Teaching

Teaching Philosophy

I am committed to cultivating the next generation of social data scientists who are dedicated to solving real-world policy problems in public health. Specifically, my teaching philosophy is as follows:

  1. Self-Efficacy: I aim to build students’ confidence by creating a supportive environment that helps them apply analytical tools to real-world problems.

  2. Spatial Thinking: I guide students in developing spatial thinking so they can understand how place shapes social and health outcomes and apply spatial reasoning to evidence-based decisions.

  3. Data to Policy Insight: My courses focus on helping students move beyond technical tasks and use data to generate clear and meaningful policy insights.

Co-instructor

  • Big Data Analysis and Machine Learning for Social Science (Winter 2026, planned)

    • Korea Social Science Data Archive at Seoul National University
    • Class size (expected): 65 graduate students and practitioners
    • A hands-on course on transforming data into insight for social and health science research. I led the daily 2-hour application sessions, where students used R and other open-source tools to build and interpret machine learning models for real-world social and health problems. The focus was on training students to extract meaningful insights from data, not simply to follow scripted exercises.

  • GIS and Spatial Statistics (Summer 2025)
    • Korea Social Science Data Archive at Seoul National University
    • Rating: 9.91 / 10
    • Class size: 50 graduate students and practitioners
    • An applied course on spatial data science in social and health sciences. I taught the 2-hour practical component each day, guiding students through open-source tools such as QGIS, GeoDa, and R to analyze spatial patterns, detect geographic disparities, and generate policy-relevant insights. The course focused not only on technical workflows but also on cultivating spatial thinking for understanding real-world social and health challenges.

  • Advanced GIS and Spatial Statistics (Summer 2023)

    • Korea Social Science Data Archive at Seoul National University
    • Rating: 9.84 / 10
    • Class size: 40 graduate students and practitioners
    • An doctoral-level course designed for students with prior GIS training who seek deeper analytical capability. I led the 2-hour application sessions each day, using open-source tools such as R to guide students through advanced spatial methods (including spatial econometrics, geostatistics, and spatial interpolation) and to help them apply spatial thinking to complex social and health science research questions.

  • Introductory GIS and Spatial Statistics (Summer 2023)

    • Korea Social Science Data Archive at Seoul National University
    • Rating: 9.54 / 10
    • Class size: 65 graduate students and practitioners
    • A foundational course for students new to GIS and spatial analysis. I taught the 2-hour practical component of each class, introducing essential skills in open-source GIS tools (e.g., QGIS and GeoDa) while helping students build core spatial thinking abilities for interpreting maps, analyzing spatial patterns, and understanding geographic context in social and health sciences.

Invited Seminar

  • Using GIS to Improve Rural Health Service Planning: Lessons from Kilifi County, Kenya

    • Kilifi Department of Health, Kilifi County, Kenya (October 15, 2025)
    • Audience: Public officials
    • Delivered a training seminar for officials in the Kilifi Department of Health on applying GIS and spatial analysis to strengthen rural health service planning. The session introduced practical mapping techniques, methods to identify service gaps, and approaches for using geospatial evidence to support maternal and child health strategies in rural areas across Kilifi County.

Guest Lecture

  • Analysis of Variance, undergraduate statistics course (EPPS 2302), UT Dallas, April 2025

  • Probability Distributions, undergraduate statistics course (EPPS 2302), UT Dallas, Feb 2025

  • Introductory Calculus for Statistics, graduate statistics course (EPPS 7313), UT Dallas, Sep 2023

Teaching Assistant

  • UT Dallas

    • Civil Liberties (undergraduate, Fall 2024; Fall 2025)
    • Methods of Quantitative Analysis in the Social and Policy Sciences (undergraduate, Fall 2022; Spring 2023; Spring 2025)
    • Economics for Public Policy (graduate, Spring 2024)
    • Descriptive and Inferential Statistics (graduate, Fall 2023)
    • American National Government (undergraduate, Fall 2021; Spring 2022)

  • Korea Social Science Data Archive at Seoul National University

    • Big Data Analysis and Machine Learning for Social Science (graduate, Winter 2024)
    • Introductory and Advanced GIS and Spatial Statistics (graduate, Summer 2022)

  • Yonsei University, South Korea

    • Quantitative Policy Analysis (undergraduate, Fall 2019)
    • Forecasting Methods and Applications (graduate, Spring 2018)

  • Ajou University, South Korea

    • Introduction to Korean Government (undergraduate, Fall 2017)
    • Research Methods in Public Administration (undergraduate, Spring 2016)