Image from William Putman/NASA Goddard Space Flight Center


I mainly use Python with the scientific stack of libraries (e.g., Numpy, xarray, Pandas, Matplotlib, Jupyter, Dask).

I provided training for scientific computing (Python, Linux, and GitHub). For example, explaining how to speed up Python code.

Complex air quality model

I taught and provided support for a complex air quality model, WRFChem (Bash and Fortran).

Emulation of complex air quality model

Here I used machine learning models (Gaussian process emulation trained from ~20 TB of simulated data from numerical atmospheric models) to predict optimum emission reduction strategies to improve air quality and public health in China.

I provided training for these emulators here (slides and GitHub).

Useful learning resources

Here are some great learning resources for topics in data science, software engineering, and machine learning that I’ve found helpful.