Teaching Experience

Politics and Technology (Undergraduate, University of Georgia — Spring 2026) | Syllabus

Through philosophy, social science, and fiction, this course examines the role technology has played in shaping the state in democratic and authoritarian nations — from the printing press and the railroad to artificial intelligence and social media. Students engage with thinkers from Bacon and Rousseau to Heidegger and contemporary political scientists to develop a framework for understanding how technological change reshapes governance, culture, and political life.

Big Data and Artificial Intelligence for Public Administration and Policy (Graduate, University of Georgia — Fall 2025) | Syllabus

An overview of the machine learning and big data methods reshaping public policy and government, with sustained attention to the ethical dilemmas they raise. The course covers the fundamentals of supervised and unsupervised learning, natural language processing, and algorithmic decision-making, then turns to questions of bias, fairness, transparency, and democratic accountability in AI-driven governance.

Introduction to Research Methods in Public Policy (Graduate, University of Georgia) | Syllabus

An introduction to research design, causal inference, and the logic of experiments and quasi-experiments in public policy research.

Data Applications for Public Policy (Graduate, University of Georgia) | Syllabus

Covers probability and statistics, linear and logistic regression, and data acquisition and wrangling, with applications to public policy problems.

Advanced data science Courses

Machine Learning for Policy Analysis (University of Georgia; Princeton University) | Syllabus

Supervised and unsupervised machine learning, natural language processing, and data wrangling, with applications to policy research.

Modern Text Analysis & Machine Learning for Policy Research (University of Georgia) | Syllabus

Natural language processing, supervised and unsupervised learning with text data, and data wrangling for policy researchers.

Applied Machine Learning (UC Berkeley) | Syllabus

Supervised and unsupervised machine learning, natural language processing, and data wrangling. Developed and taught in the UC Berkeley data science curriculum.

Workshops & Short Courses

Causal Inference with AI and Machine Learning (Online — recurring)

An intensive two-day workshop covering modern methods for causal inference with machine learning, including heterogeneous treatment effects, double machine learning, and causal forests. Designed for researchers and practitioners in the social sciences and public policy.

Statistical Computing in Python (University of Bologna School of Economics and Management; Code Horizons)

Data acquisition with APIs, SQL, and web scraping; natural language processing in Python; big data analysis in Python.

Python for Data Analysis (University of Bologna; Emory University; Code Horizons)