Teaching
I have had the opportunity to teach statistics and programming to students from a wide variety of backgrounds, including both undergraduate and graduate students in policy and engineering. I truly love teaching and strive to make quantitative topics feel accessible and engaging for all students. I am honored to have received the Dean's Award for Excellence in Student Teaching at Harvard Kennedy School in 2025.
Harvard Kennedy School of Government
API 202Z: Quantitative Analysis and Empirical Methods I
Teaching Fellow
Advanced statistics course in the MPP core curriculum. Topics included regression analysis and practical skills in data analysis.
API 203Z: Quantitative Analysis and Empirical Methods II
Teaching Fellow
Advanced statistics course in the MPP core curriculum. Topics included policy evaluation, causal inference, and machine learning.
API 201: Quantitative Analysis and Empirical Methods I
Teaching Fellow
Introductory statistics course in the MPP core curriculum. Topics included visualizing and summarizing data with R, statistical inference, probability, and decision analysis.
DPI 681M: The Science and Implications of Generative AI
Teaching Fellow
Elective course for graduate-level policy students. Designed and taught the technical complement to the course covering language models, deep learning models, and transformers.
Stanford University
Stanford Code in Place
Section Leader
Part of a teaching team for Code in Place, offered by Stanford during the COVID-19 pandemic, with 10,000 global students and 900 volunteer teachers. Prepared and taught a weekly discussion section of 10–12 students in a 5-week introductory online Python programming course.
MS&E 125: Applied Statistics
Course Assistant
Introductory statistics course in the core curriculum for undergraduate engineering students. Topics included exploring and summarizing data with R, statistical inference, linear and logistic regression models.
MS&E 252: Foundations of Decision Analysis
Course Assistant
Course in decision analysis for graduate and undergraduate students in engineering. Topics included utility theory, sensitivity analysis, and value of information analysis. Recognized by the Stanford Center for Professional Development for excellence in teaching.