数学科学奖
2013

2013年度邵逸夫数学科学奖授予大卫・多诺霍，他是美国史丹福大学的 Anne T and Robert M Bass 人文学讲座教授和统计学教授。他对现代数理统计学作出了深远的贡献：他开创了在有噪声情况的最优统计估计算法；而他又建立了在大数据中实现稀疏表示和复原的高效率技巧。

在过去的半个世纪，计算技术出现了戏剧性的进步，给数理统计学的理论和应用带来了根本性的新挑战。大卫・多诺霍在这个领域举足轻重，他开发了新颖的数学和统计工具，以处理高维大型数据、噪声污染数据等问题。他以严格数学分析为根基，建立了快速、高效且通常是最优的算法。

For more than two decades David Donoho has been a leading figure in mathematical statistics. His introduction of novel mathematical tools and ideas has helped shape both the theoretical and applied sides of modern statistics. His work is characterized by the development of fast computational algorithms together with rigorous mathematical analysis for a wide range of statistical and engineering problems.

A central problem in statistics is to devise optimal and efficient methods for estimating (possibly non-smooth) functions based on observed data which has been polluted by (often unknown) noise. Optimality here means that, as the sample size increases, the error in the estimation should decrease as fast as that for an optimal interpolation of the underlying function. The widely used least square regression method is known to be non-optimal for many classes of functions and noise that are encountered in important applications, for example non-smooth functions and non-Gaussian noise. Together with Iain Johnstone, Donoho developed provably almost optimal (that is, up to a factor of a power of the logarithm of the sample size) algorithms for function estimation in wavelet bases. Their “soft thresholding” algorithm is now one of the most widely used algorithms in statistical applications.