Libo graduated with MSc in Statistics from Changsha University of Science and Technology, with training at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Her Master’s project involved studying the application of deep learning models in financial markets. She also studied probability theory, especially the probabilistic representation of three-dimensional Navier-Stokes equations. Libo is interested in numerical analysis, particularly its application in weather forecasting. Outside of mathematics, she enjoys playing basketball, traveling, and calligraphy.

Project title:
Learning Dynamical Systems From Real Weather Data

Supervisor(s):
Lisa Kreusser, Chris Budd

Project description: 
This project focuses on enhancing weather prediction by merging mathematics, data science, and meteorology. It unfolds in two phases: the first involves learning weather system dynamics from simulated data, using mathematical and data science techniques to uncover the underlying dynamical system. The aim here is to accurately represent these dynamics, laying a solid foundation for real-world applications. The second phase shifts to real-world weather data, applying learned dynamics to improve forecast accuracy and reliability. The ultimate goal is to contribute to more precise weather forecasting.