جستجوی استاد راهنمای دانشگاهی

• Real-time Systems
• Model Checking
• Critical Systems
• +۴ مورد دیگر

Yasmina Abdeddaïm is an Associate Professor at Université Gustave Eiffel and affiliated with ESIEE Paris. She works within the Laboratoire d'Informatique Gaspard-Monge (Softwares, Networks and Real-time team) and serves as Head of the Master in Artificial Intelligence and Cybersecurity (AIC) program. Her research focuses on real-time systems, critical systems, and scheduling algorithms. University: Université Gustave Eiffel Role: Head of Master AIC program Laboratory: Laboratoire d'Informatique Gaspard-Monge Team: Softwares, Networks and Real-time Her research spans real-time systems , mixed-criticality scheduling , energy-harvesting systems , and probabilistic schedulability . Recent publications analyze compilation optimization impacts on timing variability and propose new models for real-time deep neural networks over GPUs. She employs formal methods like timed automata for scheduling verification. Her teaching includes courses on Real-time Systems , Model Checking , Critical Application Development , and Artificial Intelligence . She is based at Cité Descartes, Champs-sur-Marne, France, with office contact details provided.

Samuel Thibaultمشاهده پروفایل

استاد

• Parallel Computing
• High Performance Computing
• Task-Based Runtime Systems
• +۳ مورد دیگر

Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.

Francois Trahayمشاهده پروفایل

دانشیار

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +۲ مورد دیگر

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

Sebastien Pillementمشاهده پروفایل

مدرس

• Computer Engineering
• Embedded Systems
• Computer Architecture
• +۳ مورد دیگر

Sébastien Pillement is a Lecturer at Polytech'Nantes, the College of Engineering of the University of Nantes, and a member of the ASIC research team within the IETR UMR 6164 laboratory. His career spans over 13 years at the IUT of Lannion (University of Rennes 1) before joining the University of Nantes in 2012. He holds a PhD in Computer Science from the University of Montpellier II (1998) and a Habilitation à Diriger des Recherches (HDR) from the University of Rennes 1 (2010). Current Research Focus: Dynamically Reconfigurable Architectures (DRA), Hardware Security, Fault-Tolerant Embedded Systems, NoC (Network-on-Chip), Design Methodologies. Teaching Areas: Digital Circuits, Computer Architecture, Operating Systems, Real-Time Systems, and Algorithms for Embedded Computing. His research explores flexible and efficient architectures for embedded systems, emphasizing real-time management, reliability, and security. Key article trends include RISC-V processors, neural network acceleration on MPSoC, formal verification of arithmetic circuits, and runtime FPGA scheduling. Collaborations span international institutions like the University of Toronto and CEA, as well as industrial partners such as STMicroelectronics and Thales. PhD Students : Current: Laureline Dubucq (Security & AI), Mustafa Ibrahim (Ultra-Low Power AI), Téo Biton (Runtime Anomaly Detection), Mohamed Amine Zhiri (Adaptive NoC), Juliette Pottier (RISC-V Protection). Graduates: Quentin Dariol (Neural Network Timing), Alexis Duhamel (FPGA Scheduling), Safouane Noubir (Security in Multi-Core Systems), Hai Dang Vu (Probabilistic Timing Analysis), David Pallier (Sensor Clock Synchronization). Projects : Current: ADAPTING (2023-2029), FITNESS (2023-2027), SEC-V (2022-2025), NOP (2021-2025). Past: SPARTE (2015-2018), ARDyT (2011-2015), FosFor (2008-2011).

Vincent Danjeanمشاهده پروفایل

دانشیار

• Parallel Computing
• High-Performance Computing
• Task-Based Parallelism
• +۶ مورد دیگر

Vincent Danjean is an associate professor at Grenoble Alpes University , specializing in parallel computing, high-performance computing, and bioinformatics. He earned his PhD in 2004 from École Normale Supérieure de Lyon under the supervision of Raymond Namyst. Research Interests: Vincent's work spans several critical areas in computational science: Parallel and Distributed Systems: Focus on task-based parallelism and hybrid cluster architectures. Performance Analysis: Development of visual frameworks for analyzing parallel applications. Bioinformatics: Application of computational methods to genetic and genomic data analysis. GPU Computing: Efficient scheduling and work stealing strategies for multi-GPU systems. Reproducible Research: Workflows using Git and Org-mode for scientific transparency. Publication Trends: His publications demonstrate a consistent focus on advancing parallel computing techniques, with significant contributions to GPU scheduling, cache-efficient algorithms, and visualization tools. Recent work includes interdisciplinary applications in genomics and cybersecurity protocols. Contact: vincent.danjean@imag.fr

• Parallel and Distributed Computing
• High Performance Computing
• Edge-Cloud Computing
• +۳ مورد دیگر

Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.

Tobias Wrigstadمشاهده پروفایل

استاد

• Type Systems
• Reference Capabilities
• Programming Languages
• +۲ مورد دیگر

Tobias Wrigstad is a faculty member at Uppsala University, Sweden, with research interests spanning type systems, reference capabilities, programming language design, scripting languages, and concurrent/parallel programming. His work focuses on memory management, concurrency safety, and language extensions for performance optimization. Education: Not explicitly mentioned in provided data Research Interests: Designing type systems to enforce concurrency safety and memory correctness Reference capabilities for manual and automatic memory management Actor model programming and garbage collection co-design Cache locality optimization without program restructuring Formal verification of language designs using Dafny Recent Publications (2025-2015): Explore concurrency safety through region ownership Develop parallel array programming models in Kappa Investigate energy-efficient garbage collection Design capability-based dynamic languages for data race freedom Create formal models for heap invariants and incorrectness Optimize memory allocation via load barriers Conference Involvement: 2025: IWACO Committee Member, OOPSLA Associate Chair 2024: Program Co-Chair for IWACO, Author in VIMPL, MPLR, ISMM 2023: SPLASH Steering Committee, ECOOP PC Member 2022-2015: Active in PLDI, ECOOP, ICFP, and related workshops

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

• High-Performance Computing (HPC)
• Scientific Computing
• Numerical Simulation
• +۳ مورد دیگر

Lionel Boillot is an Expert Engineer and holds a PhD in the MAGIQUE-3D project team at Inria, located in Bordeaux, France. His work focuses on High-Performance Computing (HPC), Scientific Computing, and numerical simulation techniques for geophysical applications. He specializes in parallel algorithms, discontinuous Galerkin methods (DGM), and optimizing wave propagation simulations in anisotropic media. His research emphasizes hardware optimization, including vectorization via SIMD and GPU acceleration, and task-based parallel programming models for large-scale simulations. Education: PhD in Applied Mathematics and Parallel Algorithmic Optimization (2014), Université de Pau et des Pays de l'Adour, France. Research Interests: Boillot's work spans HPC architecture, absorbing boundary conditions for elastic waves, and seismic imaging. He has contributed to developing efficient algorithms for TTI (Tilted Transverse Isotropic) media modeling and optimizing simulations using runtime systems like StarPU. His research also explores collaborative tools in geology, such as VR-based seismic analysis (Sismage-VR). Key Projects: Collaborations include work with TOTAL (via MATHIAS symposia), the Depth Imaging Partnership (INRIA-TOTAL), and international workshops like SIAM PP and EAGE. His publications span conferences such as SIAM, SEG, and ECCOMAS, focusing on parallel computing, boundary conditions, and geophysical applications. Affiliations: Active member of the MAGIQUE-3D team, contributing to both theoretical and applied aspects of computational geophysics. His work intersects applied mathematics, computer science, and geoscience to advance computational methods in resource exploration and seismic analysis.

James R. Eaganمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Explainable AI
• Data Analysis
• +۳ مورد دیگر

James R. Eagan is an Associate Professor at Télécom Paris , part of Institut Polytechnique de Paris , and holds a Visiting Professor position at the University of Colorado, Boulder in the College of Media, Communication & Information. He leads the DIVA Lab (Data, Intelligence, Visualization, and Applications) within the Computer Science & Networking Department. Currently on sabbatical (2024–25 academic year) at CU Boulder, his research bridges Human-Computer Interaction, Explainable AI (XAI), and computational media design.

KC Sivaramakrishnanمشاهده پروفایل

استادیار

• Functional Programming
• Language Runtimes
• Concurrency
• +۴ مورد دیگر

KC Sivaramakrishnan is an Assistant Professor at Indian Institute of Technology Madras and concurrently serves as CTO of Tarides. He works at the intersection of programming languages and systems, focusing on concurrency, distributed systems, and OCaml runtime development. Primary Affiliation: Indian Institute of Technology Madras Co-founder: Tarides His research explores: Concurrency and parallelism in OCaml Effect handlers for modular programming Mergeable Replicated Data Types for distributed systems Weak memory models and consistency guarantees Lock-free algorithms and safe multicore programming Recent publications focus on OCaml 5.0's concurrency features, effect handler integration, and verified CRDT implementations. He contributes to compiler design, runtime optimization, and testing frameworks for multicore systems. Service roles include committee memberships in SPLASH, ICFP, OCaml, and PROPL conferences. He actively bridges functional programming with systems research through practical implementations.

• Human-Computer Interaction
• Task Modeling
• User Interface Design
• +۳ مورد دیگر

Sybille Caffiau serves as a Lecturer in Data Engineering at ISAE-ENSMA's School of Engineering, affiliated with the LIAS laboratory across both ENSIP (Poitiers) and ISAE-ENSMA (Chasseneuil) campuses. Her research focuses on Human-Computer Interaction with specialization in task modeling and user interface validation. Her research interests span Human-Computer Interaction , Task Modeling , and User-Centered Design . She develops and evaluates tools like K-MADe for modeling user activities, with emphasis on empirical validation of task modeling approaches. Her work bridges cognitive science principles with practical software engineering applications, particularly in educational technology and interactive system design. Analysis of her publication trends reveals consistent contributions to HCI conferences and journals since 2006, with increasing focus on tool development (notably K-MADe) and empirical validation methodologies. Her recent work demonstrates strong interdisciplinary connections between cognitive modeling, software engineering, and educational technology. No scientific awards or fellowships are documented in the available materials. While her collaborative network includes prominent researchers like Laurent Guittet and Patrick Girard, specific details about student advising or research grants are not provided in the source materials. Her thesis work (2009) established foundations for model-driven interactive application design. She operates within the LIAS laboratory's Data Engineering team, contributing to research on interactive systems and task modeling frameworks. The KMADe environment represents her primary software contribution for activity description and task modeling.