This opportunity is now closed.

Funded PhD Opportunity

Active Bayesian machine learning

Subject: Computer Science and Informatics


Summary

Overview

The aim of this project is to develop state-of-the-art Bayesian machine learning methods for use in applications where we generate our own training data using computer simulation. The research will be carried out within the Artificial Intelligence Research Group at Ulster University, and methods tested using a high impact application from the physical sciences, under active development at the University of Manchester.

Background

Recent failures in deep learning, such as the first fatality from driverless cars, have highlighted the importance of reporting the uncertainty in predictions made by machine learning [1]. The need to consider predictive uncertainty is common, and applies as much to the learning of atomic properties [2] as it does to autonomous driving. Bayesian machine learning [3] naturally provides us with estimates of the uncertainty in our predictions. The behaviour of a Bayesian model is a balance between what the training data says, and our pre-existing belief of how the model should behave, as expressed through probability distributions called priors.

This project concerns Bayesian machine learning for applications where we can generate our own training data using computer simulations. Choosing which data to learn from is called active learning, and is particularly interesting for Bayesian models given that the accuracy of our model depends also on the choice of prior.

Proposed research

The methods developed will be generic, but tested using a state-of-the-art application from computational chemistry that has been developed and updated over the past decade at the University of Manchester [4,5]. The exact research challenges to be addressed in this project can be tailored to the interests and experience of the PhD candidate, but example problems include:

*Active learning for multi-fidelity models: The simulations that generate the training data have tolerance parameters that enable us to control its fidelity (accuracy). Should we generate small amounts of high-fidelity data to train our models from, or large amounts of low-fidelity data, or a combination of both? How does this affect the uncertainty in our predictions?

*Prior specification: How can we best construct informative priors for a new problem, using (i) data from related problems, and (ii) domain knowledge, such as physical laws that place constraints on the data? How much data will we need to generate to counterbalance prior misspecification? Other challenges exist in areas such as dimensionality reduction, multi-task learning, and Bayesian optimization. All of these problems are of course not mutually exclusive, but overlapping.

[1] Kendall, A. (2018) Deep learning is not good enough, we need Bayesian Deep Learning for safe AI: Available at: https://alexgkendall.com/computer_vision/bayesian_deep_learning_for_safe_ai/

[2] Peterson, A., et al. (2017) Addressing uncertainty in atomistic machine leaning, Physical Chemistry Chemical Physics 19 10978-10985.

[3] Murphy, K. (2012) Machine Learning: A Probabilistic Perspective, MIT Press.

[4] Handley, C. M., Hawe, G. I., Kell, D. B., Popelier, P. (2009) Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning, Physical Chemistry Chemical Physics 11 6365-6376.

[5] McDonagh, J., et al. (2018) Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms, Journal of Chemical Theory and Computation, 14 (1)


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)


Funding

    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.

    DFE

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.


Other information


The Doctoral College at Ulster University


Reviews

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Monday 18 February 2019

Interview Date
25 to 29 March 2019


Applying

Apply Online


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Dr Glenn Hawe


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