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

EPSRC 现代统计学和统计机器学习博士培训中心

• 批准号: EP/S023151/1
• 金额: $ 823.17万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS023151%2F1
• 关键词: EPSRC; Centre; Doctoral; Training; Modern

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.

CDT将培训统计和统计机器学习领域的下一代领导者，他们将能够开发出广泛的新颖方法论和理论，并创建特定于应用程序的方法，从而在政府，医学，工业和科学方面的现实世界中突破。该研究将重点介绍适用的现代统计理论和方法的发展，以及统计机器学习的基础。这项研究将与应用程序密切相关。该CDT对毕业生的迫切需要。现在，在社会的所有部门都经常收集大量复杂数据，包括电子健康记录，大量的科学数据集，政府数据以及通过数字经济出现收集的数据。利用这些数据的基础技术来自统计和机器学习。利用此类数据对于未来的英国繁荣至关重要。但是，政府和学识渊博的社会的几份报告已经确定缺乏能够利用这些数据的人。在许多情况下，现有方法不足。由于缺乏可重复性或不正确的采样偏见，现成的方法可能会产生误导。此外，通常需要理解潜在的机制：需要科学有效，可解释和可重现的结果来了解科学现象并证明决定的决定，尤其是那些影响个人的决策。需要定制的基于模型的统计方法，可能需要将其与统计机器学习方法混合以处理大型数据。可以满足这些更为复杂的要求的个人是统计学中精通机器学习基础的统计学级毕业生。然而，英国每年仅毕业少数统计学博士学位，其中许多毕业生将不会接触机器学习。该中心将把帝国和牛津大学和牛津大学汇集在一起​​，两个顶级统计小组，作为平等合作伙伴，提供出色的培训环境以及提供绝对研究领导者在该领域的绝对研究领导者的直接参与。主管池将包括统计方法和理论以及统计机器学习的杰出研究人员。我们将使用创新和学生主导的教学，重点关注博士学位培训。多年来的教学削减，因此不仅在一年之内，而且在年之间都创造了强大的队列凝聚力。我们将通过合作伙伴的互动以及与统计用户的学生的成员联系，将理论进步与应用领域联系起来。CDT拥有大量知名合作伙伴，这些合作伙伴有助于塑造我们的应用优先领域（数字经济，医学，工程，公共卫生，公共卫生，科学），并将共同获得和共同筹集资金，并共同获得Phd Persivise Phd学生，以及同事教学元素。