PhD Student | IETR | France
Short Bio 📝
Ophélie Renaud is a dedicated post-doctoral researcher with a profound interest in high-performance computing (HPC) and its applications in astronomy. With a strong academic foundation and diverse professional experience, Ophélie has contributed significantly to the field of dataflow optimization and resource allocation in multi-core and heterogeneous architectures. Her research has been pivotal in advancing the efficiency and productivity of HPC systems.
Profile
Google Scholar
Education 🎓
- Doctorate (Ph.D.) in Signal, Image, Vision (2021–2024)
- Institutions: Univ Rennes, INSA Rennes, CNRS, IETR
- Dissertation: Model-based granularity optimization for high-performance computing systems in astronomy
- Engineering Degree in Electronics (2018–2021)
- Institution: INSA Rennes
- Specialization: Innovative technology design and development in alternation
- Technical University Diploma in Electrical Engineering and Industrial Computing (2016–2018)
- Institution: Université de Rennes
- Baccalaureate in Sciences (2016)
- Institution: Lycée F.R Chateaubriand de Combourg
- Specializations: Physics, European Spanish section, Swimming
Experience 💼
- Post-doctoral Researcher (Oct 2024–Mar 2025)
- IRISA/SATIE
- Focus: Dataflow modeling in radio astronomy, optimization of NenuFar
- Doctoral Researcher (2021–2024)
- Univ Rennes, INSA Rennes, CNRS, IETR
- Focus: Resource allocation optimization, dataflow programming for SKA
- Application Engineer Intern (Jul-Aug 2020)
- YASKAWA Slovenia
- Responsibilities: Automation programming, HMI and software development
- Application Engineer Apprentice (2018-2021)
- YASKAWA France
- Responsibilities: Automation programming, HMI and software development
- Robotics Technician Intern (Apr-Jun 2018)
- YASKAWA France
- Responsibilities: HMI programming
- Lifeguard (Summer 2017 and 2018)
- SNSM
- Responsibilities: Supervision, first aid, team management
Research Interests 🔍
- High-Performance Computing (HPC) Systems
- Rapid Prototyping
- Dataflow Programming
- Resource Allocation Optimization
- Simulation
- SKA Radiotelescope
Ophélie’s doctoral research addresses challenges in HPC systems, focusing on optimizing resource utilization, High Performance computing and enhancing software productivity, and advancing the co-design of architectures and applications. Her contributions include methods for multi-core resource allocation, heterogeneous processor resource distribution, and optimal topologies for HPC applications.
Awards 🏆
- Finalist in “Ma thèse en 180 secondes” Departmental Contest (May 2024)
- Participant in 3MT Contest at the 32nd European Signal Processing Conference (Aug 2024)
Publications 📚
- “Multicore and Network Topology Codesign for Pareto-Optimal Multinode Architecture” (2024)
- Authors: Ophélie Renaud, Erwan Raffin, Karol Desnos, Jean-François Nezan
- Published in: 32nd European Signal Processing Conference (EUSIPCO)
- Link
- “Automated Clustering and Pipelining of Dataflow Actors for Controlled Scheduling Complexity” (2023)
- Authors: Ophélie Renaud, Naouel Haggui, Karol Desnos, Jean-François Nezan
- Published in: 31st European Signal Processing Conference (EUSIPCO)
- Link
- “SCAPE: HW-Aware Clustering of Dataflow Actors for Tunable Scheduling Complexity” (2023)
- Authors: Ophélie Renaud, Dylan Gageot, Karol Desnos, Jean-François Nezan
- Published in: Design and Architecture for Signal and Image Processing (DASIP)
- Link