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Professor Mark Girolami

Mark Girolami is an EPSRC Established Career Research Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is the Director of the EPSRC funded Research Network on Computational Statistics and Machine Learning and in 2011 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.

He has been nominated by the IMS to deliver a Medallion Lecture at JSM 2017.

He has been invited to give a 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. At present he works as a consultant for the Global Forecasting Team at Amazon in Seattle

Full Curriculum Vitae

BBC Television interview on our work with Biodiversity scientists May 2016

Turing Lecture : Probabilistic Numerics: A New Concept? July 2016

News: Dobbiaco Summer School, Probabilistic Numerics, 2017

News: Gaussian Process Summer School on UQ - September 2016, Sheffield, Lectures plus one I delivered

News: PN Session at MCQMC August 2016

News: 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.

EPSRC Network on Computational Statistics and Machine Learning

Recent Publications

New:- A. Beskos, M. Girolami, S. Lan, P.E. Farrell, A.M. Stuart. Geometric MCMC for Infinite-Dimensional Inverse Problems. To appear Journal of Computational Physics, 2017.

New:- 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.

New:- Oates, C., Girolami, M. and Chopin, N. Control Functionals for Monte Carlo Integration. To appear Journal of Royal Statistical Society - Series B, 2017.

New:- 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.

New:- 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.

New:- 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.

New:- 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.

New:- 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

New:- C.J.Oates, F-X.Briol, M. Girolami. (July 2016) Probabilistic Integration and Intractable Distributions

New:- S. Rogers and M. Girolami. (Summer 2016) A First Course in Machine Learning, Second Edition, CRC Press.

New:- J. Cockayne, C. Oates, T. Sullivan. M. Girolami. (May 2016) Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems

New:- P. Conrad, MAG, S.Sarkka, A.M.Stuart, K.Zygalkis. (May 2016) Probability Measures for Numerical Solutions of Differential Equations, to appear Statistics and Computing

New: - Banushi. B., ..., 27 Authors later... Girolami, M., Bozec, L., Mills, K., Gissen, P., (April 2016) Regulation of Post-Golgi LH3 Trafficking is Essential for Collagen Homeostasis, to appear Nature Communications.

NEW: - Moores, M., Gracie, K., Carson, J., Faulds, K. Graham, D., Girolami, M. (April 2016) Bayesian Modelling and Quantification of Raman Spectroscopy.

NEW: - 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]

NEW: - Chris J. Oates, Jon Cockayne, François-Xavier Briol, Mark Girolami (March 2016). Convergence Rates for a Class of Estimators Based on Stein's Identity

NEW: - Jake Carson, Murray Pollock, Mark Girolami (March 2016) Unbiased local solutions of partial differential equations via the Feynman-Kac Identities

NEW: - Sam Livingstone, Mike Betancourt, Simon Byrne and Mark Girolami (January 2016) "On the Geometric Ergodicity of Hamiltonian Monte Carlo"

NEW: - Mike Betancourt, Simon Byrne, Sam Livingstone, and Mark Girolami (2016) "The Geometric Foundations of Hamiltonian Monte Carlo" to appear Bernoulli 

Chris Oates, Theo Papamarkou, and Mark Girolami. The Controlled Thermodynamic Integral, Journal of the American Statistical Association, Volume 111, Number 514, 634--645, 2016. Xi'ans Og post

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. to appear Analytical Chemistry

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.

New paper: with Seppo Virtainen to appear at ICML 2015 - Ordinal Mixed Membership Models

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

Recent work with Herr Strathmann and Dino Sejidonvic on Bayes for Big Data 


Department of Statistics
The University of Warwick
CV4 7AL, Coventry

Phone 44 (0)24 7652 4630

Email address M dot Girolami at warwick dot ac dot uk