جست‌وجوی استاد و پژوهشگر

Nour Aliمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Adaptive Systems
• +۳ مورد دیگر

Dr. Nour Ali serves as a Professor and Vice-Dean of Education in the College of Engineering, Design and Physical Sciences at Brunel University London, where she co-heads the Brunel Software Engineering Lab. With a PhD in Software Engineering from Universidad Politecnica de Valencia and a Computer Science degree from Bir-Zeit University, she brings extensive international experience from previous positions at University of Brighton, Lero (Irish Software Engineering Research Centre), and Politecnico di Milano. PhD in Software Engineering, Universidad Politecnica de Valencia, Spain Major in Computer Science, Bir-Zeit University, Palestine PG Certificate in Teaching and Learning in Higher Education, University of Brighton Fellow of the Higher Education Academy (HEA) Her research focuses on developing software architecture techniques for distributed, mobile, and adaptive systems, with particular expertise in microservice architecture recovery and visualization. With over 70 publications spanning two decades, her work bridges theoretical architecture principles with practical implementation challenges. Recent research demonstrates a strong focus on microservice architecture recovery tools like MiSAR and analysis of service granularity adaptation. Dr. Ali has made significant contributions to the software engineering community through editorial roles including Deputy Editor in Chief of IET Software, committee memberships for major conferences like ICSE and ASE, and reviewer positions for EPSRC and other funding bodies. Her publications reveal consistent contributions across architecture consistency, microservice systems, and adaptive requirements engineering, with recent work increasingly focusing on practical tool development for architecture recovery. External Examiner, York St John University (2021-2025) Deputy Editor in Chief, IET Software Committee member for ICSE, ASE, EASE, ICSA, MOBILESoft conferences EPSRC Full College Member (2018-present) Reviewer for Dutch Research Council (NWO), UK UNESCO Newton Prize As an educator, Dr. Ali leads CS3100 Software Project Management and contributes to multiple undergraduate and postgraduate courses. Her teaching philosophy integrates research insights with practical software engineering skills, supported by her PG Certificate in Teaching and Learning and HEA Fellowship. She actively supervises PhD students and contributes to curriculum development as Vice-Dean of Education for her college.

Lei Buمشاهده پروفایل

استاد

• Software Verification
• Software Testing
• Software Analysis
• +۸ مورد دیگر

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

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.

Arya Mazaheriمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Parallel Processing
• Machine Learning
• +۵ مورد دیگر

Arya Mazaheri is a Research Leader at PanocularAI, affiliated with the Technische Universität Darmstadt. His work bridges high-performance computing (HPC) and machine learning, focusing on optimizing large-scale computational systems. Based at Hochschulstr. 10, Darmstadt, Germany, he contributes to GPU acceleration, neural network pruning, and parallel processing. PhD in Performance Engineering of Data-Intensive Applications (2022) Key areas: HPC, Machine Learning, GPU Computing, Neural Network Pruning Research Trends: Mazaheri's publications from 2015-2024 reveal expertise in: Accelerating LLM inference through pipelined speculation Topology-aware network pruning with reinforcement learning GPU-based spacecraft trajectory simulations Performance portability in tensor operations Hardware-independent communication metrics for parallel systems

• High Performance Computing
• Energy Efficiency
• Parallel Computing
• +۶ مورد دیگر

Laura Carrington is a researcher at the University of California, San Diego, specializing in High Performance Computing (HPC) with a focus on energy efficiency, memory management, and performance optimization. She has contributed to the development of tools like PEBIL for binary instrumentation, ADAMANT for data movement analysis, and frameworks for power management in large-scale systems. Her research spans multiple domains including ARM processor evaluation, Xeon Phi vectorization, and communication reduction in graph algorithms. Key collaborations include work with Michael Laurenzano, Allan Snavely, Ananta Tiwari, and Pietro Cicotti. Laura's work addresses critical challenges in HPC such as DVFS configuration optimization, workload colocation, and energy-aware algorithm design. While no explicit academic rank is stated, her extensive publication record across 2002-2019 in top venues like SC, IPDPS, and IJHPCA establishes her as a significant contributor to HPC research. Her work has influenced practices in system-level power management, scientific application characterization, and energy-efficient computing for both CPU/DRAM domains and emerging memory technologies.

Georg Hagerمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Performance Engineering
• Computational Physics
• +۷ مورد دیگر

Dr. Georg Hager is Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg, and an associate lecturer at the Institute of Physics, University of Greifswald. His work focuses on performance engineering, node-level optimization, and analytic modeling in high performance computing. Research Interests: High Performance Computing (HPC) Performance Engineering and Modeling Architecture-Specific Optimization Energy Efficiency in Computing Scientific Code Optimization Execution-Cache-Memory (ECM) Model Development Computer Architecture for HPC His recent publications and tutorials emphasize performance modeling, node-level engineering, hybrid programming (MPI+OpenMP), and benchmarking on modern architectures such as Ice Lake, Sapphire Rapids, and A64FX. He has contributed to the development of the LIKWID tool suite and promotes best practices in HPC education. Scientific Awards: ISC Gauss Award (2018) Informatics Europe Curriculum Best Practices Award (2011) Georg Hager has been instrumental in developing and teaching international tutorials on performance engineering and hybrid programming, often in collaboration with HLRS Stuttgart and TU Wien. He is the co-author of the widely used textbook Introduction to High Performance Computing for Scientists and Engineers . His work bridges theoretical modeling and practical application, aiming to improve time-to-solution and resource efficiency in large-scale scientific computing.

• Database Systems
• Big Data Management
• XQuery Processing
• +۳ مورد دیگر

Vinayak R. Borkar is a researcher and software engineer affiliated with the University of California, Irvine, where he completed his PhD in 2016. His work focuses on big data platforms, database systems, and scalable query processing frameworks. PhD in Big Data Processing (UC Irvine, 2016) Contributions to Apache AsterixDB, Hyracks, and Pregelix Industry experience at BEA Systems (2000s) Research interests include database systems , big data management , XQuery optimization , and dataflow engines . His publications analyze scalable similarity queries, memory management, and declarative approaches to machine learning. Recent articles explore Apache AsterixDB , dataflow compilation , and graph analytics . Collaborators include Michael J. Carey and Alexander Behm.

• Database Systems
• Spatial Data Processing
• Indexing Algorithms
• +۳ مورد دیگر

Walid G. Aref is a Professor at Purdue University, West Lafayette, USA, specializing in database systems, spatial data processing, and big data technologies. His work focuses on adaptive indexing, LSM trees, and graph data systems. 2025: Research on skiplists, GTX graph systems, and BMTree indexing 2024: Contributions to trajectory indexing and HTAP-optimized data systems 2023: Editorial roles in ACM Transactions on Spatial Algorithms and Systems His research spans scalable spatial-keyword query processing, distributed streaming systems, and hardware-aware database optimization. Notable collaborations include Ahmed R. Mahmood and Mourad Ouzzani. Recent publications highlight trends in machine learning for indexing , NUMA-aware optimization , and multi-dimensional data structures . He has no listed scientific awards in this dataset. Walid actively contributes to transactional graph systems , load balancing , and spatiotemporal data management , with a 2021 IEEE Transactions paper on attack-resilient load balancing.

Harry Xuمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Computer Systems
• +۳ مورد دیگر

Harry Xu is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA), with a research focus on computer systems spanning programming languages, compilers, runtime/operating systems, distributed systems, and computer architecture. Prior to UCLA, he was an Associate Professor at UC Irvine (UCI). His current work centers on infrastructures for future cloud computing (user-defined clouds) and scalable, cost-effective AI/ML systems. Education : Ph.D. in Computer Science and Engineering from Ohio State University (2011) His research has pioneered techniques for combating software bloat, optimizing data analytics through programming language innovations, and developing systems like Yak GC, VQPy, and Niijima. He co-founded BreezeML, a startup for GenAI risk management, and has held visiting roles at Microsoft Research and IBM Watson Research Center. Recent publications include work on resource-disaggregated datacenters, cloud computing fault tolerance, and ML system scalability, presented at top venues like SOSP, OSDI, and NSDI. He has advised numerous Ph.D. and M.S. students, many of whom now hold academic or industry roles. Awards : 2018 Dahl-Nygaard Junior Prize, ACM Distinguished Scientist Harry actively contributes to academic service as a committee member and co-chair at conferences like PLDI, SPLASH, and VMCAI. He maintains the VQPy project and other open-source systems.

Timo Hönigمشاهده پروفایل

استاد

• Operating Systems
• Energy Efficiency
• Computer Security
• +۳ مورد دیگر

Timo Hönig is a Professor and Head of the Chair for Operating Systems and System Software at the Faculty of Computer Science, Ruhr-University Bochum. His research focuses on energy efficiency, persistent memory systems, computer security, and system software design. Current projects include DFG-funded SUSTAINET-inNOvAte and DOSS , with former projects like InvasIC (DFG/Transregio 89) and Antillas (BMBF/VDI). He serves on program committees for HotCarbon, DIMES, DSN, DATE, and other conferences. His recent publications address energy profiling, carbon-aware memory placement, kernel security, and persistent OS design. He has received best paper awards at SBESC'22 and ISORC'17. Scientific Awards: Best Paper at SBESC'22 Best Paper at ISORC'17 His teaching includes courses on operating systems, resource-efficient systems, and energy-aware computing. He has advised numerous research collaborations on embedded systems, concurrency, and sustainable computing.

• 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.

Peter Zaspelمشاهده پروفایل

دانشیار

• Scientific computing
• Machine learning
• Uncertainty quantification
• +۷ مورد دیگر

Peter Zaspel has been a W2 Professor of Software for Data-Intensive Applications at the University of Wuppertal since July 2023. Previously, he served as Assistant Professor (March 2022–June 2023) and Acting Professor of Computer Science (Machine Learning) at Jacobs University Bremen gGmbH . His career includes postdoctoral positions at the University of Basel (2017–2019), Heidelberg Institute for Theoretical Studies (HITS) and Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University (2015–2017), and as a research associate at the Institute for Numerical Simulation, University of Bonn (2009–2015). Education Habilitation in Mathematics, University of Basel, 2019–2021 PhD in Applied Mathematics, University of Bonn, 2009–2015 Diploma in Computer Science, University of Bonn, 2004–2009 Research Interests Peter Zaspel’s research integrates machine learning with high-performance computing and uncertainty quantification . Key themes include multi-fidelity learning , Bayesian inference , kernel-based stochastic collocation , and scalable parallel algorithms for GPUs and distributed-memory systems. His work spans materials science , quantum chemistry , paleoclimate reconstruction , fluid mechanics , and medical imaging . Projects & Funding DFG SPP 2363: “Multi-fidelity, Active Learning Strategies for Exciton Transfer Between Adsorbed Molecules” (2022–2025) MarDATA project: “Bayesian chronology modeling for paleoclimate archives” (2022–2025) MarDATA project: “Digital ice cores: paleoclimate reconstruction using Bayesian methods” (2022–2025) DFG project: “Excitation energy transfer in a photosynthetic system with more than 100 million atoms” (2021–2024) Invited Presentations “Steigerung der Aussagekraft von Vorhersagen durch Unsicherheitsquantifizierung”, WEML2018, Heidelberg, 2018 “Netzfreie und Multi-Index-Approximationen für parametrische Probleme der realen Welt”, RWTH Aachen, 2018 “Optimalkomplexitätskernbasierte stochastische Kollokation mit Anwendung in der Strömungsmechanik”, EPFL, 2017 “Skalierbare Löser für netzlose Methoden auf Many-Core-Clustern”, QUIET 2017, Trieste, 2017 “H-Matrizen auf Many-Core-Hardware mit Anwendungen in parametrischen PDEs”, University of Kiel, 2016 “Algorithmische Muster für hierarchische Matrizen auf Vielkernprozessoren”, University of Basel, 2016

• Databases
• Information Systems
• Data Mining
• +۳ مورد دیگر

Rebecca Sattler serves as a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin, actively contributing to the Databases and Information Systems research group. Holding an M.Sc. degree, she maintains current institutional affiliation with contact details including email rebecca.sattler@hu-berlin.de and office phone 030 2093-41243 at the Rudower Chaussee 25 campus in Berlin-Adlershof. Her research concentrates on advanced database methodologies, specializing in event stream processing architectures and automated query discovery systems. Key investigations include temporal pattern recognition in data streams, validity constraint enforcement for analytical workflows, and multi-dimensional subsequence query mechanisms. This work bridges theoretical computer science with practical implementations in real-time data environments, demonstrating consistent innovation in handling complex event processing challenges. Analysis of her 2021-2025 publication record reveals an evolving research trajectory from foundational subsequence query frameworks toward scalable incremental update techniques and live-streaming optimization. Her contributions increasingly integrate machine learning approaches with classical database theory, particularly evident in recent works applying deep reinforcement learning to network function virtualization. Within the Institute's structure, she operates as a core member of the Databases and Information Systems group, collaborating on cutting-edge projects involving trace analysis, workflow validation, and high-throughput stream processing systems that address contemporary data-intensive computing demands.

• Semantic Web Services
• B2B Integration
• Workflow Management
• +۳ مورد دیگر

Christoph Bussler is a prominent researcher with extensive contributions to Semantic Web Services B2B Integration Cloud Computing Workflow Management Enterprise Application Integration His work focuses on semantic technologies, data engineering, and optimization frameworks. Recent publications analyze Cloud storage tier optimization Graph-based cost modeling Taxonomy for cloud storage costs Serverless-container hybrid systems SQL for NoSQL databases These studies emphasize scalable solutions and semantic interoperability. He has collaborated with experts like Dumitru Roman, Radu Prodan, and Dieter Fensel on projects involving Triple Space Computing WSMX Architecture Peer-to-Peer Service Discovery Rule-Based Classification

• High Performance Computing
• Agent Based Modeling
• Analysis of Large Datasets
• +۴ مورد دیگر

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence. Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume. His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling. Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic. Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.