I am passionate about making interdisciplinary ideas in Applied Mathematics and Earth system science more accessible to broader audiences. Inspired by our recent perspective article on the evolving role of applied mathematics in geosciences, Dr. Nan Chen and I created a public-facing video series exploring how mathematics, data science, and geoscience come together to address challenges such as uncertainty, extreme events, limited observations, multiscale interactions, and climate variability.
Through short 5–12 minute presentations, the series introduces topics including modeling, prediction, uncertainty quantification, dynamical systems, data assimilation, machine learning, and the limits of predictability in Earth systems. Designed for students, early-career researchers, and newcomers to the field, the videos aim to provide accessible scientific storytelling while highlighting both how mathematics helps us understand the Earth system and how geoscience continues to shape modern mathematical development.
The series was developed collaboratively with Dr. Nan Chen, with presentations by myself. We are also grateful to the many collaborators and colleagues whose discussions, feedback, and encouragement helped shape this project, especially Reza Malek-Madani for his thoughtful feedback and support.
Nanjing University Undergraduate Student Seminar August 2026
An accessible introduction to how applied mathematics helps us understand, predict, and reason about complex Earth systems, designed for audiences without specialized mathematical or geoscience backgrounds.
Japan Geophysical Union and American Geophysical Union Joint Meeting 2026 May 2026
An interactive discussion on the role of applied mathematics in geoscience, exploring key opportunities, challenges, and directions for collaboration across the two fields.
Winona State University Mathematics and Statistics in partnership with Biology and Biomedical Science Seminar Series
February 2026
A broad, accessible introduction to how tools such as differential equations, stochastic processes, machine learning, and uncertainty quantification can be used to study problems in Earth science and biology, requiring little specialized background.