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Courses taught at Colorado State University

STAR 511: Design and Data Analysis for Researchers I (Fall 2023)

  • Graduate-level;
  • Probability distributions, inferences about population mean and variances, multiple comparisons, categorical data analysis, linear regression and correlation, etc.

Courses taught at Tsinghua University

Advanced Statistical Methods (Fall 2022)

  • Graduate-level (Ph.D., required for qualifying exam); taught in English;
  • Linear mixed model, GLM, MLE, ANOVA, survival analysis, etc.

Introduction to Biostatistics (Spring 2022, Spring 2023)

  • Undergraduate-level; taught in English;
  • ANOVA, categorical data analysis, survival analysis, design and analysis of clinical trials, etc.

Advanced Topics in Statistics IV: Statistical Genetics (Spring 2022)

  • Graduate-level (Ph.D.); taught in English & Mandarin;
  • Genome-wide association analysis, functional genomics, integration of functional genomics and genetic data, gene-environment interactions, single cell transcriptomics, deep learning for regulatory genomics, etc.

High Dimensional Statistics (Fall 2021)

  • Graduate-level (Ph.D.); taught in English;
  • Optimization theory, LASSO and generalizations of the LASSO penalty, theory for LASSO prediction and variable selection consistency, etc.

Multivariate Statistical Analysis (Spring 2021)

  • Undergraduate-level; taught in English & Mandarin;
  • Matrix algebra, multivariate normal distribution, principal component analysis, discriminant analysis, factor analysis, canonical corelation analysis

Courses at Texas A&M University

Instructor (Full responsibility, Summer 2016):

  • STAT 303 Statistical Methods

    • Introduction of probability and probability distributions; sampling and descriptive measures; inference and hypothesis testing; analysis of variance; linear regression (no calculus background required)

Teaching Assistant (Fall 2014-Spring 2016):

  • STAT 211 Principle of Statistics I

    • Introduction to probability and probability distributions; sampling and descriptive measures; inference and hypothesis testing; analysis of variance; linear regression (calculus background required)

  • STAT 212 Principle of Statistics II

    • Similar as STAT 211 but for students from different majors.

  • STAT 630 Overview of Math STAT

    • Introduction to mathematical statistics for graduate students: probability theory, theory of statistical inferences and Bayesian methods.

  • STAT 689 Semiparametric Regression using R

    • Introduction to semiparametric regression methods and analyze data in R. Topics include generalized linear models, nonparametric regression, partially linear models, additive models, grouped data, longitudinal data, etc.