Professional summary

Research Interests

At UKCEH, my 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 I have worked on in the past 3 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 Predatory Bird Monitoring Scheme. More recently, I am involved in working towards environmental digital twins and net-zero greenhouse gas emissions.

My background domain expertise is hydrology. My PhD focused on coupled hydrogeophysical modeling and monitoring, electrical methods such as electrical resistivity tomography (ERT), data assimilation, inversion and imagin, and uncertainty quantification. I remain active in these research areas.

Please consult my personal researcher site for more information:

I am affiliated with Centre of Excellence for Envrionmental Data Science (profile) and Lancaster Environment Centre (profile).


BSc, Geosystems Engineering and Hydrogeology, The University of Texas at Austin (USA), 2012.

MSc, Hydrology, University of Arizona (USA), 2015.

PhD, Environmental Science (hydrogeophysics), Lancaster University, 2019.

Panels, committees and memberships


American Geophysical Union (also member of the hydrogeophysics technical committee)

European Geosciences Union

Formerly fellow of the Geological Society.

I have served as a reviewer for 15+ academic journals. 

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.