Cédric Rommel
About Cédric Rommel
Cédric Rommel is a Postdoctoral Researcher at Inria with a background in machine learning and optimization. He has held various positions in academia and industry, contributing to projects such as OptiClimb for fuel-efficient flights.
Work at Inria
Cédric Rommel has been working at Inria as a Postdoctoral Researcher since 2020. His role involves conducting advanced research in machine learning and optimization, contributing to various projects within the organization. Inria is known for its focus on digital science and technology, providing an environment for researchers to collaborate and innovate.
Previous Experience at MINES ParisTech
Before joining Inria, Cédric Rommel worked at MINES ParisTech as a Teaching Assistant for seven months in 2021 and 2022. His responsibilities included supporting students in their learning processes and assisting in the delivery of course content. MINES ParisTech is recognized for its engineering and management programs.
PhD and Research Contributions
Cédric Rommel completed his PhD in Machine Learning and Optimization from 2015 to 2018. His research focused on data-driven optimal control, which led to the development of OptiClimb, a product that computes fuel-efficient flight paths. This work has had a global impact on aviation efficiency.
Educational Background
Cédric Rommel studied at Ecole polytechnique, where he specialized in Machine Learning and Optimization, achieving his PhD. He also holds a Master's degree in Sciences & Executive Engineering from MINES ParisTech, where he focused on Machines and Energy. His foundational education includes classes at Lycée Hoche, where he completed preparatory classes for engineering schools.
Research Focus and Projects
Cédric Rommel's current research involves learning optimal data augmentations directly from data, which is crucial for applications with limited labeled data. He has co-supervised a master's student internship that concentrated on data augmentation for electroencephalography, particularly in sleep stage classification from brain signals.