Teaching

*: upcoming.

• Reading Group
  • 2018 Fall-2019 Winter - Statistical Learning Reading Group
• Instructor
  • *2025 FALL - Theory and Application of Longitudinal Analysis (BIOSTAT653 Section 1), 65 Biostat MS and Doctoral Students
  • 2024 FALL - Theory and Application of Longitudinal Analysis (BIOSTAT653 Section 1), 55 Biostat MS and Doctoral Students
  • 2023 FALL - Theory and Application of Longitudinal Analysis (BIOSTAT653 Section 1), 40 Biostat MS and Doctoral Students
  • 2022 FALL - Theory and Application of Longitudinal Analysis (BIOSTAT653 Section 1), 50 Biostat MS and Doctoral Students
  • 2022 Winter - Biostatistical Analysis For Health-Related Studies (BIOSTAT522), 158 Non-Biostat Graduate Students
  • 2021 Fall - Theory and Application of Longitudinal Analysis (BIOSTAT653)
  • 2020 Fall - Theory and Application of Longitudinal Analysis (BIOSTAT653)
  • 2019 Fall - Applied Statistics III: Longitudinal Analysis (BIOSTAT653)
  • 2018 Fall - Applied Statistics III: Longitudinal Analysis (BIOSTAT653) [Canvas site] [syllabus]
  • 2017 Fall - Statistical Methods for Epidemiology (BIOSTAT523) [Canvas Site][syllabus]
  • 2016 Fall - Statistical and Computational Methods for Learning through Graphical Models (Advanced Topics in Biostatistics; BIOSTAT 830), Department of Biostatistics, University of Michigan.
  • 2014 Statistical Methods for Individualizing Health. Mayo Clinic, November 17, Rochester, MN. (Short course taught with Prof. Scott Zeger)
    
    
• Massive Open Online Courses (MOOCs)
  • 2013 Case-based Introduction to Biostatistics, www.coursera.org, Teaching Staff for Prof. Scott Zeger.
    
    
• Slides
  • Network. 2017 Big Data Summer Institute. [slides]
  • Visualization for Individualized Health (ggplot2 lecture) [Slides][R code][RMarkdown]
  • Methods in Biostatistics (140.653; Lecture 8) [slides] [.tex]
  • A Survey of Automatic Bayesian Software and Why You Should Care. Hopkins Biostatistics Student Computing Club. [slides]
  • Exploring the Posterior Distribution by Markov chain Monte Carlo. Hopkins Biostatistics Student Computing Club. [R Code]