Teaching

PhD students

I am always seeking ambitious, self-motivated PhD students and am committed to supporting their growth and success.

• Hao Xu (2025 Spring)
• Yuxin Zhao (2024 Spring)

Courses

• The University of Sydney
  • STAT5003 Computational Statistical Methods
    • Data processing: missing data imputation, imbalanced data, cross-validation
    • Supervised learning: linear/nonlinear regression, logistic regression, linear discriminant analysis, kNN, tree-based methods, support vector machine
    • Unsupervised learning: PCA, t-SNE, k-means, hierarchical clustering
    • Statistical methods: MLE, KDE, bootstrap, regularization, model selection, MCMC, simulation
  • STAT3023 Statistical Inference
    • Basics: MGF, transformation of random vectors, exponential families
    • Estimation theory: sufficiency, consistency, UMVUE, CR lower bound
    • Hypothesis testing: UMP test, generalized likelihood ratio test
    • Statistical decision theory: Bayes estimator, (asymptotically) minimax estimator
  • DATA3888 Data Science Capstone
    • Fully project-based
    • Course designer of “Optiver Financial Time Series Data”