Statistics and its Applications PhD projects

This page provides a (partial) list of specific (and not so specific) PhD projects currently offered around the Statistics and its Applications topic.

Identifying an interesting, worthwhile and do-able PhD project is not a trivial task since it depends crucially on the interests and abilities of the student (and the supervisor!) The list below therefore contains a mixture of very specific projects and fairly general descriptions of research interests across the School. In either case you should feel free to contact the potential supervisors to find out more if you're interested.

You should also explore the School's research groups' pages, and have a look around the homepages of individual members of staff to find out more about their research interests. You may also contact us for general enquiries.

Title

StatXAI - Statistical approaches for explainable machine learning and artificial intelligence

Group Statistics and its Applications
Supervisor
Description

Machine learning (ML) and artificial intelligence (AI) methodologies are now permeating many parts of science, technology, health and society. At their core these methods rely on highly complex, high-dimensional mathematical and statistical models.  However, unfortunately they are generally hard to interpret and and their internal decision making mechanisms are not transparent. The objective of the StatXAI project is to develop statistical tools to understand and help create explainable ML/AI methodologies and models.

Research in StatXAI will focus on four lines of work: i) to investigate advanced nonparametric and algorithmic models such as neural networks and ensemble approaches, ii) to explore diverse strategies for explainable ML/AI using statistical approaches such as LIME/Anchors [1,2], LRP [3], explainable embeddings [4] etc., iii) to develop corresponding effective algorithms and implementing them in open source software (R and Python), and iv) to deploy and test explainable models in biological and medical settings.

The University of Manchester is a partner university of the Alan Turing Institute (ATI, the UK national institute for data science and artificial intelligence).  PhD students will have the opportunity to interact and engage with the ATI.

 

References:

 

[1] Ribeiro et al. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. https://arxiv.org/abs/1602.04938 [2] Ribeiro et al. 2018. Anchors: High-Precision Model-Agnostic Explanations. https://homes.cs.washington.edu/~marcotcr/aaai18.pdf

[3] Montavon et al. 2018. Methods for Interpreting and Understanding Deep Neural Networks. Digital Signal Processing, 73:1-15.

https://doi.org/10.1016/j.dsp.2017.10.011

[4] Qi and Li. 2017. Learning Explainable Embeddings for Deep Networks.

http://www.interpretable-ml.org/nips2017workshop/papers/04.pdf

 

Prerequisites:

Interest in modern machine learning methods and computational statistics, knowledge of multivariate statistics, experience in

programming in R and Python.   Note the focus of the PhD project lies on

methods and algorithms rather than on pure theory.

 

 

Title

Optimal Experimental Designs

Group Statistics and its Applications
Supervisor
Description

The success of many experimental studies in Biology, Chemistry, Engineering, Experimental Physics, Material Science, Medicine, etc., depends on the experimental designs that are used to collect the data. The aim of this project is to develop novel statistical methods for constructing designs that have desirable statistical properties.

The applicants are expected to have deep knowledge in Statistics and Mathematics, as well as good computational skills. The focus of the project will be decided to suit the background and the strengths of the student.

Reference:
Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.

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