Michael is a senior data scientist, having joined the campus in May 2018. Michael has a PhD in Earth Sciences from Cardiff University and has experience in using satellite imagery and building probabilistic models for assessing natural hazards. In particular, he collaborated with the Malawian Government to improve their knowledge and understanding of large, infrequent earthquakes. During this time he was also involved with many STEM related workshops.
Before his PhD, Michael worked for several years at Natural Resources Wales, where he delivered a EU funded sustainable land management scheme. His current ambitions within the campus are to utilise his satellite imagery experience in a number of research projects.
We used machine learning to develop a model to identify areas of trees from satellite images in eastern Uganda, where the Mbale Trees Programme has been running since 2010.
Read more on Using Sentinel-2 images to measure the change in tree coverage in eastern Uganda: what does it mean for the Mbale Trees Programme?
Large volumes of unstructured, free-text data such as patent applications, contain potentially valuable information for policymakers. Here we describe the pipeline used to process such data sources.
Read more on pyGrams: An open source tool for discovering emerging terminology in large text datasets
In this report, we focus primarily on how the pyGrams tool can be used to analyse terms through time using the time stamps in document metadata.
Read more on Discovering emerging important terminology in large text datasets using pyGrams: a comparison between net growth and e-score methods
We introduce pyGrams – a new Python tool for extracting, visualising and identifying emerging terms in large document collections, such as patents.
Read more on Extracting, visualising and identifying emerging important terminology from patent collections
The ability for a household to access a range of services necessary for day-to-day living is of great importance to…
Read more on Access to services using multimodal transport networks