Josh graduated from the University of Durham with an MMath degree in 2016 where he did his final year project looking at modular forms. Following this he spent the summer working in the Met Office with the Forecast Service Delivery department, developing, testing and implementing software tools to access, process and summarise archived data. He then spent a couple of years working in the NHS in Bristol in both the Infection Control Team the Critical Care Data Team looking at methods to extract, merge and cleanse datasets for research and audit purposes. He is a keen cyclist who has also been known to compete in half-marathons and triathlons, enjoys classical music as well as synthpop and playing the piano, and is surprisingly interested in the London Underground (other metros also apply).

Research project title:
Change point detection in spatio-temporal data

Supervisor(s):
Matt Nunes, Sandipan Roy

Project description:
When analysing spatio-temporal data in applications such as in environmental, ecological, or epidemiological settings, detecting abrupt changes over space and or time gives insight into the underlying mechanics of a system and can have a significant impact in the interpretation of the evolution and future state of the process. The aims of the project include looking at methods that analyse spatio-temporal data and expand on these further, where gaps in the literature exist, developing a set of robust statistical tools to interpret these data sets. In particular, we will consider how the space and time elements of the data interact, and when we are able to detect change points or anomalies how easily can we classify them.