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CoSInES (COmputational Statistical INference for Engineering and Security)

CoSInES（工程和安全计算统计推断）

基本信息

批准号：
EP/R034710/1
负责人：
Gareth Roberts
金额：
$ 375.95万
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR034710%2F1
关键词：
CoSInES COmputational Statistical INference Engineering

项目摘要

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.

为了对 21 世纪数据革命中出现的海量数据有科学的认识，对先进的统计方法有着巨大的需求。人类活动的几乎每个领域中的各种重要问题都给建模、计算和统计算法带来了巨大的挑战。 CoSInES 将在这些挑战的推动下，在原则统计方法的使用方面做出重大改变。我们的大部分研究将开发和研究适用于广泛应用的通用方法。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来理解由应用激发的新的复杂模型。我们将制定适合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方法，其中隐私限制意味着无法直接访问数据。CoSInES 还将关注两个主要应用领域，这两个领域将为我们的研究带来刺激和挑战动力：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英国政府通讯总部资助。Data 正在提供颠覆性的变革，通过以前无法想象的设计、制造、运营和维护工程资产直至其退役的方式，正在彻底改变工程专业。艾伦图灵研究所的数据中心工程项目 (DCE) 在世界上第一座人行天桥的设计和运营方面处于领先地位，该天桥将在国际主要城市开放和运营，该天桥将完全由 3D 打印而成。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”来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的挑战，因为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。需要新一代的数值推理方法来支持这一进展。在国防和安全领域，由于处理和通信大型复杂数据集的需要而出现了许多统计挑战，例如在网络安全领域。虚拟世界已成为大多数组织运营的全球主导市场。这促使邪恶的行为者——从“卧室黑客”到国家支持的恐怖分子——在这种环境下运作，以进一步实现他们的经济或政治野心。为了应对这种威胁，有必要在存在数据丢失、重大时间变化以及对手愿意操纵社会和虚拟系统以实现其目标的情况下，生成环境的完整统计数据。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，同时严格应用数据保护法来维护个人隐私。因此，有必要对这种数据共享安排进行数学保证，并制定匿名数据“渗透测试”的统计方法。