Stochastic Methods for Modeling and Predicting Complex Dynamical Systems

Nan Chen, Ph.D., is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison. He is also a Faculty Affiliate of the Institute for Foundations of Data Science. Dr. Chen received his Ph.D. from the Courant Institute of Mathematical Sciences and the Center of Atmosphere and Ocean Science at New York University.  Dr. Chen's research interests include contemporary applied mathematics, stochastic modeling, data assimilation, uncertainty quantification, geophysical fluids, dynamical systems, scientific computing, machine learning, and general data science. He is also active in developing both dynamical and stochastic models and uses these models to predict real-world phenomena related to atmosphere-ocean science, climate, geophysics, and many other complex systems such as the Madden-Julian Oscillation (MJO), the monsoon, the El Nino-Southern Oscillation (ENSO), and the sea ice based on real observational data.  Dr. Chen's research work has beenpublished in top journals in both applied mathematics and many interdisciplinary areas.

• Introduction to Complex Systems, Stochastic Methods, and Model Error.- Basic Stochastic Toolkits.- Introduction to Information Theory.- Numerical Schemes for Solving Stochastic Differential Equations.- Gaussian and Non-Gaussian Processes.- Data Assimilation.- Simple Data-driven Stochastic Models.- Conditional Gaussian Nonlinear Systems.- Parameter Estimation with Uncertainty Quantification.- Ensemble Forecast.- Combining Stochastic Models with Machine Learning.