Professional summary
At UKCEH, Michael's work focuses on advancing and demonstrating the use and capability of data science methods for a wide range of environmental applications and create a step change in environmental research. This includes the application of novel machine learning and statistical methods, fostering open research, improving research narratives using notebook technology, data quality control, promoting the use of virtual labs, and increasing research impact via web apps.
Example datasets he has worked on in the past three years include UK Environmental Change Network, Cumbrian Lakes Monitoring Network, UK Water Industry Research Anti-microbial Resistance, COSMOS-UK, global weather reanalysis datasets, UK air pollutant emissions, CHESS, Hydro-JULES, National River Flow Archive, EA river chemistry, Countryside Survey and the Predatory Bird Monitoring Scheme. More recently he is involved in working towards environmental digital twins and net-zero greenhouse gas emissions.
His background domain expertise is hydrology. His PhD focused on coupled hydrogeophysical modeling and monitoring, electrical methods such as electrical resistivity tomography (ERT), data assimilation, inversion and imaging, and uncertainty quantification. He remains active in these research areas.
Please consult his personal researcher site for more information: https://cmtso.github.io/
Michael is affiliated with Centre of Excellence for Environmental Data Science (profile) and Lancaster Environment Centre (profile).
Web tools and apps
I have developed a number of Shiny web apps to demonstrate various aspects of environmental data science. Some examples can be found in this draft book.
I have developed a number of Shiny web apps to demonstrate various aspects of environmental data science. Some examples can be found in this draft book.