I’m a PhD candidate conducting research in the development of a machine learning (ML) approach to the inversion of magnetotelluric (MT) data in geothermal systems. Broadly my research interests include: magnetotellurics (MT) and controlled-source electromagnetic (CSEM) methods, machine learning in physical sciences (ML-Physics coupled problems), geophysical inverse problems (including joint inversion methods), and uncertainty quantification. I’m interested in applying these methods to research areas that include: geothermal and hydrothermal systems, structure of the lithosphere, and role of geophysics in the energy transition (geothermal energy, critical minerals, carbon capture and storage).