Teaching

Math 590-02: Quantitative Methods for Biomedical Data

Fall 2020 — present

This course introduces the quantitative methods for analyzing biomedical data, especially data in genomics. This course will start with introducing domain knowledge and then use the form of case studies to introduce commonly used methods and tools in statistics and computer science. We will discuss how to apply these methods and tools to analyze the data and draw insights and tell stories. The course is highly interdisciplinary, focusing on how to translate a biomedical problem into a quantitative problem and apply appropriate quantitative methods and tools to solve it.

This course aims to prepare students with quantitative majors (math, statistics, biostatistics, computer science, engineering, et al.) to get into the area of biomedical data science.

BIOS 906: Statistical Inference

Fall 2017 — present

This is a Ph.D.-level inference course, required for all Ph.D. students. It discusses both point estimation and hypothesis testing. For the point estimation part, this course is focused on loss function, risk function, optimal estimators under various criteria (UMRU, Pitman estimator, Bayes rule, minimax, etc.), admissibility, large sample asymptotic and their related theorems and algorithms (such as EM algorithm). For the hypothesis testing part, the emphasis is put on power and optimality in hypothesis testing (UMP and UMPU test, etc.), and the relationship between confidence regions and hypothesis testing.

BIOS 707: Statistical Methods for Learning and Discovery

Fall 2014 — Fall 2016

This course is a required course for the data mining track MB students. It introduces commonly-used methods in supervised and unsupervised learning, real data application, coding in R, and scientific plotting.

BIOS 704: Introduction to Statistical Theory and Methods II

Spring 2015

This course is a required course for all MB students. Topics include commonly used distributions, maximum likelihood estimators, method of moment estimators, moment generating functions, Central Limit Theorem, Law of Large Numbers, convergence types, sufficient statistics, confidence intervals, and basic concepts in hypothesis testing.