جست‌وجوی استاد و پژوهشگر

Joachim Falkمشاهده پروفایل

پژوهشگر

• Electronic System Level Design
• Hardware-Software Co-Design
• Data-Flow Graphs
• +۶ مورد دیگر

Joachim Falk is a researcher at the Department of Hardware-Software Co-Design (Computer Science 12) at Friedrich Alexander University Erlangen-Nuremberg. With over 20 years of experience since joining the department in 2004, he specializes in electronic system level design, dataflow programming, and hardware-software co-design, contributing significantly to the field through publications, teaching, and research leadership. His educational background includes: Doctorate degree (Dr.-Ing.) in Computer Science from FAU (2014) Diploma degree in electrical engineering (data processing) from Georg-Simon-Ohm University of Applied Science Nuremberg (2002) Falk's research focuses on Electronic System Level Design, Hardware/Software Code Generation for Data-Flow Graphs, Compiler Optimizations, and Parallel Architectures. His work bridges theoretical computer science with practical embedded systems implementation, particularly through the SystemC framework and his contributions to the SysteMoC language. He actively explores energy-efficient computing approaches for dataflow networks and embedded systems, with recent work emphasizing self-powering networks, clock and power gating techniques, and multi-reader buffer implementations for heterogeneous architectures. His recent publication trends reveal a consistent focus on optimizing dataflow networks for energy efficiency and performance. Key themes include self-powering dataflow networks, innovative clock and power management techniques, buffer management strategies for heterogeneous many-core systems, and invasive computing approaches. His research demonstrates a strong connection between theoretical models and practical implementation challenges in embedded systems design. Dr. Falk teaches courses including 'Entwicklung interaktiver eingebetteter Systeme' (Development of Interactive Embedded Systems) for Winter Semester 2024/2025 and 'SystemC' for Summer Semester 2024, supervising multiple theses on security modeling at the electronic system level and sleep/wake-up strategies for hardware implementations of dataflow networks. His research is conducted within the framework of projects like SysteMoC (representation of computational models in SystemC) and SystemCoDesigner (design space exploration for embedded systems).

• Data Management
• Computer Systems
• Database Optimization
• +۳ مورد دیگر

Prof. Dr. Jana Giceva is a Professor for Database Systems at the TUM School of Computation, Information and Technology since 2020. Her research bridges database systems with modern computer architecture, focusing on hardware-aware data processing, operating system integration, and efficient execution of big data workloads. She previously held roles at Imperial College London, Microsoft Research, and Oracle Labs. Education: PhD in Computer Science from ETH Zurich (2017) Awards: ERC Starting Grant (2024), ETH Medal (2018), VMware Early Career Faculty Award (2019), Google PhD Fellowship (2014) Her work explores database/operating system co-design , chiplet-aware scheduling , and disaggregated systems programming , with publications covering query optimization, graph data structures, and hardware acceleration. Collaborations with institutions like Imperial College London and ETH Zurich highlight her cross-disciplinary impact. Key Research Themes: Hardware-Software Integration High-Performance Query Execution Asynchronous I/O Optimization Adaptive Runtime Systems

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI) . He has over 30 years of experience in research software development, with a focus on language technology , information extraction , and question answering systems . PhD in Computer Science (1994) and Venia Legendi in Computational Linguistics (2004) from Saarland University Visiting researcher at Stanford , CMU , and MIT His research spans computational linguistics , artificial intelligence , and their applications in defence , healthcare , and education . He leads projects like AtLaS (AI-based NLP for low-quality data in defence), PRECISE4Q (predictive modeling in stroke medicine), and iREAD (personalized reading apps). Recent publications focus on low-resource language retrieval , cross-lingual transfer , and graph-based reasoning in biomedical domains. He has participated in numerous program committees (ACL, AAAI, LREC) and contributed to advancements in multilingual question answering , knowledge graph reasoning , and medical text de-identification . He has collaborated with institutions like Fondazione Bruno Kessler , econob , and NICE Systems . Current tools developed with his team power semantic search services for Informationsdienst Wissenschaft and the Deutsche Allianz Meeresforschung portal.

• Graph Algorithms
• Network Analysis
• Scientific Workflow Scheduling
• +۳ مورد دیگر

Henning Meyerhenke is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He serves as Deputy Director of Teaching and Studies, focusing on graph algorithms , network analysis , and scientific workflow scheduling . His research spans complex systems modeling and parallel computing. Ranks: Professor Institution: Humboldt University of Berlin Research Interests include energy-efficient computing, heterogeneous systems, and scalable graph algorithms. Recent work explores network sparsification , workflow mapping , and graph robustness under edge deletions. His group also investigates climate science applications via climate networks . Publication Trends reflect advancements in parallel algorithms , distributed systems , and adaptive scheduling for scientific workflows. Key subfields include memory-aware computing , harmonic centrality , and edge sampling techniques. Student Advising : Michael Piechotta (PhD candidate, defended September 15, 2025)

Knut Reinertمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Genomics
• Proteomics
• +۳ مورد دیگر

Prof. Knut Reinert is a Professor of Algorithmic Bioinformatics at the Freie Universität Berlin (FU) and a Max Planck Fellow at the Max Planck Institute for Molecular Genetics (MPI). He leads the Algorithmic Bioinformatics (ABI) group at FU and the Efficient Algorithms for Omics Data group at MPI. His research focuses on developing mathematical models and algorithms for analyzing biomedical mass data, including sequence analysis, proteomics, and omics data. Reinert is a key contributor to the SeqAn C++ library, a widely used bioinformatics software tool, and has led initiatives such as the Intel® Parallel Computing Center to accelerate NGS data analysis. Education: Diploma in Computer Science (1994, Max Planck Institute for Computer Science & Universität des Saarlandes), PhD (1999, Dr. Ing., Universität des Saarlandes). Postdoctoral research at Celera Genomics (1999–2002). Research interests include algorithm engineering for genome comparison, high-throughput sequencing data analysis, and bioinformatics software development. His work emphasizes scalable solutions for omics data interpretation, with applications in metagenomics, proteomics, and computational biology. Key achievements include leadership in de.NBI (German Network for Bioinformatics Infrastructure), the development of tools like SeqAn, Lambda, Fiona, and Ganon, and collaborations with institutions like the Max Planck Society and international partners. Awards include Max Planck Fellow status and Intel® PCC leadership. Grants and collaborations: German-Turkish research initiatives, DFG-funded RNA research projects, and leadership in EU-funded projects like Predict-IV. His lab actively engages in teaching at undergraduate and graduate levels, emphasizing innovative methods like e-learning and inverted classrooms.

• Robotics
• Machine Learning
• Electrical Engineering
• +۳ مورد دیگر

Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.

• Cognitive Psychology
• Psychophysiology
• Computational Modeling
• +۳ مورد دیگر

Dr. Victor Mittelstädt is a Post-doc Researcher at the Chair of Biological Psychology, University of Tübingen, where he investigates cognitive control mechanisms using behavioral and computational approaches. Affiliated with the Faculty of Science, his research bridges cognitive psychology, psychophysiology, and decision neuroscience. Education: Ph.D. in Psychology, University of Freiburg (2018) M.Sc. in Psychology, RWTH Aachen University (2015) B.Sc. in Psychology, University of Ulm (2012) Research Focus: His work centers on cognitive architecture in conflict tasks, including: Dynamic adjustments in distractor suppression and proactive control Neurocognitive mechanisms of task switching and voluntary action Computational modeling of aging effects on conflict processing Cross-modal transfer of cognitive control Awards & Fellowships: Feodor Lynen Research Fellowship (Humboldt Foundation) DAAD Research Grant Scholarly Contributions: He serves as a reviewer for 30+ journals including Journal of Experimental Psychology , Cognition , and Psychological Research , and evaluates grants for the German Research Foundation (DFG). His publications employ advanced methodologies like diffusion modeling and EEG to dissect cognitive processes.

• Parallel Computer architectures
• High Performance Computing
• Multi-/Manycore architectures
• +۵ مورد دیگر

PD Dr. Josef Weidendorfer is a qualified private lecturer at Technische Universität München (TUM) and leads the Future Computing Group at the Leibniz Computing Centre (LRZ). He holds a dual affiliation with TUM's Department of Informatics, Chair of Computer Architecture and Parallel Systems (Prof. Schulz), and the Leibniz Rechenzentrum der Bayerischen Akademie der Wissenschaften. His work focuses on developing smooth migration strategies for future HPC systems and evaluating novel technologies to improve system-level and workload analysis tools. Weidendorfer's research interests encompass Parallel Computer Architectures, High Performance Computing, Multi-/Manycore architectures, GPGPU, Performance analysis and optimization, Cache Simulation, Virtual Machines, and dynamic code generation. He is particularly interested in strategies for improving computational efficiency across various hardware structures, including specialized accelerator hardware for HPC codes. He regularly organizes the UCHPC workshop (since 2010 with Euro-Par) about unconventional hardware for HPC computing and is co-organizer of the PSTI workshop series. His recent publications reveal a strong focus on HPC system optimization, with particular emphasis on load balancing techniques, cache partitioning, application malleability, and performance monitoring. The research trajectory shows increasing attention to practical implementation challenges in modern heterogeneous computing environments, especially regarding GPU utilization, resource partitioning under power constraints, and phase-aware system monitoring. Weidendorfer maintains the open-source tools Callgrind/KCachegrind for cache simulation and has supervised numerous student projects across bachelor's, master's, and guided research programs. He teaches courses including Virtualization Techniques, Parallel Programming Systems, and Advanced Computer Architecture, demonstrating strong commitment to both research and education in computer architecture and parallel systems. As principal investigator for multiple large-scale projects including EU Project SEANERGYS (2025-2028) and BMBF Project ScalNext (2022-2025), he leads significant research initiatives focused on future computing technologies and HPC system development.

• Formal Verification
• Software Engineering
• Computer-Aided Verification
• +۵ مورد دیگر

Zvonimir Rakamaric is a Professor in the Department of Computer Science at the University of Utah. His primary research focuses on formal methods, software verification, and numerical analysis, with an emphasis on ensuring correctness and reliability in software systems. He leads efforts in developing tools and techniques for verifying complex systems, including formal verification of floating-point arithmetic, compiler optimizations, and concurrent/distributed systems. Key Roles: Formal Methods Researcher, Software Verification Specialist, Floating-Point Arithmetic Expert Affiliations: University of Utah School of Computing Research interests span formal verification tools (e.g., SMACK, Dafny), static analysis, and numerical error analysis. His work addresses challenges in safety-critical systems, including embedded software, high-performance computing, and compiler optimizations. Collaborations include institutions like Sandia National Labs and Microsoft Research. Publications emphasize rigorous methods for analyzing floating-point precision, equivalence checking, and tool development for formal verification. His work bridges theoretical foundations with practical tool implementations, contributing to open-source verification frameworks widely adopted in academia and industry.

• Theoretical Computer Science
• Distributed Systems
• Algorithm Design
• +۳ مورد دیگر

Jan Henrik Röwekamp is a researcher at the Department of Computer Science, University of Hamburg. His work focuses on theoretical computer science with emphases on Petri nets, distributed systems, algorithm design, and computational geometry. He holds a Master's (2013) and Bachelor's (2011) degree from the same institution. His research includes distributed simulation of Petri nets via web-based stateless services, containerization strategies for Petri net simulations, and applying Petri nets to computer vision tasks like Euclidean distance approximation. He has contributed to modeling IoT/Edge Computing architectures using Petri nets and explored distributed execution frameworks for reference nets using virtual machines. Publications span international workshops such as PNSE'19, PNSE'18, and AWPN 2017. His work bridges theoretical foundations with practical implementations in distributed systems and software engineering.

Jonas Rabensteinمشاهده پروفایل

پژوهشگر

• Operating Systems
• Distributed Systems
• System Software
• +۳ مورد دیگر

Jonas Rabenstein is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) within the Technische Fakultät at Friedrich-Alexander-Universität Erlangen-Nürnberg. His work focuses on invasive computing, distributed systems, and system software for many-core architectures. He contributes to the SFB/TRR 89 Invasive Computing project, particularly in developing the Invasive Runtime Support System (iRTSS). His research emphasizes resource arbitration, real-time systems, and embedded architectures. Research Interests: Rabenstein explores challenges in operating systems for heterogeneous hardware, virtual shared memory for MPSoCs, and scalable system software for future many-core processors. His projects address coherence management, task scheduling, and hardware-software co-design to optimize resource utilization. Teaching and Advising: He teaches System Programming courses and has advised multiple theses, including studies on relocating loaders for OctoPOS, system call frameworks, and distributed TCP/IP stacks for manycore systems. His supervision spans Bachelor's and Master's projects, emphasizing practical implementation of distributed and real-time systems. Affiliations: As part of the Distributed Systems and Operating Systems group, he collaborates on projects like OctoPOS and contributes to conferences such as HiPEAC and IPDPSW. His work bridges theoretical system design with practical deployment on emerging architectures.

Jayesh Badwaikمشاهده پروفایل

پژوهشگر

• Computational Physics
• High Performance Computing
• Software Engineering
• +۴ مورد دیگر

Dr. Jayesh Badwaik is a Scientific Researcher at the Jülich Supercomputing Center (JSC) within Forschungszentrum Jülich, a leading interdisciplinary research center in Europe. His work is centered in the Accelerating Devices Lab, where he focuses on high-performance computing architectures and computational methods for scientific applications. Dr. Badwaik's research spans multiple disciplines at the intersection of computer science, physics, and mathematics. His primary research interests include: Computational Physics and numerical methods for scientific computing High Performance Computing (HPC) with focus on exascale systems GPU programming models and accelerated computing Software engineering for large-scale scientific applications Lattice Boltzmann Methods for fluid dynamics simulations Parallel numerical algorithms for conservation laws Analysis of Dr. Badwaik's publication record reveals a clear evolution from theoretical numerical methods toward practical implementation on cutting-edge computing architectures. His early work (2016-2020) focused on mathematical foundations of numerical schemes for conservation laws, while his recent publications (2023-2024) demonstrate increasing emphasis on exascale computing challenges. His contributions to the JUPITER benchmark suite represent significant work in evaluating next-generation supercomputing systems. A substantial portion of his research centers on scaling the Lattice Boltzmann Method to exascale platforms, addressing critical challenges in computational fluid dynamics at unprecedented scales. His expertise in GPU programming is evident from his practical overview of programming models, which provides valuable insights for the HPC community. Dr. Badwaik is actively involved in the Accelerating Devices Lab at JSC, contributing to Europe's high-performance computing ecosystem. His work bridges theoretical numerical analysis with practical implementation on advanced computing architectures, making significant contributions to scientific computing at scale. His research has implications for multiple scientific domains that rely on large-scale simulations, including climate modeling, materials science, and computational fluid dynamics.

Christian Heidornمشاهده پروفایل

پژوهشگر

• Deep Learning
• Hardware-Software Co-Design
• Neural Networks
• +۵ مورد دیگر

Christian Heidorn is a Researcher at the Chair of Computer Science 12 (Hardware-Software Co-Design) within the Department of Computer Science at Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany. He has held this position since 2018 and teaches "Fundamentals of Computer Engineering" regularly across multiple semesters. His educational background includes an M.Sc. in Medical Engineering (2015-2018) and a B.Sc. in Medical Engineering (2012-2016), both from FAU. Born in 1989 in Dachau, Germany, he maintains an office in Room 02.128 at Cauerstr. 11, Erlangen. Dr. Heidorn's research focuses on the Application of Deep Learning on Tightly Coupled Processor Arrays and Invasive Computing . His work bridges medical engineering with computer architecture, particularly emphasizing neural network deployment on specialized hardware. His research projects include OpTC and KISS Invasive Computing, which optimize neural networks for embedded systems and processor arrays. His recent publications demonstrate a strong trend toward efficient neural network deployment on embedded systems, with emphasis on automotive applications (AURIX microcontrollers), hardware-aware neural network pruning, and processor array optimization. His work spans both theoretical neural architecture search and practical implementations for real-world applications. His notable scientific contributions include: Development of the OpTC toolchain for neural network deployment on automotive microcontrollers Hardware-aware evolutionary filter pruning techniques for CNNs ALPACA: An accelerator chip design for nested loop programs Efficient mapping of CNNs onto tightly coupled processor arrays Dr. Heidorn has supervised numerous Master's and Bachelor's theses on topics ranging from neural network compression to robotic hand control using EMG data and processor array optimization. His advising reflects his dual expertise in medical engineering and computer architecture, with many projects focusing on practical embedded applications of deep learning. He is actively involved in research projects related to invasive computing and tightly coupled processor arrays, with a particular focus on making deep learning more accessible on resource-constrained devices for automotive and medical applications.

Torsten Klieمشاهده پروفایل

پژوهشگر

• Cyber-physical systems
• Autonomic Communication
• Policy-based management
• +۴ مورد دیگر

Torsten Klie serves as Managing Director and Senior Research Associate ( Akademischer Oberrat ) at the FAU Research Center Embedded Systems Initiative (FAU ESI) at Friedrich-Alexander University Erlangen-Nuremberg. He is affiliated with the Department of Computer Science, specifically the Chair of Computer Science 12 (Hardware-Software Co-Design). Dr. Klie received his PhD in Computer Science (Dr.-Ing.) from Technical University of Braunschweig in December 2008 under Prof. Dr.-Ing. Lars Wolf (1st Reader) and Prof. Dr. Stefan Fischer (2nd Reader). His academic journey began with a Diploma in Business Informatics from the same institution in January 2003, preceded by research positions at the Institute of Operating Systems and Computer Networks (2005-2009) and the L3S Research Center in Hanover (2003-2005). His research focuses on cyber-physical systems, autonomic communication, and policy-based management, with increasing emphasis on industrial applications under Industry 4.0 frameworks. His work spans from foundational network management systems to current explorations of sustainable smart industry through the Industrial Internet of Things. Dr. Klie has developed a distinctive research trajectory examining how embedded systems can enable smarter industrial processes through advanced communication architectures and autonomous management approaches. Analysis of his publications from 2009-2025 reveals a clear evolution from theoretical network management frameworks toward practical industrial applications. His recent work increasingly addresses sustainability in industrial contexts, embedded AI implementations, and open-source hardware approaches to cyber-physical systems. The recurring theme across his career is the integration of hardware and software systems to create more autonomous, efficient, and responsive embedded solutions for industrial applications. Dr. Klie teaches courses on cyber-physical systems at FAU and previously taught computer networks, distributed systems, and operating systems at Technical University of Braunschweig. His teaching portfolio includes multiple specialized seminars on advanced networking topics, real-time sensor networks, and security in distributed systems. He leads the ESI Application Center focused on Sustainable Smart Industry – The Industrial Internet of Things as a Model for Sustainable Industrial Value Creation as part of the Emerging Fields Initiative. His current research direction emphasizes how embedded systems can drive sustainable industrial transformation through smarter connectivity and autonomous decision-making capabilities.

Markus Fidlerمشاهده پروفایل

استاد

• Performance of Communication Systems
• Network Calculus
• Effective Bandwidths
• +۱۳ مورد دیگر

Prof. Dr.-Ing. Markus Fidler is a Professor of Communications Networks at the Institute of Communications Technology , affiliated with the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover since 2009. His academic journey began with a doctoral degree in Computer Engineering from RWTH Aachen University (2004), followed by post-doctoral fellowships at Institute Mittag-Leffler (2004), NTNU Trondheim (2005), and University of Toronto (2006). He led the Emmy Noether Research Group at Technische University of Darmstadt (2007-2008), where he also earned his habilitation (2008). His research interests span Network Calculus , Effective Bandwidths , Available Bandwidth Estimation , and Parallel Systems (e.g., Multi-path Protocols, Synchronization Constraints). He explores Future Internet architectures, Wireless Communication (including Cognitive Radio and Car-2-X systems), and Congestion Control for Cooperative ADAS and Platooning. His work integrates Machine Learning and Stochastic Modeling for network performance analysis. Recent publication trends emphasize Age-of-Information (AoI) in tandem queues, Statistical Bounds for parallel systems, and Granularity Trade-Offs in multi-server environments. He applies Min-plus Algebra to AoI modeling and investigates Hybrid Time-Event Triggered Systems for resource-efficient communication. His projects include ADINeMo (2024) on Deviation-of-Information for sensor sampling and VaMoS 2 (2024) on validated models for MapReduce scaling. Scientific Awards ERC Starting Grant (2012) Advising includes current doctoral students Sami Akin , Brenton Walker , and Mahsa Noroozi , alongside numerous past advisees now holding academic positions globally. He leads DFG-funded projects such as FeelMaTyC (2017-2020) and GRK SocialCars (2014-2023), focusing on IoT, cooperative mobility, and network calculus applications.