Lung cancer is a challenging disease to diagnose and treat, and is the most common cause of cancer death in both men and women worldwide. Five year survival rates remain poor at 9.0%, and on a global basis, the 2012 statistics suggest that lung cancer was responsible for 1.59 million deaths. A particular difficulty is that most lung cancers are diagnosed at a late stage, with about 75% of patients having advanced disease at the time of diagnosis. Identification of patients with lung cancer at an earlier stage is therefore vital if outcomes are to be improved. CT screening can identify possible cancerous nodules in the lung, but biopsy and histology, in which a tissue sample is examined under a microscope, is then required for diagnosis. The standard procedure to extract the tissue sample is trans-thoracic biopsy, in which a needle is inserted through the chest wall, typically under CT image guidance. This provides good diagnostic results, but is associated with complications, especially pneumothoraces (collapsed lung) which occurs in 15% of cases. More recently, technical advances have allowed biopsy to be performed through a bronchoscope, reducing the risk of complications and allowing the procedure to be performed during routine examination sessions. However, success is highly operator dependent and for remote, small nodules, the diagnostic rate (the yield) is poor. This is due to a number of factors, including the complexity of the bronchial tree, patient motion due to breathing (particularly at distal segments), poor ergonomics, and the large diameter of bronchoscopes prohibiting access beyond fourth generation bronchial segments (the fourth level of 'splitting' in the bronchial tree). The purpose of the REBOT project is to develop a robot-guided endobronchial probe that will allow access to the deepest reaches of the lung. It will be introduced through a working channel of a bronchoscope, making it highly compatible with current procedures. The probe will have integrated optical coherence tomography (OCT) and fluorescence imaging to allow multi-modal visualisation of the morphological and cellular details of the airways. Optical coherence tomography will provide 3D images to a depth of 1-2 mm into the tissue, while fluorescence imaging will provide high resolution surface imaging. These real-time imaging techniques will be used to help navigate the probe to the correct location for extraction of the biopsy tissue sample, increasing the chances of a successful diagnosis.

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.

Vascular disease is the most common precursor to ischaemic heart disease and stroke, which are two of the leading causes of death worldwide. Advances in endovascular intervention in recent years have transformed patient survival rates and post-surgical quality of life. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anaesthesia, reduced blood loss and significantly lower mortality. However, endovascular intervention involves complex manoeuvring of pre-shaped catheters to reach target areas in the vasculature. Some endovascular tasks can be challenging for even highly-skilled operators. The use of robot assisted endovascular intervention aims to address some of these difficulties, with the added benefit of allowing the operator to remotely control and manipulate devices, thus avoiding exposure to X-ray radiation. The purpose of this work is to develop a new robot-assisted endovascular platform, incorporating novel device designs with improved human-robot control. It builds on our strong partnership with industry aiming to develop the next generation robots that are safe, effective, and accessible to general NHS populations.

The need for better support to deal with the threats of cybersecurity is undisputed. Organisations are faced with an ever growing number of malware and integrated malware attack tools, attempted attacks on infrastructure and services, an increasing number of insider attacks, and advanced persistent threats for high-priced assets. Dealing with such threats requires that organisations have ICT staff that is at least familiar with cybersecurity issues and preferably has actual skills in cybersecurity regardless of the role of such staff. Likewise, management and decision makers need to be aware of cybersecurity issues and reflect these in their actions. Large organisations often have a Chief Information Security Officer (CISO) who deals with the operational and strategic issues of cybersecurity for his or her organisation. But SMEs typically cannot afford a role with such oversight on cybersecurity, which makes them especially vulnerable. The scale and diversity of cybersecurity issues that an organisation faces means it cannot possibly consider each single vulnerability of its systems against each credible or potential adversary whose presence would turn a vulnerability into an actual threat. A CISO or decision maker, though, needs to have a fairly abstract view of all this complexity where the choice of abstraction is not driven by technical aspects but by modalities such as risk, compliance, availability of service, and strategy. This view often has to take into account the cybersecurity of external or partner organisations, which is problematic as organisations are reluctant to share such sensitive information. Therefore, a CISO or decision maker needs a representation of relevant internal or external systems and services that allows him or her to make decisions of either operational or strategic nature. The uncertainty expressed in such abstractions is typically probabilistic or strict in nature. For example, a bank may have a good idea of the probability that a given teller machine has a corrupted external interface that clones inserted bank cards, based on past history, location of the machine and so forth. Strict uncertainty often relates to threats for which no (or insufficient) historical information is available to estimate probability distributions, or it is used to express the combinatorial nature of a problem, for example the different orderings in which one may schedule critical tasks. This project brings together research leaders in machine learning, robust optimisation, verification and cybersecurity to explore new modelling and analysis capabilities for needs in cybersecurity. The project will investigate new approaches for modelling and optimisation by which cybersecurity of systems, processes, and infrastructures can be more robustly assessed, monitored, and controlled in the face of stochastic and strict uncertainty. Particular attention will be paid to privacy: new forms of privacy-preserving data analytics will be created and approaches to decision support that respect privacy considerations; for corporate confidentiality, we will invent foundations that enable different organisations to model and analyse cross-organisational cybersecurity aspects whilst respecting the type of privacy inherent in organisations' confidential information by establishing appropriate information barriers.