Thomas Monahan BSc

Thomas Monahan is a second year DPhil candidate in the Environmental Fluids and Machine Learning research groups at the University of Oxford. His work looks to develop new methodologies for predicting and analyzing tides and tidally driven phenomena across a range of spatiotemporal scales.

Prior to his DPhil, Thomas carried out research across several disciplines; applying data-driven machine learning to enhance the study of quantum phenomena in cold atom experiments as well as study Exoplanet atmospheres during his time at NASA's Goddard Space Flight Center. He is particularly interested in how scientific machine learning can enhance the process of scientific discovery and is interested in exploiting these techniques to study anthropogenically driven tidal change, and storm surge physics. A central focus of his work is developing operational and opensource tools with an eye towards industry. Thomas is supervised by Thomas Adcock, Tianning Tang, and Stephen Roberts.

• Tidal analysis and prediction
• Satellite altimetry -
• Storm Surge
• Sea level rise and tidal change 
• Scientific Machine Learning

Variational Bayesian Harmonic Analysis: Developing a framework for tidal and mean sea surface corrections from and for the Surface Water Ocean Topography mission using a spatially coherent variational Bayesian harmonic analysis (under review JGR: Oceans).

Response Framework: Tidal analysis and prediction through physics-informed ML: A new non-parametric framework for analysis of complex tidal phenomena under external forcing such as storm surge, tidal rivers, and interactions with mean-sea-level.

(under review: ProcRSoc A) Tidal analysis from shortened records: Theoretical analysis of "super-resolution" using newly developed harmonic and Response methods.

Spatial characteristics of nonlinear coastal and estuarine tides from the Surface Water Ocean Topography mission: Leveraging new wide-swath satellite altimetry along with new empirical analysis techniques to study nonlinear characteristics of ocean tides in coastal regions.

(In progress) AutoSSA:A fully non-parametric singular spectrum analysis tool for intelligent signal decomposition and denoising (In progress) 

Open Source Code: rtide: Python implementation of the "Response Framework" vtide: Python implementation of the variational Bayesian harmonic analysis