All my class webpages are on Blackboard. Below are the most recent syllabi and class schedules from the classes I teach.

  • STAT 515: Statistical Methods I
    Undergraduate course covering basic probability, statistical inference for means and proportions, simple linear regression, analysis of variance, and contingency tables
    [Fall ’24 Syllabus] [Fall ’24 Class Schedule]

  • STAT 517: Advanced Statistical Models
    Undergraduate course covering generalized linear models (GLMs), random effects and mixed effects models, and nonparametric and semiparametric models
    [Fall ’24 Syllabus] [Fall ’24 Class Schedule]

  • STAT 714: Linear Statistical Models
    Graduate course covering matrix algebra, estimation and inference for linear models, Gauss Markov and generalized least squares models, and shrinkage methods
    [Fall ’22 Syllabus] [Fall ’22 Class Schedule]

  • STAT 718: High-Dimensional Data
    Graduate course covering supervised learning, unsupervised learning, methodology for big data, numerical optimization, and deep learning and deep generative models
    [Spring ’23 Syllabus] [Spring ’23 Class Schedule]

  • STAT 721: Stochastic Processes
    Graduate course covering point processes, mathematical finance, Gaussian processes, Markov chain Monte Carlo (MCMC), Dirichlet processes, and reinforcement learning
    [Spring ’24 Syllabus] [Spring ’24 Class Schedule]

In addition, I have supervised the following Independent Studies (STAT 399 or STAT 798):
  • Advanced Topics in Stochastic Processes
  • Advanced Algorithms in Bayesian Computing
  • Introduction to Causal Inference
If you are interested in doing an Independent Study with me, please e-mail me and I will consider your request. Note that Masters and PhD advisees who are conducting thesis research under my supervision should not enroll in STAT 798, but should instead enroll in STAT 799: Thesis Preparation (Masters) or STAT 899: Dissertation Preparation (PhD).