Moyi Tian

I am a Postdoctoral Associate in the Department of Applied Mathematics at the University of Colorado Boulder, working with Professor Nancy Rodriguez and Professor David Bortz.

I received my Ph.D. in Applied Mathematics from Brown University in 2024, advised by Professor Björn Sandstede, with a thesis on Patterns in Network Dynamics.

My research lies at the intersection of nonlinear dynamical systems, network science, and pattern formation, with applications to social systems. I study network dynamics from two complementary directions. On the model-driven side, I analyze mechanistic models to understand how network structure and interaction rules drive emergent dynamics, such as patterns and outbursts of collective activity. On the data-driven side, I adapt and extend scientific machine learning methods for learning models from noisy data, including weak-form system identification, weak-form parameter estimation (e.g., my package WENDy-Constrained), and likelihood-based inference, to reveal which mechanisms are at work and when a minimal model is missing key ingredients. My goal is to connect the two: analysis tells us what behaviors to expect and under what conditions, and informs how we learn models from data, while data ground the models in real-world observations.