Past and Current Research

My current research focusses on solar information processing and machine learning. The goal is to fully exploit the large amounts of available space solar data for an improved understanding of solar physics phenomena. This calls for the development of automated feature recognition tools and more largely for a data science that is grounded in statistics, image processing, observational expertise, and solar physics in order to access physical information in the data that is often hidden due to their dynamical range, noise, or complexity and size of the dataset.

At Rice University, I worked on distributed estimation in sensor network. The aim was to find distributed estimation algorithms that are local, scalable, energy-consumption efficient, and fault-tolerant.

My doctoral research was concerned with the construction of wavelet transforms that automatically adapt to to the design at-hand, be it irregular, stochastic or auto-regressive. This allows to have a fast and efficient denoising method for nonparametric regression. Univariate and bivariate irregular designs were considered.


Solar physics

Sensor Networks

Wavelet methods

Conference Proceedings

PhD Thesis

Master Thesis

Last update: August 2017