Stéphane G. Mallat
Biography
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 2000 standard. With well over 100,000 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.
