David L. Donoho

From ETHW

Biography[edit source]

David L. Donoho's groundbreaking work in sparse signal recovery and compressed sensing revolutionized signal processing and helped change the way engineers think about data acquisition, profoundly impacting fields ranging from wireless communications to medical imaging. Donoho's early work on blind deconvolution showed that sufficiently non-Gaussian signals (sparse signals) can be recovered despite blurring by an unknown filter, which has been applicable to oil exploration, image processing, and wireless communications. He introduced the celebrated wavelet shrinkage algorithm with Iain Johnstone, which became one of the most important methods for separating sparse signals from noise. This work has very concrete significance for signal estimation and has impacted a number of applied fields, including astronomy. Donoho realized that transforming digital data using the wavelet transforms and other tools from applied harmonic analysis revealed that sparsity was everywhere in images and other media we routinely use and that enhanced sparsity leads to enhanced estimation, giving us far sharper signals and images to work with. His work on compressed sensing demonstrated that one can exploit sparsity or compressibility when acquiring signals of general interest, and that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data. The medical imaging research community has found ways to use the technology to speed up and improve the quality of medical imaging for millions of patients. Compressed sensing has impacted magnetic resonance imaging (MRI) by enabling scan times to be accelerated ten-fold, and a new generation of MRI scanners based on this technology has entered clinical use. Compressed sensing is also being used to improve radio intelligence gathering capability by orders of magnitude, which has impacted the development of radio-frequency sensing and spectral applications over bandwidths exceeding multiple GHz for scientific instrumentation and electronic intelligence.

An IEEE Fellow and member of the U.S. National Academy of Science, Donoho is the Anne T. and Robert M. Bass Professor of Humanities and Sciences and professor of statistics with the Department of Statistics at Stanford University.