Teaching
Instats Webinars
A 3-day intensive webinar that trains social scientists with no machine learning background to use large language models for measurement and data work. Day 1 introduces stance-detection encoders and generative decoders, and when to choose each. Day 2 builds a defensible quantitative proxy via hypothesis ensembles, gold-set coding, and validation. Day 3 applies generative models to crawling and data wrangling. All sessions run live in Google Colab with demos on real social science text.
Teaching Record
Instructor
Teaching Assistant
Course Proposals
This course trains students to use large language models (LLMs) for real-world data workflows. Students learn to clean, link, and structure complex textual data using both discriminative and generative models. The course emphasizes selecting and applying appropriate open-source models, such as LayoutLM, SentenceTransformers, DeBERTa, and GPT, for specific analytical tasks including data cleansing, record linkage, stance detection, fine-tuning, and structured data extraction from unstructured sources. Students also gain practical experience leveraging GPU computing resources through Google Colab for efficient model training and inference.
This course is designed to familiarize students with the empirical tools widely used in political science in particular and in social science more broadly. Throughout this course, students will learn data analysis and statistical inference techniques using R and RStudio, including descriptive statistics, hypothesis testing, quasi-experimental methods, and regression analysis.
This course aims to introduce the literature of climate politics to students, taking a political economy approach. Students can expect to learn various topics related to climate politics, including but not limited to microfoundational factors embedded in climate politics, as well as redistributive implications. The course culminates with the final term paper assignment, where students are expected to craft their own empirical research ideas to explore a particular topic of their interest they have learned in the course.