Winter, 2026. Syllabus. 

This course will provide an introduction to the theory and practice of combining data sources together to address questions of interest across different scientific domains. Special emphasis is given to data fusion applications in public health, medical sciences, policy learning, and machine learning. Students will review, compare, and evaluate statistical methods used in data fusion, focusing on each method's underlying assumptions, strengths, limitations, and practical implementation challenges. Specific techniques covered will include test-then-pool strategies, inverse probability weighting, bias correction methods, semiparametric estimation approaches, and Bayesian dynamic borrowing. We will also touch briefly on fusing functional data, and data with different modalities through optimal transport. 

Fall 2025. Syllabus.

This course will provide an introduction to the design, conduct, and analysis of randomized clinical trials and experimental designs more broadly. The first half of the course will focus on clinical trials, covering topics including randomization, power and sample size, endpoints, surrogate markers, blinding, interim analyses, and data monitoring. The latter half of the course will introduce methods and challenges for online experimentation, field experiments, and experimenting in real-world settings in general. Topics include A/B testing, adaptive designs, interference, spillover effects and switchback designs.