Ph.D. in Statistics with expertise in statistical learning, causal inference, and machine learning. My work focuses on the statistical foundations of predictive models, including inference, uncertainty quantification, and model evaluation.
Structured materials on statistical machine learning, covering predictive modeling, model evaluation, and modern learning methods from a statistical perspective.
→ https://weili-code.github.io/StatisticalLearning
Notes on statistical computation, including optimization, Monte Carlo methods, and Markov chain Monte Carlo (MCMC).
→ https://weili-code.github.io/StatisticalSimulation
Applications of deep learning models across multiple data modalities.
→ https://github.com/weili-code/DeepLearning_pytorch
From-scratch implementation of neural networks to study training dynamics and model behavior.
→ https://github.com/weili-code/DeepLearning_numpy
This site serves as a central portal for technical materials and projects, with an emphasis on statistical perspectives in machine learning.