جست‌وجوی استاد و پژوهشگر

Alhassan Abdelhalimمشاهده پروفایل

پژوهشگر

• Foundation Models
• Machine Learning Optimization
• Edge AI
• +۷ مورد دیگر

Alhassan Abdelhalim is a Research Associate in the Distributed Operating Systems (DOS) Research Group at the Department of Informatics, University of Hamburg. He joined in December 2024 and is actively contributing to research in foundation models, machine learning optimization, and Edge AI. His work is aligned with real-world applications such as drone-based data collection in industrial environments through the InteGreatDrones project. Education: Bachelor of Mathematics and Computer Science, University of Aswan, Egypt Master of Intelligent Adaptive Systems, University of Hamburg, Germany His research interests center on Foundation Models , Machine Learning Optimization , Edge AI , and Pervasive AI , with a strong emphasis on explainability and application in humanities and industrial automation. During his Master’s, he worked in the Machine Learning Research Group focusing on Transformers and model interpretability. The recent publications (2020–2025) demonstrate a trajectory from nature-inspired optimization in data mining to advanced NLP tasks using LLMs. His work now focuses on detecting conceptual abstraction and automating violence categorization in ancient texts, showing interdisciplinary reach into digital humanities and computational linguistics. Scientific Contributions: Co-developed methods for detecting hypernymy in LLMs using attention analysis Proposed LLM-based pipeline for scalable violence analysis in historical literature Contributed to drone swarm middleware for industrial data capture He has no listed advisees or teaching roles. His research is supported by institutional affiliations and aligns with UN Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions). He collaborates with researchers such as Michaela Regneri and Sören Laue. Laboratories & Teams: He is a member of the Distributed Operating Systems (DOS) Research Group led by Prof. Dr. Janick Edinger and contributes to interdisciplinary projects involving machine learning and autonomous systems.

• Wireless Communications
• Satellite Communication Systems
• Network Security
• +۳ مورد دیگر

Dr. Gaofeng Pan is a prominent researcher in wireless communications, focusing on satellite-aerial-terrestrial integration, network security, and signal processing. His work addresses challenges in secure transmissions, energy harvesting, and 3D trajectory optimization for UAVs and LEO constellations. Recent research themes include: Secure Communication: Physical-layer security, covert transmission, and anti-eavesdropping strategies in hybrid networks. Network Optimization: Resource allocation, beamforming, and fairness in multi-UAV and multi-satellite systems. Signal Processing: Outage analysis, time synchronization, and robust algorithms for IoT and cognitive radio. Key collaborators include Jianping An , Shuai Wang , and Mohamed-Slim Alouini , with publications spanning IEEE Transactions on Aerospace and Electronic Systems , IEEE Internet of Things Journal , and IEEE Transactions on Wireless Communications .

• Theoretical Computer Science
• Software Verification
• Model Checking
• +۹ مورد دیگر

Francisco Javier Esparza Estaun (born 1964) is a Professor at the Technical University of Munich, holding the Chair for Foundations of Software Reliability and Theoretical Computer Science within the Department of Computer Science at the TUM School of Computation, Information and Technology. He has held academic positions at Edinburgh University (2001-2003) and the University of Stuttgart (2003-2007) prior to his current appointment at TUM since 2007. Prof. Esparza's research spans theoretical computer science with a focus on formal methods for software verification. His primary interests include algorithms and tools for the design and verification of reactive and distributed systems, verification of systems with infinitely many states, software model checking, program analysis, formal models for distributed systems (particularly Petri nets and process algebras), logic and automata theory, and analysis of probabilistic systems. His work applies mathematical techniques including logic, automata theory and complexity theory to develop methods for locating and eliminating errors in software systems or verifying their correctness. His research output shows consistent contributions to verification techniques, with recent publications focusing on parameterized verification, population protocols, and novel approaches to model checking. His work bridges theoretical foundations with practical verification tools, demonstrating how deep theoretical insights can lead to efficient verification algorithms. ERC Advanced Grant (2018) Honorary Doctor of Masaryk University, Brno, Czech Republic (2009) Member of Academia Europaea (2011) Prof. Esparza has supervised numerous PhD students who have gone on to successful careers in academia and industry. His current research projects include the Continuous Verification of Cyber-Physical Systems (ConVeY) funded by a DFG Research Training Group. Previously, he led the Parameterized Verification and Synthesis (PaVeS) project funded by an ERC Advanced Grant. His research group has developed several influential verification tools including Rabinizer (for LTL translation), Strix (for LTL synthesis), Peregrine (for population protocol verification), and Owl (for omega-automata). His laboratory focuses on developing theoretical foundations for software verification while creating practical tools that implement these theories. The group maintains active collaborations with researchers worldwide and contributes to major verification conferences and journals.

Michael Schlottke-Lakemperمشاهده پروفایل

استاد

• High-Performance Scientific Computing
• Adaptive Multi-Physics Simulations
• Research Software Engineering
• +۴ مورد دیگر

Dr. Michael Schlottke-Lakemper is a Professor of High-Performance Scientific Computing at the University of Augsburg, Faculty of Mathematics, Natural Sciences, and Materials Engineering. He previously held positions as an Interim Professor of Computational Mathematics at RWTH Aachen University (2022–2024) and led a research group at the High-Performance Computing Center Stuttgart (HLRS) from 2021 to 2024. His career includes postdoctoral roles at the University of Cologne and RWTH Aachen University/FZ Jülich. Education: Ph.D. in Mechanical Engineering, RWTH Aachen University (2017) Diplom in Aerospace Engineering, University of Stuttgart (2011) His research focuses on adaptive multi-physics simulations, research software engineering for high-performance computing (HPC), and scientific machine learning. Applications span fluid mechanics, aeroacoustics, and astrophysics, with recent work emphasizing robust high-order summation-by-parts methods and Julia-based computational frameworks like Trixi.jl and TrixiParticles.jl. His publications highlight advancements in discontinuous Galerkin methods, entropy stable schemes, and HPC optimization for compressible flows. Scientific contributions include Developing dynamic load balancing algorithms for multiphysics simulations Creating hybrid computational aeroacoustics methods Advancing Julia's adoption in HPC communities Improving error-based step size control in numerical solvers Current teaching activities include graduate seminars on Maschinelles Lernen in Theorie und Praxis and undergraduate courses in Numerische Lineare Algebra . He leads a research team at the University of Augsburg with collaborators across Germany, including Simon Candelaresi, Valentin Churavy, and Niklas Neher.

Yi Juمشاهده پروفایل

پژوهشگر

• In-Situ Techniques
• Performance Modelling
• Dynamic Resource Management
• +۳ مورد دیگر

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 Hartmannمشاهده پروفایل

استاد

• Operations Research
• Logistics
• Optimization Algorithms
• +۳ مورد دیگر

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
• +۷ مورد دیگر

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 Affeldtمشاهده پروفایل

استاد مهمان

• Formal Verification
• Coq Proof Assistant
• Monadic Equational Reasoning
• +۷ مورد دیگر

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 Meyerمشاهده پروفایل

استاد

• Algorithm Engineering
• Big Data
• High Performance Computing
• +۵ مورد دیگر

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 Tackmannمشاهده پروفایل

استاد

• Experimental Particle Physics
• High Energy Physics
• Higgs Boson Physics
• +۵ مورد دیگر

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. Muirمشاهده پروفایل

پژوهشگر ارشد

• Seismology
• Geophysics
• Inverse Problems
• +۳ مورد دیگر

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 Junghansمشاهده پروفایل

پژوهشگر

• Data mining in data streams
• Resource-adaptive data mining
• Spatio-temporal data analysis
• +۳ مورد دیگر

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 Neugebauerمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Mobile Systems
• Robotics
• +۴ مورد دیگر

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 Sobeمشاهده پروفایل

استاد

• Parallel and Distributed Systems
• Data Storage Technologies
• Reliability and Fault Tolerance
• +۳ مورد دیگر

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 Regnathمشاهده پروفایل

مربی

• IoT Security
• Blockchain Applications
• Embedded Systems Security
• +۳ مورد دیگر

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.