I remember the chalk dust, slate blackboards and marble hallways of [my father's] academic office, and the lasers and low-temperature gadgets in his laboratory. Paul took our family on sabbatical to Grenoble, France, where I attended 6th grade - a transforming educational experience.

Also, seeing another culture and learning another language was very important.

I used three "statistics packages" while a Princeton undergraduate. P-STAT was an SPSS-like mainframe package which I used on Princeton's IBM 360/91 Mainframe; ISP was a UNIX minicomputer package on which I worked as a co-developer for the Princeton Statistics Department; and my teacher Don McNeil had developed software for a book of his own on exploratory data analysis; this ultimately became SPIDA after he moved to Macquarie University.

He was a very stimulating and interesting man and you could have many interesting conversations with him over tea. He was very interested in computers at the time and decided that the ideas of John Tukey, who had been my undergraduate advisor, were really the right ones, that you should be visualising data, using computer graphics. Peter was very interested in getting modern computer graphics to visualise data. I worked with my other co-students on various computer graphics. At the same time I couldn't stay away from ideas I had learnt about in my industrial research.

... exhibits coordinate-free estimators of multivariate location which can withstand up to 50% contamination of the data before breaking down. These include affine- equivariant generalisations of trimmed means and W-estimates, and a new minimum-distance estimator.

... describes the design and implementation of a compact data analysis language. The uses of the language for constructing advanced systems for multivariate graphics are described.

At Berkeley during my postdoctoral and junior faculty years (1985-1990), I was in the Mecca of classical mathematical statistics, but I pursued my 'non-classical' interests. Iain Johnstone and I showed how to optimally 'denoise' sparse signals observed in noise, injecting 'sparsity' into top statistics journals. A sparse signal sticks up here and there above the noise, like daisies above weeds; our denoiser, based on $l_{1}$-minimization, chops away the weeds while leaving the daisies. Jeff Hoch and Alan Stern successfully applied such ideas in Magnetic Resonance spectroscopy. To publish 'non-classical' work on undersampled measurements of sparse signals, I turned to applied mathematics journals.

David Donoho has studied the exploitation of sparse signals in signal recovery, including for denoising, superresolution, and solution of underdetermined equations. He coined the notion of compressed sensing which has impacted many scientific and technical fields, including magnetic resonance imaging in medicine, where it has been implemented in FDA-approved medical imaging protocols and is already used in millions of actual patient MRIs. In recent years he has been studying large-scale covariance matrix estimation, large-scale matrix denoising, detection of rare and weak signals among many pure noise non-signals, compressed sensing and related scientific imaging problems, and most recently, empirical deep learning.

The company's initial product was a user self-service system management application that was marketed to Original Equipment Manufacturer PC manufacturers. In 2002, the company developed BigFix software for company-wide networked desktop, mobile and server computer management. This shifted the focus of the company from consumer to enterprise markets. Initial uses centred on security patch management, which was identified as a significant pain point at that time for enterprise IT departments. In 2007, BigFix expanded to specific management areas: security threat suppression (BigFix Endpoint Protection), enterprise security configuration management (BigFix Security Configuration and Vulnerability Management) and enterprise systems management (BigFix Systems Lifecycle Management).

... co-founder of BigFix.com, a Berkeley, CA start-up aiming to fundamentally change the practice of technical support for computers and associated network-connected devices. As disclosed in several US Patents, BigFix has developed a new internet communications model with profound implications for the high tech sector, implications reaching far beyond the initial applications in technical support.

... explained that, because the wavelet transform sparsifies images, images can be recovered from relatively few random measurements via $l_{1}$ minimization.

... for introducing novel fundamental and powerful mathematical tools in signal processing and image analysis. His many outstanding contributions include those to compressed sensing and the construction of multiscale analysis techniques that take advantage of the specific mathematical and physical properties of the problems under consideration. His methods are very deep mathematically and very efficient computationally. This explains their success with both theoreticians and practitioners, which causes him to be one of the most cited applied and computational mathematicians of our time.

... for his profound contributions to modern mathematical statistics and in particular the development of optimal algorithms for statistical estimation in the presence of noise and of efficient techniques for sparse representation and recovery in large data-sets.

... for his fundamental contributions to the mathematical, statistical and computational analysis of signal processing.

... for groundbreaking contributions to sparse signal recovery and compressed sensing.

Matan Gavish and David Donoho, Stanford University, received third prize for "A Universal Identifier for Computational Results". The Universal Identifier is a specific implementation of one aspect the executable paper - an ID resolution system for results. The proposed solution is simple and elegant, and confers ease of use by adding one or two lines of code.

On 9 October David L Donoho received the honorary degree of Doctor of Science from The University of Chicago. The occasion was a special convocation, the 500th in the university's history, and Donoho's was one of four degrees awarded. In presenting the degree, the university's president - mathematician Robert Zimmer - said, "David L Donoho is a mathematical statistician and also one of the more influential applied mathematicians of his generation. Building upon the discipline of statistics, Donoho has developed effective new approaches to constructing low-dimensional representations for modern high-dimensional data problems. His work provides new insight into some of the most pressing scientific questions of the present day."

There are things I've done decades ago, and when I see things happen in the real world, it makes me so proud. The power we have in moving the world gives me a great deal of satisfaction in my career choice. ... There are so many relations between mathematics and the rest of the world. We see more and more relations over time; so much in the modern world is underpinned by mathematics, for example smartphones, and the vast level of mathematical fundamentals intertwined, such as prime factorisation.

David Donoho is a modern polymath who informs himself about everything: science, engineering, policy making ..., tries to understand what people are doing successfully, and develops insightful mathematics - to understand why things work/don't work - to create new models for data - to create new processing algorithms - and to ultimately inspire real concrete improvements; e.g. in human health.

David Donoho is a 'physicist of information' who is a deep mathematical thinker who tirelessly informs himself about what others are doing successfully. This requires extra effort, it is not easy. He sets the course/agenda and is in the lineage of intellectual giants like Euler, Gauss, Poincaré, Wiener, Kolmogorov, and von Neumann.