Mark Girolami is an EPSRC Established Career Research Fellow (2012 - 2018) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is the Director of the £10M Lloyds Register Foundation - Turing Programme on Data Centric Engineering and previously led the EPSRC funded Research Network on Computational Statistics and Machine Learning. In 2011 he was elected to the Fellowship of the Royal Society of Edinburgh when he was also awarded a Royal Society Wolfson Research Merit Award.
He was one of the founding Executive Directors of the Alan Turing Institute for Data Science from 2015 to 2016 before taking leadership of the Data Centric Engineering Programme at The Alan Turing Institute.
He delivered the IMS Medallion Lecture at JSM 2017.
He delivered the Bernoulli Society Forum Lecture at the European Meeting of Statisticians 2017.
His research and that of his group covers the investigation and development of advanced novel statistical methodology driven by applications in the life, clinical, physical, chemical, engineering and ecological sciences. He also works closely with industry where he has several patents leading from his work on e.g. activity profiling in telecommunications networks and developing statistical techniques for the machine based identification of counterfeit currency which is now an established technology used in current Automated Teller Machines. He has worked as a consultant for the Global Forecasting Team at Amazon in Seattle
Gaussian Process Summer School on UQ - September 2016, Sheffield, Lectures plus one I delivered
PhD student Francois-Xavier Briol jointly awarded Best Student Paper 2016 by the ASA Section on Bayesian Statistical Science for his paper on Probabilistic Integration: A Role for Statisticians in Numerical Analysis?. A couple of Blogs on the paper from Andy Gelman and Christian Robert.
He was joint Chair of the The Sixth IMS-ISBA Joint Meeting on Bayesian Computation
Probabilistic Numerics - what all the cool kids are up to these days.
NEW: - C.J..Oates, J.Cockayne, F-X.Briol, M.A.Girolami (2018). Convergence Rates for a Class of Estimators Based on Stein's Identity. Bernoulli (to appear).
New:- C.J.Oates, S.Niederer, A.Lee, F-X.Briol, M.A.Girolami. (2018). Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models, NIPS.
New:- M.M.Dunlop, M.A.Girolami, A.M.Stuart, A.L.Teckentrup. (2017). How Deep Are Deep Gaussian Processes?
New:-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).
Mike Betancourt, Simon Byrne, Sam Livingstone, and Mark Girolami (2017) The Geometric Foundations of Hamiltonian Monte Carlo, Bernoulli, 23(4A), 2257 - 2298, 2017. DOI: 10.3150/16-BEJ810.
K. Jensen, C. Soguero-Ruiz, K.O Mikalsen, R-O. Lindsetmo, I. Kouskoumvekaki, M. Girolami, S.O Skrovseth, and K.M. Augestad. Analysis of Free Text in Electronic Health Records for Identification of Cancer Patient Trajectories. Nature Scientific Reports, 2017. doi:10.1038/srep462262017
A. Beskos, M. Girolami, S. Lan, P.E. Farrell, A.M. Stuart. Geometric MCMC for Infinite-Dimensional Inverse Problems. Journal of Computational Physics, Volume 335, Pages 327–351, 2017.
V. Stathopoulos, V. Zamora-Gutierrez, K. Jones, M. Girolami. Bat Echolocation Call Identification for Biodiversity Monitoring: A Probabilistic Approach. To appear, Journal of the Royal Statistical Society - Series C, 2017, doi:10.1111/rssc.12217.
Oates, C., Girolami, M. and Chopin, N. Control Functionals for Monte Carlo Integration. Journal of Royal Statistical Society - Series B, Volume 79, Issue 3, Pages 695–718, 2017.
L.Ellam, N. Zabaras, M. Girolami. A Bayesian Approach to Multiscale Inverse Problems with On-the-fly Scale Determination. Journal of Computational Physics, 326, 115-140, 2016.
O.A.Chkrebtii, D.A.Campbell, B.Calderhead, M.A.Girolami. Bayesian Solution Uncertainty Quantification for Differential Equations. Bayesian Analysis, Vol. 11, Number. 4, Pages 1239-1267. with Discussion, 2016.
J.M. Rondina, M. Filippone, M. Girolami, N.S. Ward. Decoding Post-Stroke Motor Function from Structural Brain Imaging. NeuroImage: Clinical,12, 372-380, 2016.
M. Epstein., B. Calderhead., M. Girolami., L.G. Sivilotti. (July 2016). Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Events Correction. Biophysical Journal, Vol. 111, Issue. 2, pp 333-348, 2016.
This paper received a New and Notable discussion from Prof. F. Ball. MCMC for Ion-Channel Sojourn-Time Data: A Good Proposal, Biophysical Journal, Vol. 111, Issue. 2, Pages 267–268, 2016.
T.House, A.Ford, S.Lan, S. Bilson, E. Buckingham-Jeffery, M.A.Girolami. (August 2016) Bayesian Uncertainty Quantification for Transmissability of Influenza, Norovirus, and Ebola using Information Geometry. Journal of the Royal Society Interface, DOI: 10.1098/rsif.2016.0279
C.J.Oates, F-X.Briol, M. Girolami. (July 2016) Probabilistic Integration and Intractable Distributions
S. Rogers and M. Girolami. (Summer 2016) A First Course in Machine Learning, Second Edition, CRC Press.
J. Cockayne, C. Oates, T. Sullivan. M. Girolami. (May 2016) Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems
P. Conrad, MAG, S.Sarkka, A.M.Stuart, K.Zygalkis. (May 2016) Probability Measures for Numerical Solutions of Differential Equations, Statistics and Computing, 27:1065–1082, 2016.
Banushi. B., ..., 27 Authors later... Girolami, M., Bozec, L., Mills, K., Gissen, P., Regulation of Post-Golgi LH3 Trafficking is Essential for Collagen Homeostasis, Nature Communications 7, Article number: 1211, 2016, doi:10.1038/ncomms12111.
Moores, M., Gracie, K., Carson, J., Faulds, K. Graham, D., Girolami, M. (April 2016) Bayesian Modelling and Quantification of Raman Spectroscopy.
Briol, F-X., Oates, C. J., Girolami, M., Osborne, M. A. & Sejdinovic, D. (April 2016). Probabilistic Integration: A Role for Statisticians in Numerical Analysis? [Student Paper award 2016 from the Section on Bayesian Statistical Science of the ASA] [blog post by A. Gelman][blog post by C. Robert]
Jake Carson, Murray Pollock, Mark Girolami (March 2016) Unbiased local solutions of partial differential equations via the Feynman-Kac Identities
Sam Livingstone, Mike Betancourt, Simon Byrne and Mark Girolami (January 2016) "On the Geometric Ergodicity of Hamiltonian Monte Carlo"
Seppo Virtanen, Mattias Rost, Alistair Morrison, Matthew Chalmers, and Mark Girolami. (2016) Uncovering smartphone usage patterns with multi-view mixed membership models. Stat.
Oates CJ, Girolami M. (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), [JMLR Proc] [Selected for Oral Presentation]
Gracie, K; Moores, M; Smith, W; Harding, K; Girolami, M; Graham, D; Faulds, K. (2016) Preferential Attachment of Specific Fluorescent Dyes and Dye Labelled DNA Sequences in a SERS Multiplex. Analytical Chemistry, 88 (2), pp 1147–1153, 2016.
Shiwei Lan, Tan Bui-Thanh, Mike Christie, Mark Girolami (2016). Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems, Journal of Computational Physics Vol. 308, 81 - 101.
Briol, F-X., Oates, C. J., Girolami, M., & Osborne, M. A. (2015). Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances In Neural Information Processing Systems (NIPS) 2015.
Probabilistic Numerics and Uncertainty in Computations. Hennig, P., Osborne, M. A., & Girolami, M. Proceedings of the Royal Society A, Proc. R. Soc. A 2015 471 20150142; DOI: 10.1098/rspa.2015.0142.
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods with Anne-Marie Lynne, Heiko Strathmann, Daniel Simpson and Yves Atchade. Statistical Science, Volume 30, Number 4 (2015), 443-467, 2015.
Seppo Virtanen, Mattias Rost, Matthew Higgs, Alistair Morrison, Matthew Chalmers and Mark Girolami. Non-parametric Bayes to infer playing strategies adopted in a population of mobile gamers (pages 46–58)
STAT, Article first published online: 4 MAR 2015 | DOI: 10.1002/sta4.75
New paper from ASSET team - exploiting chemical kintetic models and computing Bayes factors to study how EWS-FLI1 employs an E2F switch to drive target gene expression - Nucleic Acids Research.
Bui, T. and Girolami, M. "Solving Large-Scale PDE-constrained Bayesian Inverse problems with Riemann Manifold Hamiltonian Monte Carlo", Inverse Problems, 30, 114014, doi:10.1088/0266-5611/30/11/114014.
Filiponne, M. and Girolami, M. "Pseudo-Marginal Bayesian Inference for Gaussian Processes", IEEE Transactions Pattern Analysis and Machine Intelligence, 36(11), 2214-2226, 2014.
Kramer A, Stathopoulos V, Girolami M, Radde N. MCMC_CLIB–an advanced MCMC sampling package for ODE models, Bioinformatics (2014) 30 (20): 2991-2992.
Book Chapter with Des Higham and Ben Calderhead on.... Zombies
Department of Statistics
The University of Warwick
CV4 7AL, Coventry
Email address M dot Girolami at warwick dot ac dot uk