Mark Girolami

Mark A. Girolami
Born	1963 United Kingdom
Occupation(s)	Civil engineer, statistician, data engineer, academic
Years active	1985–present
Known for	Sir Kirby Laing Professorship of Civil Engineering at the University of Cambridge; Chief Scientist at the Alan Turing Institute; Fellow at Christ's College, Cambridge; Royal Society Wolfson Research Merit Award recipient

Mark A. Girolami (born 1963) is a British expert in civil engineering, statistics, and data engineering. Since 2019, he has been appointed to the Sir Kirby Laing Professorship of Civil Engineering within the Department of Engineering at the University of Cambridge. Beginning in 2021, Girolami has taken on the role of chief scientist at the Alan Turing Institute. He is also a Fellow at Christ's College, Cambridge. His distinguished career includes receiving a Royal Society Wolfson Research Merit Award. Girolami played a key role as a founding editor for the journal Data-Centric Engineering and previously served as the program director for data-centric engineering initiatives at the Turing Institute.

Education

Girolami completed his studies at the University of Glasgow and subsequently worked as an engineer at IBM from 1985 until 1994. Concurrently, he undertook a part-time PhD focused on Statistical Signal Processing at the University of the West of Scotland. In recognition of his significant contributions to engineering and computing, he was honored with an honorary doctorate by the University of the West of Scotland in 2024.

Career and research

Following the completion of his doctoral studies, Girolami held senior academic positions at both the University of Glasgow and University College London. Before his current tenure at the University of Cambridge, he was affiliated with Imperial College London.

Selected publications

Among his published works are:

• Self-organising neural networks: independent component analysis and blind source separation (1999)
• Advances in independent component analysis (2000)
• Learning and inference in computational systems biology (2009)
• Handbook of statistical systems biology (2011)
• A first course in machine learning (2nd ed., 2020)

His prominent journal articles address subjects including independent component analysis, clustering methods based on Mercer kernels, Riemann Manifold Langevin and Hamiltonian Monte Carlo techniques, as well as probabilistic integration within statistical computation.