Welcome! I am a PhD candidate in the Department of Politics at Princeton University and a fellow of the Program for Quantitative and Analytical Political Science (Q-APS). My interests cover comparative political economy, politics of finance, and Chinese politics. I also do research on statistical methods of causal inference. My work has appeared or will soon appear in Journal of Politics and Research and Politics.
In 2015, my paper on state capacity won the Malcolm Jewell Award for the best graduate student paper presented at the Southern Political Science Association Annual Meeting. In 2017, my colleagues and I won the Fragile Families Challenge for the best statistical prediction of material hardship among disadvantaged children in the United States.
At Princeton, I have had a lot of joy teaching both substantive and methodological courses at various levels. I have taught courses in comparative politics and international relations to undergraduates. I have also taught the third course in my department’s quantitative methods sequence to PhD students, as well as programming language and research design to entering undergraduates via the Freshman Scholars Institute.
Awakening Leviathan: Effect of Democracy on State Capacity. Forthcoming.
Research and Politics
(with Yiqing Xu)
-- Awarded the 2015 Malcolm Jewell Award for the best graduate student paper presented at the SPSA annual meeting.
(PDF, Replication Materials)
Political Backlash to State Intervention: Experimental and Observational Evidence from Chinese Stock Investors. (with Xiao Ma & Jason Q. Guo)
(Poster presented at 2017 Society of Political Methodology)
Authoritarian Power-Sharing through Informal Institutions
PhD-level course, Princeton, NJ, Fall 2016
Preceptor for Prof. Kosuke Imai
Third course in the Politics department’s graduate quantitative methods sequence, covering discrete choice models, machine learning via EM algorithm and variational inference,
models for time-series cross-section data, and event history analysis, all taught with both econometrics perspectives and causal inference perspectives.
(Student evaluations: 4.6/5)
Undergraduate-level course, Princeton, NJ, Spring 2017
Preceptor for Prof. Andrew Moravcsik
This course is an introduction to the causes and nature of international conflict and cooperation. We critically examine various theories of international politics by drawing on examples drawn from international security, economic and legal affairs across different historical eras from 10,000 BC to the present. Topics include the causes of war, the pursuit of economic prosperity, the sources of international order and its breakdown, and the rise of challenges to national sovereignty, and such contemporary issues as international environmental politics, human rights promotion, global terrorism, and the future of US foreign policy.
(Student evaluations: 4.1/5)
Undergraduate-level course, Princeton, NJ, Summer 2017
Preceptor for Prof. Will Lowe
An introduction course to statistics and programming for newly admitted undergrads at Princeton, covering experimental deisgn, predictive modelling, as well as elementary techniques for analysis of network, text and spatial data.
(Student evaluations: 4.8/5)
Undergraduate-level course, Princeton, NJ, Fall 2017
Preceptor for Prof. Rory Truex
This course provides an overview of China's political system. We will begin with a brief historical overview of China's political development from 1949 to the present. The remainder of the course will examine the key challenges facing the current generation of CCP leadership, focusing on prospects for democratization and political reform. Among other topics, we will examine: factionalism and political purges; corruption; avenues for political participation; village elections; public opinion; protest movements and dissidents; co-optation of the business class; and media and internet control.
(Student evaluations: 4.5/5)
PhD-level course, Princeton, NJ, Winter 2018
This camp will prepare students for POL 572 and other quantitative analysis courses offered in the Politics department and elsewhere. Although participation in this camp is completely voluntary, the materials covered in this camp are a pre-requisite for POL 572. Students will learn the basics of statistical programming using R, an open-source computing environment. Using data from published journal articles, students will learn how to manipulate data, create graphs and tables, and conduct basic statistical analysis. This camp assumes knowledge of probability and statistics as covered in POL 571.
(Student evaluations: 4.2/5)