Teaching

UCLA

as Instructor of Record

  • PIC 16B: Python with Applications II (Fall 2023 – two lectures, Winter 2024, Fall 2024) [syllabus] [schedule] [website]
    • Topics include SQL and polars for big data analytics, interactive visualization, web crawling, webapp development, deep learning libraries, just-in-time compilation, and multithreading.
  • PIC 16A: Python with Applications I (Winter 2024, Fall 2024) [syllabus] [schedule]
    • Topics include Python basics, object-oriented programming, numerical computation, visualization, data wrangling, and machine learning.
  • BIOSTAT 203C: Introduction to Data Science in Python (Spring 2024)
    • Developed a new course for the Master of Data Science in Health program hosted by the Biostat department. The Biostat department’s first offering for course in Python.

as Workshop Instructor

  • Collaboratory Workshops:
    • Quarterly 3-hour, 3-day workshop series tailored to individuals in the biosciences community who are interested in learning data analysis, programming and statistical techniques. Students can earn credits for BIOINFO 275A/B by taking these workshops.
    • Machine Learning with Python (6 times in 2021-22; 2023-25)
    • Introduction to Python (3 times in 2022-23)

Seoul National University

as Teaching Assistant

  • 326.211 Probability Concept and Applications (Spring 2020)
    • Basic probability concepts, theories and their applications to related fields such as natural science, engineering, and social science.
  • 326.212 Statistical Computing and Lab. (Fall 2014, Fall 2015)
    • Lower-division programming course in R and C. Developed and managed course materials on GitHub.
  • M1399.000100 Computational Statistics (Spring 2015, Spring 2016, Spring 2017)
    • Upper-division course on computational methods in Statistics. Graded homeworks/exams. Homeworks managed on GitHub.
  • M1399.000200 Advanced Statistical Computing (Fall 2016, Fall 2019)
    • Graduate-level course on convex optimization and computation methods in Statistics. Graded homeworks using Julia. Course managed on GitHub.