Innovation Placements

Eight student placements were funded by this project, which delivered the following pieces of research:

Solar Flare Prediction
Objective

To build a tool for space weather and solar flare prediction.

Summary of work undertaken

In this project, a tool was created for space weather analysis and, in particular, solar flare forecasting. More precisely a code was developed that takes solar magnetogram observations (of magnetic fields emerging into the solar atmosphere) as input and calculates a series of measures related to the topology of the solar magnetic field. Particular signatures of these measures can be used for flare forecasting and compared to other satellite data.

Natural Language Processing for Work Order Classification
Objective

Apply Natural Language Processing to classify work orders in order to automate the process of reviewing work order information done by Senior Engineers on two London Underground rail lines.

Summary of work undertaken

Investigated how to handle large data sets, and application of methods such as text pre-processing, lemmatization, etc. Used MLFlow to experiment and optimise logistic regression models using this data, achieving significant improvements in performance through various methods of data cleaning and manipulation.

Natural Language Processing Applied to Engineering Team Documents
Objective

To use Natural Language Processing against an Industry Partner’s Asset data, to provide a better understanding on asset performance.

Summary of work undertaken

Used Deep Learning Natural Language classifiers and transferred learning to label the text generated by the partners engineering teams, which provided a better understanding on asset performance, and a better understanding of the costs involved in running the maintenance of assets.

HES Health Episode Statistics Database
Objective

To remove systematic errors from the NHS HES Health Episode Statistics database.

Summary of work undertaken

Machine Learning was used to identify systemic errors and biases in the HES database. These were removed and a cleaner version of the HES database was produced and made available for analysis and interpretation.

Deprivation Indicators on Asthma in Young Adults
Objective

To measure the effect of deprivation indicators on asthma in young adults.

Summary of work undertaken

The Project used data from the IWCH data vault to develop and use a patient centred analytics platform by applying analytics and ML methods to the data to compare whether the main features that affect young adult asthma sufferers are linked to deprivation indicators.

Learning Health System for Children 

NHS Institute for Women’s and
Children’s Health (IWCH)

Objective

To improve the learning health system for children.

Summary of work undertaken

The project built on a 10-year programme of work in the Children and Young People’s Health Partnership (CYPHP) to deliver story boards for platform and dashboard construction to enable population health management for children with long term conditions.

Mapped and catalogued data flows for the afferent and efferent arms of Learning Health System Generic dashboards for clinical and population health management of children that can be adapted for specific long-term conditions. Place-based data maps illustrating areas of health need and risks were also produced.

Enabling Machine Learning Hybrid Simulations and Uncertainty Quantification 
Objective

To produce a new Scientific Machine Learning Benchmark.

Summary of work undertaken

A new Radiative Transfer Machine Learning Neural Network Application was produced and added to the SciML machine learning benchmark suite.

Quantum Computing 
Goal 

To study the algorithm suggested by Jordan et al (https://arxiv.org/abs/1112.4833) and write a first implementation for the QLM using myQLM.

Summary of work undertaken

Following a learning phase to gain familiarisation of the details of Jordan’s algorithm and an understanding of the basics of Quantum Computing and myQLM, an algorithm for the simplest case of a scalar field theory was implemented. Training materials for an Introduction to Quantum Computing workshop were developed and a first iteration of the course was delivered in September 2022.

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