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(EPSRC DTP) Explainable AI for Multimodal Data Integration in Biomedical Applications

  • Full or part time
  • Application Deadline
    Thursday, April 30, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description


State-of-the-art black box AI algorithms have revolutionised our ability to construct algorithms for use with complex data sets. These approaches have been of tremendous utility across a number of sectors but have presented some limitations in the biomedical sciences where algorithms that are able to produce “explainable” outputs are highly desirable or critical for some tasks. One particular problem is the need to integrate many types of biomedical data sources (e.g. medical images, genetics and electronic health records) in order to combine these to produce outputs but it remains an open question as to how such integration should be done to produce explainable outputs.

This project will seek to develop explainable probabilistic machine learning methods for multimodal data integration for biomedical applications. We will consider a number of theoretical and practical issues with data integration and explainable AI and develop robust statistically sound solutions to these research problems. The candidate will gain a strong foundation in the development of novel machine learning methodologies using concepts such as Bayesian modelling, decision theory and deep learning. The supervision has a wealth of experience in methodological development and applied data science providing the student with a fully immersive training and learning environment.

The candidate should have a strong interest in developing a career in artificial intelligence research and possess a strong quantitative background obtained from a first degree in mathematics, physics, engineering or computer science. The candidate will be required to train and acquire skills in advanced statistical programming in a modern environment (e.g. PyTorch, TensorFlow, JAX). It is expected that the candidate will develop research outputs that will be publishable in the internationally leading machine learning outlets (including NeurIPS, ICML, AISTATS, UAI). UK/Overseas research visits will be embedded within the PhD study period to give the candidate exposure to the international AI environment.

Informal enquiries should be directed to Professor Christopher Yau () or Dr David Wong ().

Entry Requirements:

Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/)

Funding Notes

EPSRC DTP studentship with funding for a duration of 3.5 years to commence in September 2020. The studentship covers UK/EU tuition fees and an annual minimum stipend £15,285 per annum. Due to funding restrictions, the studentship is open to UK and EU nationals with 3 years residency in the UK.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Martens, K., and Yau, C. (2020) Neural Decomposition: Functional ANOVA with Variational Autoencoders. The 23rd International Conference on Artificial Intelligence and Statistics.

Martens, K., and Yau, C. (2020) Translation-invariant feature-level clustering with Variational Autoencoders, The 23rd International Conference on Artificial Intelligence and Statistics.

Martens, K., Campbell, K. R., and Yau, C. (2019) Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models, International Conference on Machine Learning.

Orphanidou, C., Wong, D. (2017) Machine Learning models for Multidimensional Sensor Data, in Handbook of Large-Scale Distributed Computing in Smart Healthcare, Springer, pp.177-216

Wong, D., Relton SD, Hui F, Alty J, Qahwaji R, Graham CD, Williams S. Supervised classification of bradykinesia for Parkinson’s disease diagnosis from smartphone videos. (2019) The 32nd IEEE International Symposium on Computer-Based Medical Systems

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