Teaching

Instructor

  • EPPS Math and Coding Camp (Fall, 2024)

    • The University of Texas at Dallas

    • In-person Class

    • The EPPS Math and Coding Camp is designed to provide first-year EPPS graduate students with a week of training in fundamental mathematics and basic programming. The camp focuses on equipping and inspiring students with applicable math and data analysis techniques for their upcoming graduate research methods courses. I managed the program in this camp and taught students coding, primarily focusing on R.

    • EPPS Math and Coding Camp homepage

  • GIS and Spatial Statistics for Social Science: Advanced (Summer, 2024)

    • Korea Social Science Data Archive at Seoul National University

    • Online Class

    • This course is designed for students or researchers with foundational experience in spatial data analysis or GIS (Geographic Information System). Building on GIS concepts, it offers hands-on practice with advanced spatial statistical methodologies, including the 2-Step Floating Catchment Area (2SFCA), Generalized Linear Mixed Models (GLMM), and spatiotemporal Bayesian models, using the R programming language.

    • Instructor Evaluation: 9.78/10

  • GIS and Spatial Statistics for Social Science: Basic (Summer, 2024)

    • Korea Social Science Data Archive at Seoul National University

    • Online Class

    • This course is designed to familiarize students with GIS (Geographic Information System) techniques, including mapping, buffer analysis, nearest neighbor analysis, and spatial regression models. Students will learn to analyze map data and various spatial datasets statistically, derive spatially meaningful insights, and practice basic spatial analysis methodologies using real-world social science data.

    • Instructor Evaluation: 9.81/10

  • Big Data Analysis and Machine Learning for Social Science (Winter, 2024)

    • Korea Social Science Data Archive at Seoul National University

    • Online Class

    • This course aims to establish theoretical foundations to understand how machine learning techniques, actively developed and applied in recent years in science and engineering, can be utilized to analyze big data constructed in the social sciences. After grasping the basic structures of representative supervised and unsupervised learning methods, our main objective is to conduct practical exercises using R packages with various examples.

    • Instructor Evaluation: 9.70/10

  • EPPS 2302. Methods of Quantitative Analysis in the Social and Policy Sciences (Fall, 2023)

    • The University of Texas at Dallas
    • In-person Class
    • This course introduces basic concepts and methods of statistical analysis used in different fields of social and policy science research to better understand human relationships and the impacts of government action on them. Topics include data description, using probability to assess the reasonableness of claims about the world based on sample data, exploring cause-effect interactions through regression models, and application of software to ease visualization and calculation. Students completing this course will be good consumers of statistical information and have a solid foundation for pursuing further study of quantitative analysis.
    • Syllabus
    • Instructor Evaluation: 4.75/5

Guest Lectures

  • SOC 4385 Global Health and Society (Spring, 2024)

    • The University of Texas at Dallas

    • In this guest lecture for the Global Health and Society course, I introduced the concepts of spatial statistics and mapping as they apply to the social sciences. The lecture was designed to provide theoretical knowledge and practical skills, focusing on the use of GeoDa software. This lecture began with an overview of spatial statistics, discussing its importance and relevance in analyzing geographic and demographic data. Following the theoretical introduction, we transitioned to a hands-on session using GeoDa, a user-friendly spatial data analysis software. This lecture explored how to create various types of maps, such as choropleth maps, to visualize data distributions and identify spatial patterns. The practical session included exercises on how to apply spatial statistical methods to real-world data, allowing students to gain a deeper understanding of the techniques discussed earlier.

Teaching Assistants

  • Advanced GIS and Spatial Statistics

    • Korea Social Science Data Archive at Seoul National University (Summer 2023)

    • Rating: 9.84/10

  • Introductory GIS and Spatial Statistics

    • Korea Social Science Data Archive at Seoul National University (Summer 2023)

    • Rating: 9.54/10

  • EPPS 7316. Regression and Multivariate Analysis (Instructor: Dr. Patrick Brandt, Spring, 2024)

  • EPPS 7316. Regression and Multivariate Analysis (Instructor: Dr. Patrick Brandt, Spring, 2023)

  • EPPS 7313. Descriptive and Inferential Statistics (Instructor: Dr. Dohyeong Kim, Fall, 2022)

  • PPPE 6321. Economics for Public Policy (nstructor: Dr. Dohyeong Kim, Spring, 2022)