David Donoho is a statistician whose work ranges from statistical computing to classical mathematical analysis.
His theoretical work includes the nonlinear recovery of signals despite massively incomplete data; recovery of curves from noisy statistical data in a theoretically optimum manner; and robust methods for treating severely contaminated high-dimensional data. Underlying this work is mathematics ranging from inequalities in the theory of entire functions to geometric properties of high-dimensional convex sets. Donoho has interests in large-scale statistical applications, particularly signal analysis in geophysical and medical problems. In presenting his findings, Donoho not only describes the intellectual underpinnings for his results, he also makes software available for others to learn from and expand his research. For example, he was a designer of MacSpin, a package that visualizes statistical data as rotating, three-dimensional point clouds; Donoho also coordinated the development of mathematical software tools that expand on his initial work in wavelet theory.
He is the Anne T. and Robert M. Bass Professor of Humanities and Sciences and a professor in the Department of Statistics at Stanford University.
Donoho received an A.B. (1978) from Princeton University and a Ph.D. (1984) from Harvard University.