I have been doing research in learnable signal processing since 2013, in particular with learnable parametrized wavelets which have then been extended for deep wavelet transforms. The latter has found many applications, e.g., in the NASA's Mars rover for marsquake detection. In 2016 when joining Rice University for a PhD with Prof. Richard Baraniuk, I broadened my scope to explore Deep Networks from a theoretical persepective by employing affine spline operators. This led me to revisit and improve state-of-the-art methods, e.g., batch-normalization or generative networks. In 2021 when joining Meta AI Research (FAIR) for a postdoc with Prof. Yann LeCun, I further enlarged my research interests e.g. to include self-supervised learning or biases emerging from data-augmentation and regularization leading to many publications and conference tutorials. In 2023, I have joined GQS, Citadel, to work on highly noisy and nonstationnary financial time-series and to provide AI solutions for prediction and representation learning. Such industry exposure is driving my research agenda to provide practical solutions from first principles which I have been pursuing every day for the last 10 years.
