrical Engineering, Ruhr-Universität Bochum (1992–1999) His research interests span robust high-resolution sensor array processing, 4G/5G mobile networks, and network information theory. Notable projects include developing tensor models for ultrasonic sensor calibration and applying machine learning to anomaly detection in network flows. His work bridges theoretical optimization and practical applications in automotive radar, 6G networks, and medical imaging. Labs/Teams: Leads the Communication Systems Group at TU Darmstadt, focusing on interdisciplinary projects in signal processing and communication systems.
