Biography and publications
Stéphane Mallat was born onOctober24 He graduated from the École polytechnique in , went on to obtain a Ph.D. from the University of Pennsylvania in , and defended his habilitation thesis in mathematics at the Université de Paris-Dauphine in
He was Professor of Mathematics and Computer Science at New York University's Courant Institute from to , then returned to France as Professor of Applied Mathematics at École Polytechnique until He chaired the same department from to In , he co-founded a start-up, Let it Wave, which he ran until He became a professor at the École normale supérieure de la rue d'Ulm from to , then was appointed professor at the Collège de France in , holding the Data Sciencechair.
Stéphane Mallat's research focuses on mathematics applied to signal processing and statistical learning. In particular, he introduced multiresolution theory to build wavelet bases, as well as the fast wavelet transform, from which the JPEG image compression standard was derived. He is the originator of parsimony representations by matching pursuit in dictionaries, for data processing. He is now working on the mathematical modeling of n
MALLAT Stéphane
Short bio
Professor at NYU from to Professor at Ecole Polytechnique, from to Co-founder and CEO of a semiconductor start-up from to Professor in Computer Science at Ecole Normale Supérieure from to Professor at the Collège de France in Data Sciences since Member of the French Academy of sciences, of the French Academy of Technologies and foreign member of the US National Academy of Engineering. IEEE and EUSIPCO Fellow. Recipient of the SPIE Outstanding Achievement Award, of the European IST Grand prize, of the INIST-CNRS prize for most cited French Researcher in engineering, of the IEEE Signal Processing best sustaining paper award, of the IEEE Freidrich Gauss Prize.
Topics of interest
Harmonic analysis, machine learning, signal processing
Project in Prairie
Stéphane Mallat will be working on the mathematical understanding and interpretability of deep neural networks with applications to images, audio, financial data, quantum chemistry and cosmology. He teaches Data Sciences and will promote the interface between industry, academia and students through the organization of data challenges in
Quote
Deep neural networks have considerable applications to ser
Data science
Processing data to validate a hypothesis or estimate parameters has long been the exclusive domain of statistics. However, as the dimensions of data have increased, the Combinatorics of possibilities has exploded. This curse of dimensionality is a central difficulty in data analysis, be it images, sounds, texts, or experimental measurements as in physics, biology or economics. Modeling and representing the hidden structures of data calls on various branches of mathematics, but also on computer science. Statistical learning algorithms, such as neural networks, are configured to optimize the analysis of data based on examples. They are behind the spectacular results of artificial intelligence. Their scientific, industrial and societal applications are considerable, and their performance is progressing much faster than our mastery of their mathematical properties.
The Chair offers lectures in applied mathematics, bridging the gap between the jungle of new algorithmic developments and an understanding of the underlying general principles. Applications cover all aspects of signal processing and statistical learning. In addition to statistics and probability, harm
Stéphane G. Mallat
IEEE FOURIER AWARD FOR SIGNAL PROCESSING
Sponsored by Mitsubishi Electric Research Labs (MERL)
“For contributions to the theory and applications of wavelets and machine learning.”
Stéphane Mallat is the most influential scientist in signal processing and applied mathematics of the past several decades. He introduced sparse representations over potentially nonstructured and redundant families of patterns, which is now an entirely new field, with many applications related to image processing, statistics, compressive sensing, and machine learning. He developed the multiresolution wavelet theory and the fast wavelet transform, which fundamentally improved image compression and noise removal in images. His work has had real-world impact in areas from seismic to health data, and in image compression with the widely used JPEG standard. With well over , Google Scholar citations, Mallat’s work has been transformational across all of science and technology.
An IEEE Fellow, Mallat is a Professor and Chair of Data Sciences, Collège de France, Paris, France.
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