Mr. Etienne Memin | Systemes Dynamiques | Best Researcher Award
PHD at Rennes I University
👨🏫 🌊 Since 2022, I’ve proudly led the interdisciplinary ODYSSEY research group, a collaborative effort affiliated with Inria, Ifremer, Institut Mines-Télécom Atlantique, the University of Western Brittany Brest, and the University of Rennes. Spanning locations in Brest and Rennes, our team operates within three CNRS laboratories, focusing on Mathematics (IRMAR/INSMI), Earth Sciences and Astronomy (LOPS/INSU), and Computer Science (Lab-STICC/INSSII). Comprising 21 permanent Researchers, 1 Research Engineer, 23 Ph.D. students, and 6 post-doctoral fellows, our group converges expertise in Physical Oceanography, Computational Science, Data Analysis, and Applied Mathematics. Our research endeavors aim to merge data-driven ocean models, physics-based approaches, and observational data, with a primary focus on ocean dynamics modeling and analysis. With strong international partnerships and expertise in stochastic modeling, machine learning, and data assimilation, our group continues to forge new paths in understanding and modeling ocean dynamics. 🌍🔍
🌐 Professional Profiles:
🎓 Education
2003: Habilitation degree in Computer Science, Rennes I University 1993: PhD in Computer Science, Rennes I University
💼 Professional Experience
Visiting Professor at Imperial College London (2020-2026) Research Director (Full Professor status) at Inria and Leader of the Fluminance Inria research group Invited Research Position at the Fluid Mechanics Laboratory, University of Buenos Aires Leadership in basic research competitive European projects Tenured Assistant Professor roles and Lecturer positions at esteemed universities.
🔬 Research Interests & Contributions
Developing large-scale stochastic dynamical models, Identifying reduced-order dynamical models, Creating specialized flow measurement techniques, Designing data assimilation frameworks.
🌐 Scientific Responsibilities: Leadership roles in significant research teams and projects, Active involvement in national and international collaborative projects, Co-PI for ERC STUOD (Stochastic Transport for Upper Ocean Dynamics) 📰 Editorial Board Membership: Associate Editor of esteemed journals, including Image and Vision Computing Journal and International Journal in Computer Vision 🎙️ Conferences & Workshops: Participation as a keynote speaker or lecturer in various prestigious conferences and workshops worldwide, focusing on SPDEs, data assimilation, climate dynamics, and related areas. 🎓 Theses & Post-doctoral Supervision: Supervision of numerous PhD students and post-doctoral fellows Strong success rate of alumni securing positions in academia, industry, and post-doctoral roles Mentoring recipients of prestigious awards for their outstanding theses 💻 Major Software Contributions: Significant contributions to software developments in the realm of real-time motion estimation and atmospheric wind estimation, with notable impact and recognition.
🔍 Research Focus 🌐
Within geophysical fluid dynamics and stochastic modeling, the collaborative research led by E. Mémin delves into diverse aspects. Their investigations span understanding turbulent flows (like channel flow and boundary layers) using stochastic models and data assimilation techniques. These endeavors explore various phenomena, such as surface waves, high-rise wind flow reconstruction, and uncertain environmental conditions affecting fluid dynamics. Methodologically, the research aims to develop advanced algorithms and mathematical frameworks for diagnosing, analyzing, and simulating complex geophysical systems. The ultimate goal lies in enhancing our comprehension of fluid behavior and its role in shaping environmental patterns through probabilistic and variational data-driven methodologies. 🌊✨
Peer Reviewer & Academic Engagements
Mr. Etienne Memin citation metrics and indices from Google Scholar are as follows:
Citations: 6043 (All), 1947 (Since 2019)
h-index: 43 (All), 25 (Since 2018)
i10-index: 96 (All), 50 (Since 2018)
Dense estimation and object-oriented segmentation of the optical flow with robust techniques Paper Published in 1996 Cited by 9
Dense motion estimation from eye-safe aerosol lidar data Paper Published in 2010 Cited by 9
Application of optical flow for river velocimetry Paper Published in 2017 Cited by 9
Wavelets to reconstruct turbulence multifractals from experimental image sequences Paper Published in 2011 Cited by 10
A Consistent Stochastic Large-Scale Representation of the Navier–Stokes Equations Paper Published in 2023 Cited by 10
Object‐oriented processing of CRM precipitation forecasts by stochastic filtering Paper Published in 2016 Cited by 11
Estimation of physical parameters under location uncertainty using an ensemble2–expectation–maximization algorithm Paper Published in 2019 Cited by 11
Stochastic flow approach to model the mean velocity profile of wall-bounded flows Paper Published in 2019 Cited by 12
Stochastic parametrization: An alternative to inflation in ensemble Kalman filters Paper Published in 2022 Cited by 12
Quantifying truncation-related uncertainties in unsteady fluid dynamics reduced order models Paper Published in 2021 Cited by 13