Scholar
Nan Chen
Google Scholar ID: 9u6jLukAAAAJ
Associate Professor of Mathematics, University of Wisconsin-Madison
Citations & Impact
Citations
1,634
H-index
21
i10-index
51
Publications
20
Co-authors
64
list available
Contact
Publications
Resume (English only)
Education
- Ph.D. from Courant Institute of Mathematical Sciences (CIMS) and Center for Atmosphere Ocean Science (CAOS), New York University (NYU), May 2016; advisor: Dr. Andrew Majda
- Postdoctoral Research Associate at CIMS, NYU (June 2016 – May 2018); mentor: Dr. Andrew Majda
- Master’s degree from School of Mathematical Sciences, Fudan University; advisor: Dr. Jin Cheng
- Bachelor’s degree in Mechanical Engineering, Fudan University
- During master's studies, visited Department of Scientific Computing, Florida State University for one year, working with Dr. Max Gunzburger and Dr. Xiaoming Wang
Background
- Associate Professor at the Department of Mathematics, University of Wisconsin-Madison
- Faculty affiliate of the Institute for Foundations of Data Science (IFDS)
- Secretary of SIAM MPE
- Member of a US CLIVAR Working Group
- Research interests include modeling complex systems, stochastic methods, machine learning techniques and applications, digital twins, causal inference, numerical algorithms, geophysics, and general data science
- Focuses on high-dimensional problems, turbulence, and partial information scenarios
- Research topics encompass uncertainty quantification (UQ), data assimilation, information theory, scientific machine learning, applied stochastic analysis, inverse problems, high-dimensional data analysis, and effective prediction
- Develops efficient and statistically accurate algorithms to mitigate the curse of dimensionality in large-dimensional complex dynamical systems with strong non-Gaussian features
- Actively develops both dynamical and stochastic models to predict real-world phenomena in atmosphere-ocean science and other complex systems such as the Madden-Julian Oscillation (MJO), monsoon, El Niño Southern Oscillation (ENSO), and sea ice using observational data
- Recent work involves new UQ and stochastic methods for materials science
- Mathematical and computational tools developed have broad impact across atmospheric-ocean science, materials science, neuroscience, excitable media, physics, and engineering
