Cédric Rommel
Machine Learning Scientist at Ava
I am a machine learning scientist at Ava working in speaker recognition research to help breaking communication barriers between the deaf & the hearing world. I just obtained a PhD (CIFRE) in Optimization and Statistical Learning from Ecole Polytechnique, which took place within the COMMANDS team from INRIA, at the Applied Mathematics Department of Ecole Polytechnique (CMAP), under the supervision of Frédéric Bonnans and Pierre Martinon. I was also greatly advised by the researchers from the french start up Safety Line, which funded my PhD. During my thesis I could develop practical solutions for identifying civil aircraft dynamical models using historical flights data, and use such models to reduce future flights fuel consumption and CO2 emissions through trajectory optimization (model-based reinforcement learning). I also hold a MSc. in engineering and applied mathematics from MINES ParisTech.
My main research interests are:
- Applications: Speech-to-text, speaker diarization, trajectory optimization.
- Statistical Learning: Density estimation, Functional data, Reinforcement Learning, Multi-task/Multi-output learning, feature selection, Regularized linear models, kernel methods.
- Control: Systems identification, Optimal control, Direct transcription, Dynamic Programming.
- Optimization: First order methods, nonsmooth nonconvex optimization, alternating minimization.
Ph.D. Thesis
- C. Rommel. Exploration de données pour l’optimisation de trajectoires aériennes (Data analysis for aircraft trajectory optimization), 2018. (manuscript, slides)
Preprints
- C. Rommel, J. F. Bonnans, B. Gregorutti, P. Martinon. Gaussian Mixture Penalty for Trajectory Optimization Problems, 2018. (preprint)
- C. Rommel, J. F. Bonnans, B. Gregorutti, P. Martinon. Quantifying the Closeness to a Set of Random Curves via the Mean Marginal Likelihood, 2018. (preprint)
- C. Rommel, J. F. Bonnans, B. Gregorutti, P. Martinon. Block sparse linear models for learning structured dynamical systems in aeronautics, 2017. (preprint)
Publications
- C. Rommel, J. F. Bonnans, B. Gregorutti, P. Martinon Aircraft dynamics identification for optimal control. 7th European Conference on Aeronautics and Space Sciences (EUCASS), 2017. (pdf here)
Poster
- June 25-26th 2018 • Paris • Data Science Summer School, Ecole Polytechnique • Quantifying the Closeness to a Set of Random Curves via the Mean Marginal Likelihood. (pdf here)
- September 14-15th 2017 • Paris • Junior Conference on Data Science and Engineering, Université Paris-Saclay • Aircraft Dynamics Identification. (pdf here)
- August 28th - September 1st 2017 • Paris • Data Science Summer School, Ecole Polytechnique • Aircraft Dynamics Identification. (pdf here)
- October 20-21th 2016 • Paris • Mathématiques, Oxygène du numérique, IHP • Exploration de données pour l'optimisation de trajectoires aériennes. ⇦Awarded best poster price !!
- October 4-5th 2016 • Paris • Workshop Transition Energetique, EDF • Exploration de données aéronautiques au service de la consommation de carburant.
Talks
- October 21st 2018 • Paris • PGMO Days 2017 • Multi-task Bolasso based aircraft dynamics identification. (slides)
- August 28th 2018 • Iasi • COMPSTAT 2018 • Quantifying the Closeness to a Set of Random Curves via the Mean Marginal Likelihood. (slides)
- July 2nd 2018 • Bordeaux • ISMP 2018 • Gaussian Mixture Penalization for Trajectory Optimization Problems. (slides)
- November 14th 2017 • Paris • PGMO Days 2017 • Multi-task Bolasso based aircraft dynamics identification. (slides)
- October 4th 2017 • Paris • CMAP 1st day meeting 2017 • Parametric estimator of civil aircraft dynamics.
- July 4th 2017 • Milan • EUCASS 2017 • Aircraft dynamics identification for optimal control.
- May 5th 2017 • Paris • CMAP PhD Seminar • Aircraft Dynamics Identification
- February 23rd 2017 • Paris • CMAP Journal Club • Presentation of: C. Ciliberto, A. Rudi, L. Rosasco. A consistent regularization approach for structured prediction. NIPS, 2016 (slides)