Yinling Zhang

zhang2447@wisc.edu

About

I am currently a PhD student at Mathematics Department in University of Wisconsin, Madison, supervised by Prof. Nan Chen. Previously, I received bachelor's Degree at Shanghai Jiaotong University.

Research Interest: Stochastic Model, Data Assimilation, Machine Learning Prediction, Uncertainty Quantification, Scientific Computing, Information Theory, Climate Model

Teaching Experience

  • [2021.09 - 2021.12] Teaching assitant for Calculus and Analytic Geometry I

Publications

A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization

Nan Chen, Yinling Zhang*

Rigorous Derivation of Stochastic Conceptual Models for the El Ni ̃no-Southern Oscillation from a Spatially-Extended Dynamical System

Nan Chen,Yinling Zhang*

Data-Driven Statistical Reduced-Order Modeling and Quantification of Polycrystal Mechanics Leading to Porosity-Based Ductile Damage

Yinling Zhang, Nan Chen*, Curt A. Bronkhorst, Hansohl Choc, Robert Argus

Conferences

2022 SIAM Great Lakes Section Annual Meeting
Wayne State University, September 24, 2022

Topic: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems

2022 Women in Scientific Computing on Complex Physical and Biological Systems
University of Florida, October 24-26, 2022

Topic: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems

2022 SIAM student seminar
University of Wisconsin Madison, October 28, 2022

Topic: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems

2022 Machine Learning for Climate and Weather Applications
University of Chicago, October 31 - November 4, 2022

Topic: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems

2022 AGU Fall Meeting
University of Chicago, December 12 - 16, 2022

Topic1: A Causality-Based Learning Approach for Underlying Dynamics of Complex Dynamical Systems\n Topic2: A Knowledge-Informed Machine Learning Approach to Identify a Hierarchy of Stochastic Conceptual Models for ENSO Complexity

Misc