A.    Teaching Experience

A-1. Private-Academy Tutoring: I taught AP Economics at a private academy for three years. I consistently received positive feedback from both students and staff, which led to yearly contract renewals.

A-2. Summer Workshop at SNU Business School: Based on strong evaluations from a previous small-group study session, I am preparing to deliver a four-session Generative AI workshop to graduate students at SNU Business School in Summer 2025. The syllabus is structured as follows:

Session 1: Python Basics

-       Environment setup (Python, Google Colab)

-       Core syntax: file I/O, data types & variables, conditional logic & loops, functions & packages, indexing & slicing, string formatting

-       Working with pandas DataFrames

Session 2: APIs and Data Acquisition

-       Why APIs? Big data challenges, Transform unstructured data into structured formats

-       Motivating Dataset: Search query logs

-       Hands-on: Making Naver Shopping API calls with Python to identify product categories, parsing responses into pandas

Session 3: Text Analysis with LLMs vs. Classical NLP

-       LLM Techniques: Prompt engineering, few-shot learning, chain-of-thought

-       Evaluation metrics: precision, recall, F1, Cohen’s κ

-       Motivating Dataset: CFPB Consumer Complaint dataset

-       Hands-on 1: Classical NLP (topic modeling)

-       Hands-on 2: Calling the OpenAI API with Python, parsing outputs into pandas

Session 4: Causal Inference using Difference-in-Differences

-       Identification & Classic DiD: Key assumptions and estimation

-       Extensions: Understanding heterogeneous timing, pitfalls of standard TWFE

-       Recent Methods (Borusyak et al. 2024, Callaway and Sant’Anna 2021, De Chaisemartin and d’Haultfoeuille 2020, Sun and Abraham 2021)

-       Robustness Checks: Placebo simulations, event study plots, sensitivity tests of parallel trends

B.    Curriculum Qualifications

B-1. Curriculum Innovation: I am working with Professors Sang Pil Han (ASU) and Donghyuk Shin (KAIST) to integrate an “Agentic AI” module into the Machine Learning in Business curriculum at both institutions. Students will experience automated end-to-end workflows for model selection, hyperparameter tuning, and evaluation, while learning to identify which steps still require human oversight. This module prepares students for AI‑augmented decision‑making systems in practice.

B-2. Relevant Coursework: I completed three sequential courses at the Graduate School of Data Science, Seoul National University.

Big Data and Knowledge Management Systems: In this course, I learned the theoretical foundations of data management and relational databases. I learned how to design a database given specific business contexts and gained hands-on experience with PostgreSQL and Neo4j. For our team project, we built an end-to-end, LLM-powered application using Streamlit. We designed real-time data pipelines that captured user prompts, stored them in a database, retrieved them, and fed them back into the LLM to drive interactive responses.

Computing for Data Science I & II: These two courses covered the core principles of object-oriented programming, core data structures (arrays, linked lists, trees, graphs), and algorithms (sorting, searching, traversal). Weekly assignments were in Python and C++.