We are offering a range of projects in the broad areas of evolutionary and Bayesian search at Swansea University, UK (https://www.swansea.ac.uk/compsci/) as part of the UKRI Centre for Doctoral Training in
Artificial Intelligence, Machine Learning & Advanced Computing (http://cdt-aimlac.org/). The studentships are
fully funded for UK/EU students. Here is a list of potential projects.
Title: Putting the Human Back in the Loop of Bayesian Optimisation
for Aerospace Design
1st supervisor:
Dr
Alma
Rahat
2nd supervisor:
Dr
Sean
Walton
Department/Institution: Computational Foundry, Swansea University
Research theme: T3 – novel mathematical, physical and computer science approaches
Project description: Bayesian optimisation algorithms have proved useful for solving many complex aerospace
design problems. In simple terms a designer defines a problem with a starting shape for a component such as a wing as well as some kind of performance metric the algorithm tries to maximise. Computational fluid dynamic
(CFD)
simulations are run to estimate the performance metric to drive the optimisation process which may be computationally expensive and time-consuming. A surrogate model for performance is thus used to identify the most promising solutions that may be subjected
to CFD simulations. As new simulations are performed, the surrogate model is retrained and predictions for good solutions improve, and after a certain number of simulations, the best estimation of the optimal design is presented to the designer. Most of the
current research has been focused on reducing the number of simulations required to estimate the best design by introducing new utility functions that define how to balance between exploration and exploitation from the predictions of surrogate models in locating
promising solutions. However, they mostly ignore the preferences from the designer on some of the most important aspects of design, such as aesthetics or fabrication feasibility, that can not be simply modelled by a computer during the optimisation process.
Therefore, the successful PhD candidate will develop techniques for putting the human back into the loop and allowing the optimiser to be driven by designer preference as well as performance metrics.
Title: Predicting Effective Control Parameters for Evolutionary
Algorithms Using Machine Learning Techniques
1st supervisor: Dr
Sean
Walton
2nd supervisor:
Dr
Alma
Rahat
Department/Institution: Computational Foundry, Swansea University
Research theme: T3 – novel mathematical, physical and computer science approaches
Project description: Evolutionary algorithms have proven to be capable of solving complex problems. A
limitation of these techniques is that they often have a number of control parameters which need tweaking for every new problem they are applied to in order to maximise performance. The most common way to address this problem is to allow these control parameters
to adapt as the algorithm runs. Recent developments however have shown that by sampling a new problem it is possible to predict effective control parameters before the start of the optimisation. In this project you will work to develop new techniques using
machine learning to predict effective control parameters for new problems.
Title: Efficient Learning of the Optimal Probability Distribution
over the Policy Space in Reinforcement Learning.
1st supervisor:
Dr
Alma
Rahat
2nd supervisor:
Dr
Sean
Walton
Department/Institution: Computational Foundry, Swansea University
Research theme: T3 – novel mathematical, physical and computer science approaches
Project description: Reinforcement Learning is an important technique in Machine Learning for control
problems, and it is closely related to how we learn in an unknown environment. In this method, an agent performs a possible action based on what state it is in and its prior belief of the reward for that action, and receives a reward or punishment as a consequence
of that action; this helps it to differentiate between good and bad actions given a state as the agent may update its belief with more experience. Essentially, the learner aims to develop an estimation of the optimal probability distribution for selecting
an action over the state-action space
(also
known as the policy space). Traditional approaches use repeated trials and errors to estimate this distribution over the policy space, which can be time-consuming due to the sheer number of required repetitions for a good estimation. Given the utility of reinforcement
learning, it would be game changing to be able to improve the speed of learning by reducing the number of repetitions required. In this project, inspired from the Bayesian optimisation approaches, we propose to investigate a novel data-driven direct policy
search approach where you will model the probability distribution from carefully selected data, take an entropy based search approach to identify the most informative trials to perform, and sequentially improve the estimation of the optimal probability distribution
over the policy space.
Title: Robust Parameter Optimisation for Image Segmentation
1st Supervisor: Dr Thomas Torsney-Weir
2nd Supervisor: Dr Alma Rahat
Department/Institution: Computational Foundry, Swansea University.
Research Theme:
- T2: biological, health and clinical sciences
(medical
imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches
(data,
hardware, software, algorithm
Description:
Medical professionals rely on robust image segmentation algorithms to highlight anomalous tissues in
a patient, e.g. cancer. However, image segmentation algorithms are typically calibrated on a limited number of training images through a tedious trial and error process. This gives limited context to the robustness against overfitting which makes it difficult
to predict how well the segmentation algorithm will perform on unseen images. This could lead to an incorrect diagnosis. The goal of this project is to use a combination of visualization techniques
(e.g.
Tuner[1]) and Bayesian model calibration techniques
(e.g.
history matching[2]) to develop a system for robust parameter optimisation with a focus on image segmentation algorithms.
References.
[1] Torsney-Weir, T., Saad, A., Moller, T., Hege, H.C., Weber, B., Verbavatz, J.M. and Bergner, S., 2011.
Tuner: Principled parameter finding for image segmentation algorithms using visual response surface exploration.
IEEE Transactions on Visualization and Computer Graphics,
17(12),
pp.1892-1901.
[2] Andrianakis, I., Vernon, I.R., McCreesh, N., McKinley, T.J., Oakley, J.E., Nsubuga, R.N., Goldstein,
M. and White, R.G., 2015. Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda.
PLoS computational biology,
11(1),
p.e1003968.
Title: Multi-Objective Evolutionary Approach Towards Dynamic Graph
Drawing
1st Supervisor: Dr Alma Rahat
2nd Supervisor: Dr Daniel Archambault
Department/Institution: Computational Foundry, Swansea University.
Research Theme:
T3: novel mathematical, physical, and computer science approaches
(data,
hardware, software, algorithm
Description:
To draw an appealing dynamic graph, it is important to strike a balance between readability and stability
as there is a natural conflict between these objectives
[1].
Current approaches set up force systems which can be optimised to reach a reasonable trade off between the two. In this project, we investigate how multi-objective evolutionary algorithms can be used to explore the trade-offs between readability and stability.
References.
[1] D. Archambault and H. C. Purchase. Can animation support the visualization of dynamic graphs? Information
Sciences, 330:495–509, 2016.
Applications.
Deadline for applications is on the 31st of January, 2020.
--
Dr Alma Rahat
Lecturer in Big Data/Data Science,
Department of Computer Science | Adran Gyfrifiadureg,
College of Science | Coleg Gwyddoniaeth,
Swansea University | Prifysgol Abertawe,
United Kingdom.
Office: 112, Computational Foundry, Bay Campus | 112, Ffowndri Gyfrifiadol, Campws y Bae.
Tel: +44 (0) 1792 518621
Web:
https://www.swansea.ac.uk/staff/science/computer-science/alma-rahat/
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