CoSInES (COmputational Statistical INference for Engineering and Security)

Lead Research Organisation: University of Warwick
Department Name: Statistics

Abstract

There are tremendous demands for advanced statistical methodology to make scientific sense of the deluge of data emerging from the data revolution of the 21st Century. Huge challenges in modelling, computation, and statistical algorithms have been created by diverse and important questions in virtually every area of human activity. CoSInES will create a step change in the use of principled statistical methodology, motivated by and feeding into these challenges.

Much of our research will develop and study generic methods with applicability in a wide-range of applications. We will study high-dimensional statistical algorithms whose performance scales well to high-dimensions and to big data sets. We will develop statistical theory to understand new complex models stimulated from applications. We will produce methodology tailored to specific computational hardware. We will study the statistical and algorithmic effects of mis-match between data and models. We shall also build methodology for statistical inference where privacy constraints mean that the data cannot be directly accessed.

CoSInES willl also focus on two major application domains which will form stimulating and challenging motivation for our research: Data-centric engineering, and Defence and Security. To maximise the impact and speed of translation of our research in these areas, we will closely partner the Alan Turing Institute which is running large programmes in these areas funded respectively by the Lloyd's Register Foundation and GCHQ.

Data is providing a disruptive transformation that is revolutionising the engineering professions with previously unimagined ways of designing, manufacturing, operating and maintaining engineering assets all the way through to their decommissioning. The Data centric engineering programme (DCE) at the Alan Turing Institute is leading in the design and operation of the worlds very first pedestrian bridge to be opened and operated in a major international city that will be completely 3-D printed. Fibre-optic sensors embedded in the structure will provide continuous streams of data measuring the main structural properties of the bridge. Unique opportunities to monitor and control the bridge via "digital twins" are being developed by DCE and this is presenting enormous challenges to existing applied mathematical and statistical modelling of these complex structures where even the bulk material properties are unknown and certainly stochastic in their values. A new generation of numerical inferential methods are being demanded to support this progress.

Within the Defence and Security domain, there are many statistical challenges emerging from the need to process and communicate big and complex data sets, for example within the area of cyber-security. The virtual world has emerged as a dominant global marketplace within which the majority of organisations operate. This has motivated nefarious actors - from "bedroom hackers" to state-sponsored terrorists - to operate in this environment to further their economic or political ambitions. To counter this threat, it is necessary to produce a complete statistical representation of the environment, in the presence of missing data, significant temporal change, and an adversary willing to manipulate socio and virtual systems in order to achieve their goals.

As a second example, to counter the threat of global terrorism, it is necessary for law-enforcement agencies within the UK to share data, whilst rigorously applying data protection laws to maintain individuals' privacy. It is therefore necessary to have mathematical guarantees over such data sharing arrangements, and to formulate statistical methodologies for the "penetration testing" of anonymised data.

Planned Impact

Academic impact of the project will be achieved by standard mechanisms: publication, software development, conference presentations, and highlighting activities on the project website. Academic beneficiaries of this reach will include statisticians working on theory and methodology as well as a wide range of application areas. Academics outside statistics will also benefit from the methodology and software created within the project.

Engineers will benefit from the research in Objective 7 which will create a principled statistical framework for Data-centric Engineering. In turn, the government, commercial companies and the public will benefit from improved reliability of engineering structures and the economies and improved productivity created as a result of the improved scientific understanding accessed through our research. Research in this area will be rapidly disseminated to the Engineering community through the Turing Data-centric Engineering pogramme, through translational activities organised by CoSInES (such as our Impact and Innovation Showcase days), and through the bespoke software.

Through the research in Objective 8, government, commercial companies and the public will benefit from improved cyber-security and the extra security afforded through improved data-sharing efficiency of law-enforcement agencies. Through the Alan Turing Institute's Defence & Security Programme, the output of this research will directly impact the operational sectors of the UK's defence and security function, through the deployment of bespoke software, and the furthering of the statistical knowledge of the UK Government's intelligence analysts. We will also organise Impact and Innovation Showcase days focused in this area.

Publications

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Finke A (2020) Limit theorems for sequential MCMC methods in Advances in Applied Probability

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Chimisov Cyril (2018) Adapting The Gibbs Sampler in arXiv e-prints

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Nemeth Christopher (2019) Stochastic gradient Markov chain Monte Carlo in arXiv e-prints

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Laurini Fabrizio (2019) Evaluation of extremal properties of GARCH(p,q) processes in arXiv e-prints

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Vats Dootika (2020) Efficient Bernoulli factory MCMC for intractable posteriors in arXiv e-prints

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Chevallier Augustin (2020) Reversible Jump PDMP Samplers for Variable Selection in arXiv e-prints

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Angeli Letizia (2019) Limit theorems for cloning algorithms in arXiv e-prints

 
Description 20th century computational statistical methodologies (eg based around MCMC, SMC and variants) bring complex problems within the grasp of classical and Bayesian model-based paradigms. However, today's complex problems pose immense challenges for principled statistical methods. CoSInES is working to bridge the gap between Statistical Science and the most challenging inferential problems posed by Data Science. There is a particular focus on problems emanating from Engineering and Security and a number of key application challenges are under investigation, including methodology for "digital twins" within data-centric engineering and anomaly detection in internet traffic signals.
Exploitation Route Most of the work in this project is providing generic methodology and underpinning theory for computational statistics which can be applied to virtually all areas where rigorous statistical methodology is used. The more applied work focusing in data-centric engineering and security should have benefits for researchers in this application areas as well as implications for a wide range of engineering projects.
Sectors Construction,Digital/Communication/Information Technologies (including Software),Security and Diplomacy

URL https://www.cosines.org