SDS 5800 Topics in Statistics: Optimization Methods For Machine Learning taught by Dr. Ran Chen. This course aims to equip students with both fundamental and advanced optimization techniques and methods relevant to large-scale statistics and machine learning.

SDS 5805 Topics in Statistics: Data Mining with Dr. Carlos Misael Madrid Padilla. This course provides both a practical and theoretical foundation in data mining, emphasizing computational techniques and statistical reasoning. Students will explore supervised and unsupervised learning methods, applying them to real-world datasets across different domains.

SDS 5480 Topics in Statistics: Machine Learning Methods in Biological Sciences with Dr. Bill Katsianos. The goal of this course is to review some key concepts and methods in statistical learning and apply these to analyze large and noisy biological datasets generated by next-generation high-throughput technology.

This coming Spring 2026, we will offer for the first time a course in Causal Inference (SDS 5320) with Dr. Chao Cheng. This course offers a comprehensive introduction to the concepts, methods, and applications of causal inference in the social and biomedical sciences. Topics include the potential outcomes framework, assumptions for causal identification, and a range of statistical approaches for estimating various causal effects in both experimental and observational studies, such as propensity score methods, doubly robust estimators, g-computation methods, instrumental variables estimation, and double machine learning estimators.