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Yi JuView profile

Researcher

• In-Situ Techniques
• Performance Modelling
• Dynamic Resource Management
• +3 more

Yi Ju is a Researcher and Ph.D. candidate at the Technical University of Munich, affiliated with the Department of Informatics (Informatik 10) and the Max Planck Computing and Data Facility (MPCDF) under Prof. Erwin Laure. He works within the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz, focusing on cutting-edge HPC research and teaching advanced computer architecture courses. His primary research areas include In-Situ Techniques for real-time data processing, Performance Modelling of computational workflows, and Dynamic Resource Management in heterogeneous systems. His work bridges theoretical modeling with practical implementation in GPU-accelerated environments, computational fluid dynamics, and quantum-HPC integration, emphasizing efficiency and scalability in exascale computing. Analysis of Yi Ju's publications reveals a consistent trajectory toward optimizing in-situ processing pipelines across diverse architectures. His research demonstrates increasing sophistication in resource allocation strategies, with recent work exploring MPI-session frameworks and GPU-specific optimizations. Emerging trends show growing emphasis on quantum computing integration and precision-aware algorithms for scientific simulations. Yi Ju actively contributes to major HPC initiatives: SEANERGYS (EuroHPC) for energy-efficient computing PlasmaPEPS for plasma physics simulations OpenCUBE for big data analytics DaREXA-F for exascale applications ScalNEXT for scalable computing frameworks He collaborates extensively with MPCDF on performance modeling and resource management solutions for large-scale scientific simulations, while also mentoring students through TUM's advanced computer architecture curriculum.

Sönke HartmannView profile

Professor

• Operations Research
• Logistics
• Optimization Algorithms
• +3 more

Sönke Hartmann is Professor for Operations Research and Logistics at the Hamburg School of Business Administration (HSBA) since 2007. His academic work focuses on optimization algorithms, resource-constrained project scheduling, and container terminal optimization. He teaches courses in Operations Research, Statistics, Mathematics, Computer Science Foundations, AI & Data Science, and Data Analysis. Professor Hartmann completed his Computer Science studies at the University of Kiel and earned his PhD in Economics with a specialization in Operations Research. His doctoral research laid the foundation for his subsequent work in mathematical optimization and scheduling problems. His research spans multiple areas within operations research and logistics. Dr. Hartmann has made significant contributions to resource-constrained project scheduling, developing algorithms that address complex resource allocation challenges. His work on container terminal optimization has practical applications in port logistics, focusing on improving efficiency through simulation and mathematical models. He has also explored applications of mathematical programming to solve logic puzzles and smartphone applications, demonstrating the versatility of optimization techniques across different domains. Professor Hartmann's publication record shows a clear evolution from theoretical scheduling algorithms to practical applications in container terminals. His recent work (2025) provides a comprehensive review of fifty years of research on resource-constrained project scheduling, while his earlier publications established foundational work in genetic algorithms for project scheduling. The journal distribution shows strong representation in top-tier operations research journals like European Journal of Operational Research. As an active member of the academic community, Professor Hartmann serves as a reviewer for more than 30 scientific journals, contributing his expertise to maintain scholarly standards in operations research and logistics publications. His research has practical implications for container terminal operations, with applications in scheduling equipment and manpower, optimizing container movements, and improving overall terminal efficiency through simulation models. This industry-focused research demonstrates the real-world impact of his academic work.

• Parallel and Distributed Systems
• High-Performance Computing
• Parallel Programming
• +7 more

Prof. Dr. Thomas Rauber is a Professor at the University of Bayreuth in the Faculty of Mathematics, Physics and Computer Science, where he leads the Chair of Applied Computer Science II – Parallel and Distributed Systems. His research spans several decades with continuous scholarly output, demonstrating significant contributions to parallel computing, high-performance systems, and energy-efficient computation. Rauber maintains strong collaborative relationships with researchers including Gudula Rünger and Matthias Korch, with whom he has co-authored numerous publications. His primary research interests include parallel and distributed systems, high-performance computing, task scheduling, energy efficiency in computing, scientific computing with focus on Runge-Kutta methods and ODE solvers, and performance modeling. Rauber's work has evolved from foundational parallel programming concepts to contemporary concerns about energy consumption in computing systems. His research addresses both theoretical aspects of parallel algorithms and practical implementation challenges on modern architectures. Rauber's publication record shows a clear trend toward energy-aware computing, with recent work focusing on the trade-offs between performance, energy consumption, and solution accuracy. His 2023-2025 publications demonstrate continued innovation in task scheduling, software-defined environments for cloud applications, and optimization of numerical methods for modern multicore processors. His textbook "Parallel Programming for Multicore and Cluster Systems" (now in its third edition) has become a standard reference in the field. While specific grant information isn't detailed in the provided text, Rauber's extensive publication record across multiple decades suggests sustained research funding. His work on projects like TGrid (Runtime environment for heterogeneous systems and grid systems) and investigations into task pools for dynamic load balancing indicates involvement in significant research initiatives. Rauber maintains an active research laboratory focused on parallel and distributed systems, with ongoing projects examining communicating multiprocessor tasks, runtime environments for heterogeneous systems, and self-adaptation techniques for time-step-based simulations on heterogeneous HPC systems. His research group continues to produce influential work at the intersection of theoretical computer science and practical high-performance computing applications.

Reynald AffeldtView profile

Visiting Professor

• Formal Verification
• Coq Proof Assistant
• Monadic Equational Reasoning
• +7 more

Reynald Affeldt is a Chief Senior Research Scientist at AIST (National Institute of Advanced Industrial Science and Technology, Japan) and a Visiting Professor at NAIST (Nara Institute of Science and Technology). His work focuses on formal verification of software using logic and the Coq proof-assistant, with notable contributions to monadic equational reasoning, probabilistic programming, and robotics applications. His research spans multiple domains, including Monadic effects and equational reasoning Probabilistic and nondeterministic programming Information theory and error-correcting codes 3D geometry for robotics Recent publications highlight his efforts in extending monadic reasoning with transformers and formalizing probabilistic semantics in Coq. He has contributed to various conferences, notably as a committee member for CPP and TyDe tracks, and as a program chair for APLAS-related events. His work includes applications to OCaml, imperative language semantics, and algorithm verification (e.g., n-queens, quicksort).

Ulrich MeyerView profile

Professor

• Algorithm Engineering
• Big Data
• High Performance Computing
• +5 more

Prof. Dr. Ulrich Meyer is a full Professor of Computer Science at Goethe University Frankfurt, where he joined as a full professor in 2007 at the age of 35. He is also a Fellow at the Frankfurt Institute for Advanced Studies (FIAS) since 2020 and serves as the spokesperson for the DFG priority program "Algorithms for Big Data" (SPP 1736), which spans over 15 sites across Germany, Austria, and Switzerland. Professor Meyer received his Ph.D. in computer science from Saarland University in 2002. Following his doctorate, he worked as a postdoc and later as a senior researcher (W2) at the Max-Planck Institute for Computer Science in Saarbrücken before joining Goethe University Frankfurt. His research focuses on the design and engineering of algorithms and data structures for very large data sets, with particular emphasis on parallelism (Shared-Memory, clusters, GPUs), memory hierarchies (caches, SSDs, hard disks), and energy efficiency. His work spans the spectrum from theoretical worst-case results and average-case analyses to experimental evaluation of heuristics. A significant portion of his research involves graph algorithms, where his group has achieved more than a thousand-fold speed-up over state-of-the-art methods through algorithms with better access patterns. His recent work has focused on memory-efficient graph exploration in dynamically changing data, approximate solutions, and efficient generation of randomized graph test data. Professor Meyer has received numerous prestigious awards including the 2019 ESA Test of Time Award for the Delta-Stepping Algorithm, Best Paper Awards at ESA 2019 for research on Fragile Computing and Graph Generation, the 2011 Award Germany Land of Ideas for Ecosort, records in the JouleSort Competition in 2009/10, and the 2003 Best Dissertation Award from the University of Saarland. As spokesperson for the DFG priority program SPP 1736 "Algorithms for Big Data," Professor Meyer coordinates research across multiple institutions. His Algorithm Engineering group is also part of the DFG Research Unit FOR 2971 "Algorithms, Dynamics, and Information Flow in Networks" and the LOEWE Priority Programme CMMS. His research has been supported by various grants that have enabled significant contributions to parallel and external-memory graph algorithms, large-scale network generation, and energy-efficient computing. Professor Meyer's research group forms part of the Computer Sciences and AI Systems division at FIAS. They collaborate extensively with researchers across Europe through the DFG priority program and other initiatives. The group is particularly known for their work on memory-efficient graph algorithms, large-scale network generation, and contributions to understanding the algorithmic foundations of big data processing.

Kerstin TackmannView profile

Professor

• Experimental Particle Physics
• High Energy Physics
• Higgs Boson Physics
• +5 more

Prof. Dr. Kerstin Tackmann is a Principal Investigator at Universität Hamburg and Leading Scientist at Deutsches Elektronen-Synchrotron (DESY). She actively participates in the ATLAS collaboration at CERN's Large Hadron Collider (LHC) and the Belle II collaboration at KEK in Japan. Her academic journey includes a doctorate from University of California, Berkeley (2008) and diploma from Technische Universität Dresden (2004). Co-supervisor for Jim Lacey Roger Naranjo Garcia Daniela Küchler Kurt Brendlinger Früd Braren Tina Ojeda Research Interests: Kerstin Tackmann's work focuses on Higgs boson properties , particularly its decays into two photons and four leptons. She also investigates B-meson decays for quark mixing analysis and top quark physics . Her technical contributions include photon and electron reconstruction , detector calibration , and data simulation improvements. Current projects analyze new physics beyond Standard Model through differential cross-sections and Yukawa coupling constraints . Scientific Trends: Recent publications show emphasis on top quark mass measurements , slepton/sneutrino searches , W-boson production , and D-meson yields . Methodologies involve ATLAS detector performance , missing transverse momentum reconstruction , and high-precision Higgs studies . Awards: Kerstin Tackmann received the ERC Starting Grant (2016-2021) Helmholtz Young Investigator Group Funding (2011-2016) Labs & Teams: She leads the ATLAS group at DESY and collaborates with the Belle II team at KEK. Her work integrates with CERN's LHC experiments and DESY's particle physics division .

Jack B. MuirView profile

Research Fellow

• Seismology
• Geophysics
• Inverse Problems
• +3 more

Jack B. Muir is a Marie Skłodowska-Curie Fellow at the University of Oxford's Department of Earth Sciences and Junior Research Fellow at Wolfson College. His research integrates advanced mathematics with seismology to address inverse problems in Earth imaging and hazard assessment. Education: PhD in Geophysics, Caltech Seismolab (2021) Research focuses on physics-informed neural networks for seismic wavefield simulation (TerraPINN project), nonparametric seismicity rate modeling using deep Gaussian processes, geologically-constrained tomography, and Bayesian methods for wavefield reconstruction. His work targets applications from near-surface structures to Earth's core, emphasizing machine learning acceleration and uncertainty quantification in inverse problems. Recent projects include Distributed Acoustic Sensing (DAS) optimization and seismic swarm analysis. Publication trends (2022-2025) reveal strong emphasis on machine learning integration (PINNs, Gaussian processes) with geophysical inverse problems, particularly for DAS data processing, deep Earth imaging, and probabilistic hazard assessment. Key themes include multi-scale analysis, instrument response calibration, and computational efficiency. Scientific Awards: Marie Skłodowska-Curie Fellowship John Monash Scholarship Junior Research Fellowship at Wolfson College, Oxford Grant-funded projects include TerraPINN for physics-based seismic hazard assessment and collaborations leveraging Caltech's Community Seismic Network. He actively develops open-source tools for core-mantle boundary modeling and DAS data processing. Labs and teams involve Oxford's Seismology group (Tarje Nissen-Meyer), Caltech (Zach Ross), Australian National University (Hrvoje Tkalčić), and JAMSTEC (Satoru Tanaka), with fieldwork utilizing ocean-bottom seismometers and urban sensor networks.

Conny JunghansView profile

Researcher

• Data mining in data streams
• Resource-adaptive data mining
• Spatio-temporal data analysis
• +3 more

Dr. Conny Junghans is a researcher in computer science with expertise in data mining, spatio-temporal analysis, and information security. She completed her diploma at Ilmenau University of Technology (2005) and earned her PhD at the University of California, Davis (2009). From 2009-2011, she worked at Ruprecht-Karls-University of Heidelberg in the Database Systems Research group under Prof. Dr. Michael Gertz. Education: Diploma in Computer Science, Ilmenau University of Technology (2005) PhD in Computer Science, University of California at Davis (2009) Her research focuses on data stream mining with adaptive resource management, spatial/sensor network anomaly detection, and data quality assurance. She has contributed to multilingual document similarity models and burst detection in stream engines. Recent publications highlight trends in quality-aware systems, obstacle handling in sensor networks, and adaptive spatio-temporal prediction. She has served on program committees for SSDBM and CIKM conferences and acted as an external reviewer for multiple journals and conferences.

Mario NeugebauerView profile

Professor

• Wireless Sensor Networks
• Mobile Systems
• Robotics
• +4 more

Prof. Dr.-Ing. Mario Neugebauer is a faculty member at the Faculty of Computer Science/Mathematics at Dresden University of Applied Sciences (HTW Dresden). His teaching areas span fundamental programming, database systems, mobile networks, and communication technologies. Research Focus: Off-road navigation, wearable systems for production/logistics, localization systems, and mobile sensor devices. Thesis Topics: Image analysis for bird conservation, shadow detection on wind turbines, bat monitoring, and mission planning for autonomous robots. Technologies: Rust, ROS2, Kotlin/Spring Boot, Python, Raspberry Pi, C/C++ for microcontrollers, and IEEE 802.15.4 protocols. Teaching Responsibilities: Courses include Programming I/II , Mobile Networks , and Business Informatics and Digitization for Informatik, Medieninformatik, and Umweltmonitoring programs.

Peter SobeView profile

Professor

• Parallel and Distributed Systems
• Data Storage Technologies
• Reliability and Fault Tolerance
• +3 more

Prof. Dr.-Ing. habil. Peter Sobe is a professor at the Faculty of Computer Science/Mathematics of Dresden University of Applied Sciences . His teaching focuses on foundational computer science and programming, with an emphasis on algorithms, data structures, and mathematical methods for technical applications. Research Areas : Parallel and Distributed Systems, Data Storage Technologies, Reliability and Fault Tolerance International Projects : OOP4Fun (object-oriented programming education), ERASMUS+ partnerships with Riga Technical University and Tallinn Technical University Workshops : Organizer of the Fault Tolerance Discussion Group workshops (2018, 2014) His academic work spans distributed storage systems, covering techniques like data deduplication, regenerating codes, and fault-aware protocols. Research trends include reliability modeling, hardware acceleration via FPGA, and performance optimization in parallel computing frameworks. Prof. Sobe is actively involved in the German Informatics Society's PARS (Parallel Algorithms, Computer Structures) and FERS (Fault-tolerant Systems) working groups. He leads teaching platforms and research collaborations across Europe via the Baltic Summer School (BaSoTI) and ERASMUS+ programs.

Emanuel RegnathView profile

Tutor

• IoT Security
• Blockchain Applications
• Embedded Systems Security
• +3 more

Emanuel Regnath is a Tutor at the Chair of Embedded Systems and Internet of Things under Prof. Dr. Sebastian Steinhorst at the Technische Universität München . His work spans research, teaching, and supervision of student theses. Research focuses on Secure Decentralization Methodologies for the Internet of Things Teaching includes courses like Software Architecture for Distributed Embedded Systems and IoT Security His research interests include: Consensus Protocols for Cyber-Physical Systems synchronization Blockchain applications in resource-constrained devices Lightweight Message Authentication using hash-based signatures Key publication trends span: Decentralized vehicle coordination (2019-2021) Blockchain verification for IoT (2015-2020) Security architectures for autonomous systems (2017-2021) Supervised student work includes: Tariro Chingosho: "Designing a Notation System for Consensus Algorithms" (2021) Donát Nagy: "Reputation Framework to Increase Reputation Accuracy" (2021) Markus Moskopp: "Implementation and Evaluation of Consensus Protocols for IoT Systems" (2021) ...and 12 other theses from 2015-2021.

Jan-Felix SchrapeView profile

Associate Professor

• Sociology of innovation
• Media sociology
• Technology sociology
• +6 more

Jan-Felix Schrape serves as Associate Professor at the Institute of Social Sciences, University of Stuttgart, specializing in the Department of Organizational and Innovation Sociology. He has held this position since 2022, following previous academic appointments including Visiting Professor at RWTH Aachen's Chair of Technology and Society (2021-2022) and Acting Chair of Sociology at the University of Hohenheim (2018-2019). His academic credentials include a doctorate in Sociology from the University of Regensburg (2010), preceded by a Master's degree in sociology, economic and social history, and cognitive science at the University of Freiburg. Prior to his academic career, Schrape gained industry experience as Marketing Manager at two IT companies in Freiburg and Basel from 2006-2010. Professor Schrape's research focuses on the sociology of innovation, media and technology, with particular expertise in organizational and economic sociology, digitalization research, and sociological theory. His work examines how digital technologies reshape social structures, with emphasis on platform economies, distributed innovation processes, and the sociological implications of artificial intelligence. His publications reveal consistent scholarly productivity through 2025, including the recent book "Technik und Gesellschaft. Eine kurze Einführung" published in Reclam's Universal-Bibliothek series. His extensive publication record demonstrates evolving research interests with current emphasis on distributed innovation processes, platform architectures, and AI's societal dimensions. Recent works include significant contributions to the "Handbook of Innovation: Perspectives from the Social Sciences" and analyses of Google's AI Overviews impact on information behavior. Professor Schrape actively participates in academic discourse, having organized the "Treiber der Transition?" ad-hoc group for the 42nd German Sociological Association Congress (2025) and contributing to the "Next Frontiers" future congress at the University of Stuttgart. He teaches courses including "Media Sociology: Media Evolution, Mass Media, Digital Media, and Reality" and "Organization and Innovation: Digitalization, Sustainability, Artificial Intelligence" during the Summer semester of 2025.

• Ethics of technology
• Political ethics
• AI ethics
• +5 more

Matthias Braun is Professor of Social Ethics at the Faculty of Protestant Theology at the University of Bonn, holding the Chair of (Social) Ethics since the winter semester of 2022/23. Previously, he was head of the junior research group "Ethics and Governance of New Technologies" at the Friedrich-Alexander University Erlangen-Nuremberg until 2022. His academic journey includes research positions at institutions in Marburg, Erlangen, Bergen, Maastricht, and Oxford. Braun's research focuses on the ethical and governance challenges of emerging technologies, particularly examining how new technologies change social forms of life and institutions. His work spans political ethics, AI ethics, digital twin technology, data governance, and theological ethics. Using empirical and analytical-hermeneutic methods, he assesses social values and norms to develop responsible approaches to technological development. His recent publications demonstrate a clear trajectory toward increasingly sophisticated examinations of technology's societal impacts, with a growing emphasis on practical implementation of ethical frameworks. The research shows deep engagement with both theoretical foundations and concrete applications, particularly in healthcare contexts where ethical considerations have immediate real-world consequences. European Research Council research award recipient Falling Walls Award in Social Science and Humanities Braun leads the ERC-funded project on the ethics of digital twins and collaborates extensively across disciplinary boundaries. His work with the Collaborative Research Center EmpkinS and the Transdisciplinary Research Center "Life & Health" demonstrates his commitment to bridging academic research with practical societal concerns. Through initiatives like the Lunch Series on "Ethics, AI, and Health" and the "SciCom with Kids" program, he actively engages with diverse audiences to foster public discourse on technology ethics.

Kevin-Martin AignerView profile

Researcher

• Optimization under Uncertainty
• Power Network Optimization
• Data-Driven Optimization
• +3 more

Kevin-Martin Aigner serves as an Academic Councillor (Akad. Rat) at the Department of Data Science within the Faculty of Science at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is affiliated with the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Liers, focusing on mathematical optimization methods for energy systems and power networks. His work bridges theoretical optimization with practical energy transition challenges. His research centers on optimization under uncertainty, with key interests in distributionally robust optimization, data-driven methods, and sector-coupled energy system modeling. He develops novel approaches for handling renewable energy uncertainties in power grids, solar feed-in variability, and multi-sector energy integration. His methodologies emphasize explainability in optimization processes and robust decision-making under complex uncertainty structures. Recent publications reveal consistent focus on power network optimization (2021-2025), with increasing emphasis on distributionally robust frameworks, explainable AI integration, and regional multi-sector energy modeling. His work demonstrates strong interdisciplinary connections between operations research, machine learning, and sustainable energy systems engineering. Scientific recognition includes: GOR Dissertation Award for "Data-driven Optimization under Uncertainty for Power Networks" Dr. Aigner actively contributes to major research initiatives including: CRC TRR 154: Mathematical Modeling, Simulation and Optimization using Gas Networks (2022-2026, DFG-funded) ESM-Regio: Multi-sector Coupled Energy System Modeling on Regional Level (2021-2024, BMWE-funded) Optimal Control of Electrical Distribution Networks with Uncertain Solar Feed-in (2018-2021) He participates in the Optimization under Uncertainty & Data Analysis (OUDA) research group and has organized academic events including the TRR 154 Summer School on Optimization, Uncertainty and AI (2024) and Women in Optimization 2024. His teaching includes Discrete Optimization I and specialized seminars on Mixed-Integer Nonlinear Optimization.

Arne SchmitzView profile

Researcher

• Radio Wave Propagation
• Computer Graphics
• Wireless Network Simulation
• +3 more

Dr. Arne Schmitz is a researcher at the Department of Computer Science, RWTH Aachen University, specializing in computational methods for wireless communications and computer graphics. His work bridges radio wave propagation physics with advanced visualization techniques, focusing on practical applications for mobile network planning and mobile device interfaces. His primary research domains include: Radio wave propagation modeling in urban environments GPU-accelerated beam/ray tracing algorithms Antenna pattern compression using spherical harmonics Real-time 3D visualization for resource-constrained mobile devices Ad-hoc multi-display systems for collaborative mobile applications Analysis of his publication timeline reveals an evolution from foundational radio propagation models (2006) toward increasingly sophisticated urban simulation frameworks (2011-2012), with consistent emphasis on computational efficiency. His most impactful contributions integrate computer graphics rendering techniques with telecommunications engineering, particularly in adapting beam tracing for radio wave simulation and developing novel mobile visualization paradigms that overcome hardware limitations through client-server architectures. Dr. Schmitz maintains a strong collaborative relationship with Professor Leif Kobbelt and colleagues at RWTH Aachen, with co-authorship appearing in 9 of his 10 documented publications. His research demonstrates consistent funding support through participation in IEEE and ACM conferences, though specific grant details are not disclosed in the available materials.