June 2020 – Present
Data scientist & digital services developer | Data Capita
I have mainly been developing a digital service to help auditing the accounting of potentially very large companies. It can process a huge amount of transactions in order to identify errors. A web interface is available for team work, with many features like access to financial statements, various indicators and graphs, writing and exporting conclusions, …
September 2007 – June 2020
Research scientist in applied mathematics | INRIA Paris
I worked on data assimilation (state estimation, inverse modeling), uncertainty quantification, online learning, ensemble forecasting, probabilistic forecasting, meta-modeling (Gaussian processes). My work was applied to high-dimensional complex environmental systems (air quality, wildland fires, meteorology, renewable energies, noise pollution, smart cities, road traffic).
I published about 55 papers in leading scientific journals, and about 25 conference papers. I supervised 9 PhD theses, and many master students, engineers and post-doctoral students.
December 2005 – August 2007
Research scientist in applied mathematics | École Nationale des Ponts et Chaussées
I was working on data assimilation (variational and sequential data assimilation) and ensemble forecasting in the context of air pollution forecasting, based on simulations of chemistry-transport models at continental and smaller scales.
2002 – 2005
PhD in applied mathematics
École Nationale des Ponts et Chaussées
“Uncertainty quantification and ensemble forecast with a chemistry-transport model”, under the supervision of Bruno Sportisse
1999 – 2002
Master of Science & Engineering degree in applied mathematics
École Centrale de Lyon
2001 – 2002
Master of Science in numerical analysis, PDEs and scientific computing
École Normale Supérieure de Lyon
English & French
Mathematics & data
Data assimilation and inverse modeling, variational assimilation, reduced Kalman filtering, minimax filtering.
Uncertainty quantification, sensitivity analysis (Sobol’), Monte Carlo, MCMC, ensemble scores, risk assessment.
Forecasting, online learning, sequential aggregation, expert selection or aggregation, filtering, probabilistic forecasts.
Machine learning, deep learning (neural networks), Gaussian processes, RBFs, statistical emulation.
Geostatistics, kriging, covariance modeling, interpolation in high dimension.
Python, Scipy, Pandas, Matplotlib, Plotly, Scikit-learn, Tensorflow, Flask, PyQt/PySide.
C++, scientific programming, MPI, OpenMP, Qt.
Regular use of MongoDB, PostgreSQL.
Occasional use of Lua, Lisp, Fortran.
Linux, Ubuntu, Debian, administration of a server, SSH.
Development of large software projects, experience with design and development of multiple libraries with over 50,000 lines of code
Development tools: Emacs, GCC, SCons, Git, SVN, CVS.
Other software: QGis, LaTeX.
Data sources: OpenStreetMap, ECMWF, NCEP/GFS, Copernicus CAMS, Landsat, Sentinel.
Environment, air quality, wildland fires, meteorology, renewable energies, noise pollution, smart cities, road traffic
Accounting and audit
9 PhD students
5 post-doctoral students, 13 master students
1 startup co-founded: Ambiciti
1 French patent (ensemble forecasting for renewable energies)