Advanced novel statistical methodology with diverse applications in science and engineering.
The Inference Group
The Inference Group at Imperial is part of the Statistics Section of the Mathematics Department and is led by Professor Mark Girolami. The activities of the group covers the investigation and development of advanced novel statistical methodology, driven by applications in the physical, chemical, engineering and socio-economic sciences.
- June 2018: Alex Terenin was awarded the prize for the best poster at the Imperial College London SIAM Student Conference.
- June 2018: Our group has been acknowledged by the STARTS Prize winners for collaborations on the world’s first 3-D printed metal bridge project.
- April 2018: Our paper Stochastic modelling of urban structure has been published in Proceedings of the Royal Society A.
- April 2018: Mark Girolami to lead a five-year project Bridging big data and engineering.
- April 2018: Mark Girolami has been appointed the Lloyd’s Register Foundation / Royal Academy of Engineering Research Chair in Data-Centric Engineering.
- MURI: Semantic Information Pursuit for Multimodal Data Analysis
- ICONIC: Inference Computation and Numerics for Insights into Cities
L. Ellam, G. Pavliotis, M. Girolami, A. Wilson (2018), Stochastic modelling of urban structure, Proc. R. Soc. A, rspa.2017.0700, 2018.
L. Ellam, H. Strathmann, M. Girolami, I. Murray (2017). A determinant-free method to simulate the parameters of large Gaussian fields. Stat DOI: 10.1002/sta4.153.
F-X Briol, C.J. Oates, J. Cockayne, W.Y. Chen, and M.A. Girolami (2017). On the Sampling problem for Kernel Quadrature. International Conference on Machine Learning (ICML).
K. Jensen, C. Soguero-Ruiz, K.O Mikalsen, R-O. Lindsetmo, I. Kouskoumvekaki, M. Girolami, S.O Skrovseth, and K.M. Augestad (2017). Analysis of Free Text in Electronic Health Records for Identification of Cancer Patient Trajectories. Nature Scientific Reports, doi:10.1038/srep462262017
A. Beskos, M. Girolami, S. Lan, P.E. Farrell, A.M. Stuart (2017). Geometric MCMC for Infinite-Dimensional Inverse Problems. Journal of Computational Physics, Volume 335, Pages 327–351,