EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning

CDT将培养统计和统计机器学习领域的下一代领导者，他们将能够开发广泛适用的新方法和理论，并创建特定于应用的方法，从而在政府、医学、工业和科学领域的现实问题上取得突破。该研究将侧重于应用现代统计理论和方法的发展，以及统计机器学习的基础。这项研究将与应用密切相关。国家迫切需要这个CDT的毕业生。大量复杂的数据现在在社会的各个部门例行收集，包括电子健康记录、大量科学数据集、政府数据和通过数字经济的出现收集的数据。利用这些数据的基础技术来自统计学和机器学习。利用这些数据对英国未来的繁荣至关重要。然而，来自政府和学术团体的几份报告指出，缺乏能够利用这些数据的个人。在许多情况下，现有的方法是不够的。现成的方法可能会误导由于缺乏可重复性或抽样偏差，他们没有纠正。此外，通常需要了解潜在的机制：需要科学上有效、可解释和可重复的结果来理解科学现象和证明决定的合理性，特别是那些影响个人的决定。需要定制的、基于模型的统计方法，这些方法可能需要与统计机器学习方法相结合来处理大数据。能够满足这些更复杂需求的个人是精通机器学习基础的统计学博士学位毕业生。然而，英国每年只有一小部分统计学博士毕业，而这些毕业生中的许多人都没有接触过机器学习。该中心将把帝国理工学院和牛津大学这两家顶尖的统计机构作为平等的合作伙伴聚集在一起，提供一个特殊的培训环境，并让各自领域的绝对研究领导者直接参与。导师池将包括统计方法和理论以及统计机器学习方面的杰出研究人员。我们将采用创新和以学生为主导的教学，重点是博士水平的培训。教学是跨年的，因此不仅在一个年级组内，而且在不同年级组之间，都创造了强大的班级凝聚力。我们将通过合作伙伴的互动以及学生与统计用户的安排，将理论进展与应用领域联系起来。CDT拥有大量高知名度的合作伙伴，他们帮助塑造了我们的应用优先领域（数字经济、医学、工程、公共卫生、科学），他们将共同资助和共同监督博士生，并共同提供教学元素。

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications.There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data.In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning.The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning.We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics.The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.