Advanced machine learning techniques for analysis of nanoscopic images

This project has been developed by the University of Liverpool in partnership with Sivananthan Labaratories.
Extracting quantitative information from nanoscale images of materials structures and processes is a repetitive and costly process. Currently humans are required to manually acquire images from experimentation and compare them with large repositories of data to find physical/chemical properties of nanoscale materials systems. In many cases, these databases often contain images with large disparities in their noise patterns, spatial/temporal resolution, magnification and/or signal-to-noise ratio, making the identification and use of these features challenging to accomplish in practice.
In this project, our goal is to develop an optimised workflow for extracting quantitative information that is focused on one type of nanoscale image obtained by (scanning) transmission electron microscopy/(S)TEM. In many (S)TEM images, the size and shape (morphology) of nanoparticles and their special arrangements (dispersion) are a key analytical measure that has been demonstrated.
Recent work by Chiwoo Park and Yung Ding has made significant strides in analysis of these types of images through implementation of existing algorithms to (S)TEM images. Their recent research outlines statistical techniques for the analysis of the morphology, location, dispersion analysis and multi-object tracking analysis of nanoparticles. These techniques can be leveraged to process existing unannotated data, and will serve as a baseline for performance for comparison with future models.
This PhD will investigate generalised advanced analysis techniques to advance the capabilities (S)TEM imaging. Where applicable, supervised machine learning methods such as ANN, CNNs and Adversarial networks can be investigated for their ability to analyse images with varying degrees of noise within a microscopic setting. The aim of this project will be to leverage the support provided by the CDT to develop a generalised machine learning model capable of analysing hundreds of images. If successful, this will be used real-time during nanoscopy experiments, advancing the existing capabilities of (S)TEM research.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.