I am a PhD candidate in applied mathematics specializing in inverse problems, PDE-constrained optimization, and numerical methods, with a focus on identifiability, regularization, and uncertainty quantification.
My research develops mathematical frameworks for ill-posed inverse problems, addressing how reliable information can be extracted from sparse, noisy, and incomplete observations. I am particularly interested in the interplay between model structure, data information content, and uncertainty, within both deterministic and Bayesian formulations.
A defining feature of my work is that these inverse problems are not only ill-posed, but strongly structured by physical constraints, which fundamentally shape identifiability, regularization choices, and uncertainty quantification.
Within this physically constrained setting, a central theme of my work is the inversion of hidden geometric structures from indirect surface observations, where non-uniqueness and limited information content are dominant challenges.
Earth systems such as landslides and slope instabilities serve as a physically meaningful and data-constrained testbed, where I have developed and validated methods for inferring subsurface thickness and basal geometry from surface deformation data.
While my current applications focus on slope instabilities, the underlying mathematical structures naturally extend to a broader class of inverse problems in science and engineering, including other Earth systems and data-limited physical models.
