Modern Statistics and Statistical Machine Learning

This is the Oxford component of StatML, an EPSRC Centre for Doctoral Training (CDT) in Modern Statistics and Statistical Machine Learning, co-hosted by Imperial College London and the University of Oxford. The CDT will provide students with training in both cutting-edge research methodologies and the development of business and transferable skills – essential elements required by employers in industry and business.

Each student will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and at the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with a challenging real problem. A significant number of projects will be co-supervised with industry.

The students will pursue two mini-projects during their first year (specific timings may vary for part-time students), with the expectation that one of them will lead to their main research project. At the admissions stage students will choose a mini-project. These mini-projects are proposed by our supervisory pool and industrial partners. Students will be based at the home institution of their main supervisor of the first mini-project.

During their first three months (six months for part-time students) at the CDT students will work on their first mini-project, and during months four to six (seven to twelve months for part-time students) of their DPhil they will work on a second mini-project. For students whose studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question. Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.

The students will then begin their main DPhil project, which can be based on one of the two mini-projects. The final thesis is normally submitted for examination during the fourth year (or eighth year if studying part-time) and is followed by the viva examination.

Where appropriate for the research, student projects will be run jointly with the CDT’s leading industrial partners, and you will have the chance to undertake a placement in data-intensive statistics with some of the strongest statistics groups in the USA, Europe and Asia.

Alongside their research projects students will engage with taught courses each lasting for two weeks. Core topics will be taught during their first year (specific timings may vary for part-time students) and are:

• Bayesian Modelling and Computation
• Statistical Machine Learning; and
• Modern Statistical Theory.

Students will also be required to take a number of optional courses throughout their four years, which could be made up of choices from the following list: Advanced Monte Carlo methods, Causality and Graphical models, Networks, Nonparametric Bayes, Modern Asymptotics, Optimisation, (Deep) learning Theory and Practice, Reinforcement learning and Multi-Armed Bandits, Applied statistics and Genetics/computational biology.

The allocation of graduate supervision for this course is the responsibility of the Department of Statistics (Oxford) and/or the Department of Mathematics (Imperial). It is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. A supervisor may be found outside these departments.

Students are matched to their supervisor for the first mini-project at the start of the course. Within the first year of the course, the student will have the opportunity to work with an alternative supervisor for a second mini-project.

This is a new course and there are no alumni yet. StatML is dedicated to providing the organisation, environment and personnel needed to develop the future industrial and academic individuals doing world-leading research in statistics for modern day science, engineering and commerce, all exemplified by ‘big data’.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. In certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include sabbatical leave, parental leave or change in employment.

For further information, please see our page on changes to courses.

Other courses you may wish to consider

If you're thinking about applying for this course, you may also wish to consider the courses listed below. These courses may have been suggested due to their similarity with this course, or because they are offered by the same department or faculty.

All graduate courses offered by the Department of Statistics

Mathematical Sciences MSc

Modern Statistics and Statistical Machine Learning EPSRC CDT

Statistical Science MSc

Statistical Science PGDip

Statistics DPhil

Statistics MSc by Research

Next
Entry requirements