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
- PH.140.850 - How to AI (for public health) (First Term 2025) - Course instructor
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).