Software

Image from William Putman/NASA Goddard Space Flight Center

Python

• I mainly use Python with the scientific stack of libraries.

• I created an online course on High Performance Python.

  • It includes profiling, vectorisation with NumPy, compiling with Numba, parallelisation with Dask and Ray, and using GPUs with JAX and CUDA/Numba.

Numerical atmospheric models

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

Machine learning

• I used machine learning models to predict optimum emission reduction strategies to improve air quality and public health in China.

  • These were Gaussian process emulators trained from ~20 TB of simulated data from numerical atmospheric models.

  • I provided training for these emulators video (Colab, slides, and GitHub).

• I created an online course on Introduction to Machine Learning.

  • It covers fundamentals, machine learning with scikit-learn, deep learning with TensorFlow / Keras and PyTorch / PyTorch Lightning, data pipelines, model tuning, transfer learning, and distributed training.

Useful learning resources

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