Nick Higham is Royal Society Research Professor and Richardson Professor of Applied Mathematics in the School of Mathematics at the University of Manchester. He served as President of the Society for Industrial and Applied Mathematics (SIAM), 2018-2019. Much of his research is concerned with the accuracy and stability of numerical algorithms, and the second edition of his monograph on this topic was published by SIAM in 2002.

His other books include Functions of Matrices: Theory and Computation (SIAM, 2008), the first ever research monograph on matrix functions, and the 1000-page The Princeton Companion to Applied Mathematics (2015), of which he was editor.

Higham has contributed software to LAPACK and the NAG library, and has written numerous M-files included in MATLAB. His algorithms are also included in Julia, SciPy, Mathematica and other packages.

He is a Fellow of the Royal Society, a SIAM Fellow, and a Member of Academia Europaea. Honours include a 1999 Junior Whitehead Prize and the 2008 Fröhlich Prize, both from the London Mathematical Society, and he held a Royal Society-Wolfson Research Merit Award (2003--2008). He blogs about applied mathematics at

Research interests

Higham develops fundamental theory and algorithms that enable reliable and efficient solution of practically-relevant problems. He is working on developing a new generation of numerical linear algebra algorithms that exploit current and future computers. The algorithms will be fast and will be accompanied by rigorous error analysis to guarantee their reliability. The target problems are linear equations, linear least squares problems, eigenvalue problems, the singular value decomposition and matrix function evaluation. These are the innermost kernels in many scientific and engineering applications—in particular, in data science and in machine learning—so it is essential that they are fast, accurate, and reliable. 

A particular aspect of this work is the exploitation of low precision arithmetic, which is now available in hardware and is increasingly being used in machine learning and scientific computing more generally. The use of low precision raises questions about the reliability of computations (provably getting the right results).