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 [as Graduate TA]
STAT 761 - Mathematical Machine Learning I, with Professor Chrysos, Spring 2025 [PhD course]
Gave guest lectures on Maximum Likelihood Estimation (MLE) and Kullback-Leibler (KL) divergence - Slides_MLE, Slides_KL
STAT 861 - Mathematical Machine Learning II, with Professor Kandasamy, Fall 2024 [PhD course]
STAT 240 - Data Science Modeling, with Professor Wu, Fall 2023, Spring 2024
STAT 610 - Statistical Inference, with Professor An, Spring 2023 [Graduate course] - Slides
STAT 333 - Applied Regression Analysis, with Professor Wu, Fall 2022
STAT 340 - Data Science Modeling II, with Professor Wu, Spring 2022
STAT 311 - Introduction to Mathematical Statistics, with Professor An, Fall 2021, Fall 2024
University of Wisconsin-Madison [as Academic Tutor to student-athletes]
MATH 112 - Algebra, Fall 2021, Spring 2022, Fall 2022
STAT 301 - Introduction to Statistics, Fall 2021
STAT 324 - Statistics for Engineers, Fall 2021
SOC 360 - Statistics for Sociology, Fall 2021
STAT 371 - Statistics for the Life Sciences, Fall 2021
PUBLHLTH 783 - Statistics for Public Health, Fall 2021 [Graduate course]
Barcelona Tech
Programming, B.S. Statistics, Teaching Assistant to Professor Fairen, Spring 2017
Probability and Stochastic Processes, B.S. Statistics, Teaching Assistant to Professor Delicado, Fall 2016