Stochastic Methods for Modeling and Predicting Complex Dynamical Systems

Nan Chen, Ph.D., is an Associate Professor at the Department of Mathematics, 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, New York University (NYU), in 2016. He worked as a postdoc research associate at NYU for two years before joining UW-Madison. Dr. Chen's research interests lie in applied mathematics, geophysics, complex dynamical systems, stochastic methods, numerical algorithms, and general data science. He is also active in developing dynamical and stochastic models and using these models to analyze and predict real-world phenomena related to atmosphere-ocean science, climate, and other complex systems with the help of real observational data.  He has received several awards, including the Kurt O. Friedrichs Prize for an outstanding dissertation in mathematics and the Young Investigator Award from the Office of Naval Research.

• Stochastic Toolkits.- Introduction to Information Theory.- Basic Stochastic Computational Methods.- Simple Gaussian and Non-Gaussian SDEs.- Data Assimilation.- Optimal Control.- Prediction.- Data-Driven Low-Order Stochastic Models.- Conditional Gaussian Nonlinear Systems.- Parameter Estimation with Uncertainty Quantification.- Combining Stochastic Models with Machine Learning.- Instruction Manual for the MATLAB Codes.