teaching

Empower students of statistics and data science with customization and motivating applications.

I regard the opportunity to teach, mentor, and interact with students as the utmost privilege. As a statistician and data science educator, my teaching philosophy is to empower my students so that they can approach their pursuit of statistics with curiosity and confidence. Furthermore, I strive to reduce barriers to statistics and data science for all students, regardless of their previous mathematical background.

Below is a selected list of my teaching and mentoring experiences:

Johns Hopkins Bloomberg School of Public Health

Project Supervisions at Johns Hopkins

  • Can Wang (ScM Biostatistics). Evaluating LLMs for clinical question answering; co-author on arXiv:2509.10843 with ongoing work on AI agents for causal effect estimation.
  • Cathy Zhang (ScM Biostatistics). Full-text methods quality assessment with LLMs; co-author on Large Language Models for Full-Text Methods Assessment: A Case Study on Mediation Analysis; poster at IUS-NISS Conference on AI & Statistics 2025 supported by a Joseph Zeger Travel Award.
  • Shengyi Li (ScM Biostatistics). Distributional matrix completion for gene perturbation prediction; poster at MLCB 2025 supported by a Joseph Zeger Travel Award.
  • Moran Gao (ScM Biostatistics). Prediction-powered inference for single-cell studies.

Stanford University

University of Washington

I have served as a Teaching Assistant for the following courses and have won an Outstanding Teaching Assistant Award in the School of Public Health at the University of Washington:

  • Longitudinal Data Analysis (BIOSTAT 540; Graduate-level; Course rating: 4.7/5.0 (n = 47); Instructor: Katie Wilson)
  • Categorical Data Analysis (BIOSTAT 536; Graduate-level; Instructor: Katie Kerr)
  • Introductory Laboratory-Based Biostatistics (UCONJ 510; Graduate-level; Instructor: Lloyd Mancl)
  • Machine Learning for Biomedical and Public Health Data (BIOSTAT 546; Graduate-level; Instructor: Daniela Witten)
  • Summer Institute in Statistical Genetics (Instructors: Ken Rice & Ting Ye)
  • Summer Institute in Statistics for Clinical & Epidemiological Research (Instructors: Katie Wilson & Anna Plantinga)

I’ve also given guest lectures and led weekly discussions in the following classes:

  • Machine Learning for Biomedical and Public Health Data (BIOSTAT 546): Delivered guest lectures on decision trees, support vector machines, and principal component analysis.
  • Categorical Data Analysis (BIOSTAT 536; Graduate level).

Mentor for the Directed Reading Program at University of Washington

  • Mentored undergraduate students on topics of identification in missing data and causal inference.
  • My mentee’s presentation is linked here.

Teaching Assistant at University of California, Berkeley

  • Introduction to Machine Learning (CS 189/289A; Advanced undergraduate level; Fall 2016 & Spring 2017).
  • Discrete Mathematics and Probability (CS 70; Undergraduate-level; Summer 2016 & Spring 2017).