CoSInES (COmputational Statistical INference for Engineering and Security)

人们对先进的统计方法有着巨大的需求，以科学地理解21世纪世纪数据革命中涌现的大量数据。在建模、计算和统计算法方面的巨大挑战是由人类活动几乎每个领域的各种重要问题造成的。CoSInES将创造一个步骤的变化，在使用原则性的统计方法，动机和喂养到这些挑战。我们的大部分研究将开发和研究通用方法，适用于广泛的应用。我们将研究高维统计算法，其性能可以很好地扩展到高维和大数据集。我们将发展统计理论，以理解从应用程序中激发的新的复杂模型。我们将产生针对特定计算硬件的方法。我们将研究数据和模型之间不匹配的统计和算法影响。我们还将建立统计推断的方法，其中隐私限制意味着数据不能直接访问。CoSInES还将专注于两个主要的应用领域，这将对我们的研究形成激励和挑战性的动机：以数据为中心的工程，以及国防和安全。为了最大限度地提高我们在这些领域的研究成果的影响力和转化速度，我们将与艾伦图灵研究所密切合作，该研究所正在这些领域开展大型项目，分别由劳埃德船级社基金会和GCHQ资助。数据正在提供一种颠覆性的转变，以前所未有的设计，制造，运营和维护工程资产，直至其退役。艾伦图灵研究所的数据中心工程项目（DCE）正在设计和运营世界上第一座将在一个主要国际城市开放和运营的人行天桥，该天桥将完全由3D打印。嵌入结构中的光纤传感器将提供连续的数据流，测量桥梁的主要结构特性。DCE正在开发通过“数字孪生”监测和控制桥梁的独特机会，这对这些复杂结构的现有应用数学和统计建模提出了巨大挑战，即使是散装材料的属性也是未知的，并且其值肯定是随机的。在国防和安全领域，由于需要处理和交流大量复杂的数据集，例如在网络安全领域，出现了许多统计挑战。虚拟世界已经成为一个占主导地位的全球市场，大多数组织都在其中运营。这促使从“卧室黑客”到国家支持的恐怖分子等邪恶行为者在这种环境中活动，以推进其经济或政治野心。为了应对这一威胁，有必要在存在缺失数据、重大时间变化以及对手愿意操纵社交和虚拟系统以实现其目标的情况下，对环境进行完整的统计表示。作为第二个例子，为了应对全球恐怖主义的威胁，英国境内的执法机构有必要共享数据，同时严格执行数据保护法，以维护个人隐私。因此，有必要对这种数据共享安排提供数学保证，并制定对匿名数据进行“渗透测试”的统计方法。

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.