Charlotte Moser

I am a mathematics PhD student at the University of Wisconsin Madison working as a research assistant for Dr. Nan Chen .

I work on the intersection of mathematics and environmental science, using and developing mathematical methods to address environmental issues. On the mathematics side I am working on uncertainty quantification, data assimilation, machine learning and data science, modeling of complex systems, Bayesian inference, information theory, and extreme events. On the application side I study Madden Julian Oscillation, El Nino Southern Oscillation, ocean dynamics, eddies, fluid dynamics, and observational data analysis. I am currently working on identifying and  targeting biases in complex earth systems models as well as systematically integrating insights from complex and idealized models. 

I am passionate about lowering interdisciplinary barriers to build more comprehensive solutions to real-world complex problems in Earth systems science. To that end, Dr. Nan Chen and I have created a video series inspired by our recent perspective article on the evolving role of applied mathematics in Earth system science. Through accessible 5–12 minute presentations, the series introduces topics including dynamical systems, machine learning, data assimilation, and the limits of predictability, with the goal of making modern interdisciplinary geoscience more accessible to students, researchers, and broader audiences.

Building on this effort, we also developed a complementary educational series alongside Dr. Nan Chen and I's book Applied Mathematics Toolkit: Modeling • Data • Algorithms for Scientists and Engineers. Based on a “one concept • one example” philosophy, each short video focuses on a single idea using simple, intuitive examples designed to support learning, review, and conceptual understanding. The series currently includes modules on statistics and machine learning, with additional modules in applied analysis, dynamical systems, and stochastic methods in progress. Presented by myself and shared openly online, the project aims to make applied mathematics more accessible, modular, and reusable across disciplines.