Mark Girolami is the Sir Kirby Laing Professor of Civil Engineering (1965) within the Department of Engineering at the University of Cambridge where he also holds the Royal Academy of Engineering Research Chair in Data Centric Engineering. In this role he will provide academic leadership for the Centre for Digital Built Britain across the University and more broadly throughout the national and international research communities. Prior to joining the University of Cambridge he held the Chair of Statistics in the Department of Mathematicsat Imperial College London.
He was one of the original founding Executive Directors of the Alan Turing Institute the UK’s national institute for Data Science and Artificial Intelligence, after which he was appointed as Strategic Programme Director at Turing, where he established and continues to lead the Lloyd’s Register FoundationProgramme on Data Centric Engineering.
Professor Girolami is an elected fellow of the Royal Society of Edinburgh, he was an EPSRC Advanced Research Fellow (2007-2012), an EPSRC Established Career Research Fellow (2012-2018), and a recipient of a Royal Society Wolfson Research Merit Award.
He delivered the IMS Medallion Lecture at the Joint Statistical Meeting 2017, and the Bernoulli Society Forum Lecture at the European Meeting of Statisticians 2017.
In 2020 Professor Girolami will deliver the BCS and IET Turing Talk in London, Manchester, and Belfast.
Professor Girolami currently serves as the Editor-in-Chief of Statistics and Computing and the new open access journal Data Centric Engineeringpublished by Cambridge University Press.
Mike Giles is a Professor of Scientific Computing at the University of Oxford. His research focuses on improving the accuracy, efficiency and analysis of Monte Carlo methods. A particular highlight has been the development and numerical analysis of multilevel Monte Carlo methods which are now being used widely. He is interested in high performance parallel computing, and for almost 10 years has been working on the exploitation of GPUs (graphics processors) for a variety of financial, scientific and engineering applications. His research in ICONIC will cover both of these aspects, looking at the development and analysis of efficient Monte Carlo solution methods for the models developed by Mark and Des, and also mixed precision and parallel computing implementations on GPUs and other many-core processors such as Intel’s latest Xeon CPUs.
Des Higham is the 1966 Chair of Numerical Analysis at the University of Edinburgh. He is an EPSRC/RCUK Digital Economy Established Career Fellow. Des is a Fellow of the Royal Society of Edinburgh, a SIAM Fellow, and recently held a Royal Society Wolfson Research Merit Award. Des has experience in stochastic modelling and computation, and has been involved in extending the scope of Mike’s multilevel Monte Carlo techniques to discrete space models. He will be working with Mark on modelling and inference for urban behaviour, notably crime, and with Nick on some of the underlying issues in matrix computation.
Nick Higham is Royal Society Research Professor and Richardson Professor of Applied Mathematics at the University of Manchester. His research focuses on the development of algorithms, primarily in numerical linear algebra, and analysis of their accuracy and stability. In ICONIC his research topics will include functions of matrices, rounding error analysis, and matrix nearness and completion problems. In particular, he will work with Mike to develop effective mixed precision algorithms that exploit GPUs other many-core processors. Nick is a Fellow of the Royal Society, a SIAM Fellow, and a Member of Academia Europaea. He publishes a blog about applied mathematics.
Pierre Blanchard (University of Manchester 2017-2019) obtained his PhD in Applied Mathematics and Scientific Computing from the University of Bordeaux, France, in 2017. A theme of his research to date has been hierachical representation of matrices, as applied to various domains of scientific computing, including seismic wave scattering, materials sciences and geostatistics.
Theo Mary (University of Manchester 2018-2019) obtained his PhD in Applied Mathematics and Computer Science from the University of Toulouse, France, in 2017. His research to date has focused on exploiting low-rank approximations inside direct, factorization-based methods to reduce the computational cost of the solution of sparse linear systems arising in several applications, including seismic imaging, electromagnetic modeling, and structural mechanics.
Srikara Pranesh (University of Manchester 2019-) obtained his PhD from the department of Civil Engineering at the Indian Institute of Science, Bangalore in 2018. During his PhD his research focused on developing efficient numerical algorithms for large scale uncertainty quantification. Currently his research interests include High Performance Computing, Multi-Precision Algorithms, and numerical aspects of Uncertainty Quantification.
Seppo Virtanen (The University of Cambridge) works at the intersection between statistical inference and mathematical modeling. His research interests include multi-view learning, latent variable modeling and approximate Bayesian inference. His recent work focuses on dynamic models of text documents.
Louis Ellam (Imperial College London) was a research assistant working on stochastic modelling and statistical inference for urban systems and is completing his PhD in Statistics at Imperial College London. His research to date has focused on the development of new Bayesian methodologies for inverse problems, and has included high-dimensional Gaussian models, elliptic PDEs and singly-constrained dynamic urban models. He had now taken up a lectureship at DeMontfort University and left the ICONIC project.
Wei Fang (University of Oxford) is a research assistant working on stochastic modelling and statistical inference for urban systems and is completing his PhD in Mathematics at University of Oxford. His research has focused on the development of new adaptive Multilevel Monte Carlo method to solve the stochastic differential equations with non-globally Lipschitz drift numerically in the finite time interval and compute the expectations and sensitivities with respect to the invariant measure for the ergodic SDEs in the infinite time interval.
Craig Gilmour (University of Strathclyde) is a research assistant working on crime prediction, and is completing his PhD in Mathematics at University of Strathclyde. His research to date has focused on applying self-exciting point processes in the context of urban crime.
Associated Research Students
Jan Povala (Imperial College London) is a DPhil student working on crime prediction and statistical inference using spatio-temporal log-Gaussian Cox processes. His project is part of the Financial Computing CDT at Imperial College London.
Oliver Sheridan-Methven (University of Oxford) is a DPhil student working on the use of high performance low/mixed precision vectorised computer arithmetic for stochastic simulations. His project is sponsored by ARM and NAG and is part of the InFoMM CDT.