جستجوی استاد راهنمای دانشگاهی

• Secure Decentralized Systems
• Blockchain Technology
• Systems Security
• +۳ مورد دیگر

Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +۳ مورد دیگر

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Thomas Ludwigمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +۳ مورد دیگر

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

• Parallel File and Storage Systems
• Modular Supercomputing
• Resource Disaggregation and Virtualization
• +۶ مورد دیگر

Sarah Neuwirth is a tenured Professor for Computer Science at Johannes Gutenberg University Mainz (JGU) and a Visiting Researcher at the Jülich Supercomputing Centre. She manages JGU's High Performance Computing (HPC) division, coordinates regional/national HPC activities, and represents JGU in NHR, Gauss-Allianz, and HPC committees. Education : PhD (Dr. rer. nat.) in Computer Science (2018), Heidelberg University Diplom in Computer Science (2012), University of Mannheim Bachelor of Science in Computer Science (2010), University of Mannheim Research Interests : Parallel File and Storage Systems Modular Supercomputing (resource disaggregation/virtualization) Performance Engineering High Performance Computing Networking Reproducible Benchmarking Parallel I/O Publications Trends : Her work focuses on HPC performance modeling, parallel I/O optimization, modular supercomputing, network characterization, and reproducible benchmarks. Key themes include resource disaggregation, automated workflows, and data-intensive distributed applications. Scientific Awards : 2023 PRACE Ada Lovelace Award for HPC ZONTA Science Award 2019 Grants & Leadership : She leads the High Performance Computing division at JGU, participated in European DEEP projects, and serves on SC conference committees.

André Brinkmannمشاهده پروفایل

استاد

• Data Center Management
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

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.

• High-Performance Computing (HPC)
• Computational Fluid Dynamics
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.

Rainer Kellerمشاهده پروفایل

استاد

• Operating Systems
• Distributed Systems
• Parallel Programming
• +۴ مورد دیگر

Prof. Dr.-Ing. Rainer Keller serves as Vice Dean of the School of Computer Science and Information Technology at Esslingen University of Applied Sciences. He concurrently holds Laboratory Management roles for both the Operating Systems Laboratory and Information Technology Laboratory, coordinates the Applied Computer Science (Master) program, and serves on the Environmental Committee for his school. His academic journey includes a Doctorate in Engineering (with distinction), Research Associate and Group Leader positions at HLRS, University of Stuttgart (leading the 9-member "Applications, Models and Tools" group), PostDoc tenure at Oak Ridge National Laboratory (ORNL), and prior appointments at Stuttgart University of Applied Sciences. Keller's research centers on operating systems, distributed systems, and HPC with specialized expertise in Linux-based parallel programming tools and file I/O optimization. His work bridges theoretical system models with practical performance tuning, particularly in high-performance computing environments where syscall tracing and storage optimization are critical. Recent publications reveal consistent focus on system-level performance analysis, evolving from foundational file I/O profiling (2020) to advanced syscall tracing mechanisms (2022) and forward-looking access optimization frameworks (2025). These works demonstrate applied research in Linux ecosystems with direct relevance to HPC infrastructure. As Program Coordinator for the Applied Computer Science (Master) program and laboratory manager for two key facilities, he oversees academic development and hands-on technical training. His consultation hours (Tuesdays 1-2 PM by appointment) support student engagement in systems research. He actively contributes to national HPC infrastructure as a Member of the State User Committee (LNA) bwHPC, influencing regional high-performance computing strategies while maintaining his laboratory management responsibilities at Esslingen.

Reza Salkhordehمشاهده پروفایل

مدرس

• Operating Systems
• Solid-State Drives
• Data Storage Systems
• +۵ مورد دیگر

Reza Salkhordeh is a Lecturer and Postdoctoral Researcher at Johannes Gutenberg University Mainz, Germany, where he leads the Efficient Computing and Storage Group. He holds a Ph.D. in Computer Engineering from Sharif University of Technology (2018) and has been affiliated with Ferdowsi University of Mashhad and Sharif University of Technology. His research focuses on operating systems, storage systems, and non-volatile memory technologies, with a particular emphasis on high-performance computing and I/O optimization. He has taught courses such as Storage Systems and Advanced Topics in Operating Systems since 2020. His academic journey includes a B.Sc. from Ferdowsi University (2011), M.Sc. and Ph.D. from Sharif University (2013, 2018). He has held roles such as Technical Lead for the High-Performance Data Storage System (HPDS) project in Tehran, Iran, and has mentored 4 M.Sc. and 6 B.Sc. students. His work spans patented technologies like Reconfigurable Cache Architectures and Load Balancers for I/O caching systems. Research interests include heterogeneous memory management, storage system design, and optimizing I/O performance in distributed environments. His recent publications address challenges in NVMM utilization, garbage collection in SSDs, and I/O tracing for HPC systems. He is actively involved in conference committees (e.g., FAST, ARCS, SC) and has reviewed for top journals like IEEE TPDS and ACM Transactions on Storage. Notable recognitions include membership in Iran’s National Elites Foundation (2012–2015) and top rankings in national exams (3rd in PhD, 35th in MSc). His contributions to storage systems have advanced enterprise-grade architectures and decentralized file systems, with a focus on practical implementations for modern computing challenges.

• High-Performance Computing (HPC)
• Parallel Programming
• Performance Modeling
• +۶ مورد دیگر

Felix Wolf is a Full Professor at Technische Universität Darmstadt's Department of Computer Science since 2015 and leads the NHR4CES@TU Darmstadt HPC center. He served as Department Chair and Vice Department Chair at TU Darmstadt, with prior faculty roles at RWTH Aachen University (2006–2015) and adjunct positions at the University of Tennessee (2003–2008). His academic work spans performance analysis, parallel programming, and scalability modeling. Ph.D. in Computer Science, RWTH Aachen University (2003) M.Sc., RWTH Aachen University (1998) Research Interests : Wolf focuses on High-Performance Computing , including performance modeling , parallel programming tools , and I/O optimization . His work addresses GPU acceleration , machine learning integration into HPC systems, and structural plasticity simulation . Key projects involve tools like Scalasca, Score-P, and Extra-P for automated performance analysis. Article Trends : Recent publications emphasize HPC systems for AI-driven workflows , I/O contention reduction , and GPU port validation . His team explores empirical modeling for deep learning and scientific simulations , with applications in engineering and neuroscience .

Sergei Savitskyمشاهده پروفایل

استاد

• Reconfigurable Computing
• Digital Signal Processing
• Hardware Architectures
• +۳ مورد دیگر

Sergei Savitsky is a Professor at Wedel University of Applied Sciences, where he serves as Head of the Bachelor of Computer Science program and Senate Chairman. He has been a university lecturer at the institution since October 2008, following professional experience at NXP Semiconductors (2006-2008) and Philips Research Europe (2001-2006). His educational background includes: Doctorate in Engineering (Dr.-Ing.) from Technical University of Dresden (2002) with distinction "summa cum laude" Habilitation (Dr.-Ing. habil.) from Technical University of Dresden (2024) with teaching authorization in "Technical Computer Science" Diplom-Informatiker (equivalent to MSc) in Computer Science from Technical University of Dresden (1998) Savitsky's research focuses on reconfigurable computing systems , with particular expertise in FPGA design, hardware acceleration, and error correction coding. His work bridges theoretical computer science with practical hardware implementation, resulting in numerous patents and publications in top venues. He has made significant contributions to the development of adaptive hardware architectures for forward error correction, which are critical for modern communication and storage systems. His recent publications demonstrate a strong trajectory in optimizing hardware design processes, with particular focus on applying machine learning techniques like self-organizing maps and gradient descent algorithms to improve FPGA placement efficiency. His research spans both theoretical foundations and practical applications, with patents filed in collaboration with industry partners like NXP Semiconductors and ST-Ericsson. Among his recognitions is the Best Paper Award at CENICS 2019 for his work on accelerating FPGA placement algorithms. As an educator, Savitsky teaches courses related to digital system design, including "Computer-aided design of digital systems," where he emphasizes algorithmic aspects of Electronic Design Automation beyond basic digital technology concepts.

• Discretization Methods
• Porous Media Flow
• Numerical Simulation
• +۳ مورد دیگر

Bernd Flemisch is an Adjunct Professor at the University of Stuttgart, affiliated with the Department of Hydromechanics and Modelling of Hydrosystems within the Institute for Modelling Hydraulic and Environmental Systems. He holds a Dr. rer. nat. habil. (2013) and has been active in academic research since 2001. Education: 2001: M.Sc. in Mathematics, Iowa State University 2006: Doctoral Degree (Dr. rer. nat.), University of Stuttgart 2013: Habilitation (Dr. rer. nat. habil.), University of Stuttgart Research Interests: Flemisch specializes in discretization methods, solvers, and research software development for porous media flow. His work focuses on numerical simulation of multiphase systems, coupled flow processes, and adaptive multi-scale modeling. Key areas include computational geosciences, fluid mechanics, and energy storage solutions such as hydrogen and CO₂ sequestration. Professional Activities: Since 2024: Project leader in CRC 1667 and Base4NFDI/Jupyter4NFDI Since 2023: Dean of Studies for Civil Engineering at University of Stuttgart Leadership roles in NFDI4Ing (National Research Data Infrastructure for Engineering) Co-developer of open-source simulators like DuMuX and Frackit Lab & Teams: Engages in collaborative projects such as the FluidFlower CO₂ storage experiment and the Stuttgart Center for Simulation Science (SC SimTech). His work emphasizes reproducible research and sustainable software practices.

Michael Bussmannمشاهده پروفایل

پژوهشگر ارشد

• Laser Plasma Acceleration
• Plasma Physics
• Computational Physics
• +۳ مورد دیگر

Dr. Michael Bussmann is the Founding Manager of the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). He holds a Research Fellow position and has been a Group Leader at HZDR since 2016, focusing on computational radiation physics and laser-plasma interactions. His academic journey includes a PhD in Physics from Ludwig-Maximilians University Munich, with research encompassing laser-cooling of ion beams, plasma dynamics, and high-performance computing. Bussmann's work bridges experimental and computational physics, particularly in advancing particle acceleration technologies and plasma-based applications. Research Interests: His primary focus lies in laser plasma acceleration, computational physics, radiation physics, and the development of exascale simulation tools. Notably, he contributes to projects like PIConGPU and explores applications of AI in scientific workflows. His interdisciplinary approach integrates plasma physics with medical research, such as federated learning for oncology. Publications: Recent work spans topics from exascale plasma simulations to AI-driven medical research, reflecting his dual expertise in computational science and applied physics. Key contributions include optimizing laser-ion acceleration using machine learning and advancing federated learning frameworks in cancer studies. Awards: While no specific awards are listed, his sustained leadership in high-impact research and contributions to HZDR’s initiatives highlight his recognized expertise. Grants & Collaborations: Bussmann leads the CASUS initiative, fostering collaboration between HZDR and international partners. His projects often involve EU frameworks and industrial partnerships, emphasizing translational research in energy and healthcare. Labs/Teams: Directs the Computational Radiation Physics group at HZDR and oversees CASUS, a hub for data-driven systems research. His teams develop cutting-edge simulation tools and explore interdisciplinary applications of plasma physics.

Ilia Petrovمشاهده پروفایل

استاد

• Data Management
• Data Analytics
• High Performance Computing
• +۴ مورد دیگر

Prof. Ilia Petrov is a faculty member at Reutlingen University's Faculty of Computer Science and Engineering (INF), leading the Data-Management and -Analysis department. He holds the academic rank of Professor and has been affiliated with the university since 2012. His research focuses on advancing data management and analytics, particularly in the realms of database systems, hardware-optimized storage, and near-data processing. He leads the Daten-Management Labor (Data Management Lab), which explores innovations in computational storage, flash memory optimization, and high-performance database architectures. His research interests include the development of novel storage technologies, such as native computational storage and processing-in-memory systems, alongside efficient indexing methods (e.g., B-Tree variants) for multi-version databases. He has contributed to projects like DANSEN and nativeNDP, which aim to leverage modern hardware for accelerating database operations. Key themes in his work involve minimizing write amplification on flash storage, optimizing HTAP workloads, and enabling efficient near-data processing through FPGA and NVMe integration. Prof. Petrov's publications span topics including computational storage architectures, Bloom filter optimizations for range queries, and concurrency control in large-scale multi-socket systems. His work emphasizes the synergy between database software and hardware innovations, with applications in enterprise systems and big data analytics. He has collaborated on frameworks like NVMulator for non-volatile memory emulation and Cache-coherent shared locking mechanisms for smart storage systems. His lab's activities include the development of KV-store accelerators (e.g., nKV) and cost models for query optimization in key-value systems. He has also explored blockchain data structures and snapshot isolation techniques for database transactions. Current trends in his research address the challenges of real-time data processing, cross-layer data formats, and the integration of emerging storage technologies like computational storage drives.

Viktor Vafeiadisمشاهده پروفایل

پژوهشگر

• Weak Memory Models
• Concurrency
• Program Verification
• +۴ مورد دیگر

Viktor Vafeiadis is a tenured researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He received his BA and PhD from the University of Cambridge (2004, 2008) and worked as a postdoctoral researcher at Microsoft Research Cambridge and the University of Cambridge before joining MPI-SWS in 2010. His research focuses on concurrency , verification of concurrent programs, weak memory consistency , weak persistency , and formal semantics of programming languages. Research Areas: Weak Memory Models, Program Verification, Persistent Programming, Automated Model Checking Key Tools: Cave (automated verification tool), GenMC (model checker), Kater (consistency checker) His recent work explores linearizability under relaxed memory , mixed-size memory accesses , and formalizing ARMv8 persistency . Awards include the ACM SIGPLAN John C. Reynolds Award (2008), ERC Consolidator Grant (2020), and Robin Milner Young Researcher Award (2022). Selected Awards: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2008) ERC Consolidator Grant (2020) ACM SIGPLAN Robin Milner Young Researcher Award (2022) Distinguished Paper Awards: PLDI'17, OOPSLA'21, ECOOP'17 He has mentored numerous PhD students including Ellen Arlt , Pavel Golovin , and Aristotelis Koutsouridis , while alumni advisees include Soham Chakraborty and Michalis Kokologiannakis . His collaborative work spans institutions like University of Cambridge , Microsoft Research , and ETH Zürich .

• Cloud Computing
• Distributed Systems
• Grid Computing
• +۸ مورد دیگر

Christine Morin is a Professor at INRIA - Institut National de Recherche en Informatique et en Automatique, focusing on advanced research topics in cloud computing, distributed systems, and high-performance computing. Fields of Research: Cloud Computing, Distributed Systems, Grid Computing, Virtualization, Parallel Computing, High Performance Computing, Fault Tolerance, Resource Management, Operating Systems, Autonomic Computing, Energy Efficiency Long-term Research Contributions: SLA management in cloud environments, self-adaptable systems, energy-efficient computing, distributed application deployment Her work has been particularly influential in the areas of cloud security, SLA verification, fault tolerance mechanisms, and energy-efficient computing. She has pioneered approaches to self-adaptable security monitoring in IaaS clouds and developed innovative frameworks for cloud bursting PaaS environments. Christine Morin has published extensively on topics related to: Autonomic cloud management systems like Snooze and Merkat Energy-aware resource allocation and consumption models Message logging and fault tolerance protocols Distributed application deployment under SLA constraints Virtual organization support in grid operating systems Her research has contributed significantly to the evolution of cloud computing infrastructure, particularly in the areas of security monitoring, resource management, and energy efficiency.