Teaching

Philosophy

From pre-college all the way to PhD-level courses, I always strive to teach in a way that the main takeaways reveal themselves as ‘obvious’ or at least natural in light of previous discussion (“If you can’t explain it simply, then you don’t understand it well enough” - Albert Einstein). I seek to instill understanding, not mindless regurgitation, and for this I use plenty of simple examples and emphasize the bases of the topics under consideration: a solid foundation will get you further than you may think. Lastly, effective teaching must be engaging, which informs my presentation style and priorities (as resources, chiefly, time, are limited).

University of Wisconsin-Madison (Graduate Teaching Assistant)

1. ECE 761 - Mathematical Machine Learning I, with Professor Chrysos, Spring 2025 (PhD course)
   • Gave guest lectures on Maximum Likelihood Estimation and Kullback-Leibler divergence
2. CS 861 - Mathematical Machine Learning II, with Professor Kandasamy, Fall 2024 (PhD course)
3. STAT 610 - Statistical Inference, with Professor An, Spring 2023 (Graduate course)
   • Led weakly seminars on core topics
4. STAT 240 - Data Science Modeling, with Professor Wu, Fall 2023, Spring 2024
5. STAT 333 - Applied Regression Analysis, with Professor Wu, Fall 2022
6. STAT 340 - Data Science Modeling II, with Professor Wu, Spring 2022
7. STAT 311 - Introduction to Mathematical Statistics, with Professor An, Fall 2021, Fall 2024