About

I’m a Postdoctoral Research Scientist in the Department of Oceanography at the University of Hawaiʻi at Mānoa, where I work with Dr. Brian Powell developing scientific machine learning methods for complex dynamical systems in ocean modeling and data assimilation.

Research Focus

I develop scientific machine learning methods for understanding and forecasting complex ocean dynamical systems. My work combines data-driven models with mechanistic knowledge to create interpretable tools that respect physical laws while learning directly from observations.

A core focus of my research is using machine learning to improve data assimilation. I’m developing physics-informed surrogate models and neural network-based tangent linear models that replace hand-crafted components, such as numerical tangent-linear and adjoint operators, with differentiable architectures generated automatically through modern automatic differentiation. These approaches accelerate Four-Dimensional Variational Data Assimilation (4D-Var) while preserving the physical structure and interpretability required for scientific applications.

This work is supported by the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES).

Background

I completed my PhD in Computational Engineering at Durham University in 2023, where I developed sparse regression methods for discovering governing equations from time-series data. My PhD research resulted in the ARGOS R package and a publication in Communications Physics. I also hold an M.S. in Data Science from Northwestern University and a B.A. in Statistics and Mathematics from Wittenberg University.

I’m passionate about developing open-source tools that advance research across domains and addressing real-world challenges in climate and sustainability through methodological innovation.