Welcome

I am an Associate Professor (with tenure) in the Department of Political Science at the Massachusetts Institute of Technology. I am also a faculty affiliate of the Institute for Data, Systems, and Society (IDSS) at MIT. I specialize in political methodology and develop statistical methods for questions in political science and the social sciences. Specifically, I work on causal inference and machine learning methods.

My current research programs focus on three areas:

• External Validity
• Causal Inference with Network and Spatial Data
• Machine learning and AI for the Social Sciences

Please see my representative papers below and Research for the overview of my research areas. My work has appeared or is forthcoming in various academic journals, such as American Political Science Review, American Journal of Political Science, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B), NeurIPS, and Proceedings of the National Academy of Sciences (PNAS).

My contributions have been recognized with various awards. In 2025, I received the Emerging Scholar Award from the Society for Political Methodology, which ``honors a young researcher, within ten years of their degree, who is making notable contributions to the field of political methodology.'' For my broad methodological contributions, I also received the Best Paper Awards from three different sections of the American Political Science Association: the Political Methodology section (2019, 2025), the Experimental Research section (2024), and the Political Networks section (2022). In 2017, I also received the John T. Williams Dissertation Prize from the Society for Political Methodology.

Before joining MIT, I was an Assistant Professor at Columbia University from 2020 to 2025. I received a Ph.D. from Princeton University (2020) and a B.A. from the University of Tokyo (2015). I was a visiting graduate student fellow in the Department of Government at Harvard University from 2018 to 2020. I also studied at the University of Michigan as a visiting student in 2013.

Representative Papers

1. External Validity

• "Elements of External Validity: Framework, Design, and Analysis."
  American Political Science Review (2023).

• "Designing Multi-Site Studies for External Validity: Site Selection via Synthetic Purposive Sampling."
  American Journal of Political Science (Conditionally Accepted)
  [R package]
  

2. Causal Inference with Network and Spatial Data (Causal Inference with Interference)

• "Identification and Estimation of Causal Peer Effects Using Double Negative Controls for Unmeasured Network Confounding."
  Journal of the Royal Statistical Society: Series B (2024).
  

• "Spillover Effects in the Presence of Unobserved Networks."
  Political Analysis (2021).

3. Machine Learning and AI for the Social Sciences

• "Using Large Language Model Annotations for the Social Sciences: A General Framework of Using Predicted Variables in Downstream Analyses."
  Revise and Resubmit at American Journal of Political Science.
  [R package]
  This paper generalizes methods proposed in our paper published in NeurIPS (2023).

• "Causal Interaction in Factorial Experiments: Application to Conjoint Analysis."
  Journal of the American Statistical Association (2019).
  

Recent Invited Talks

• In 2026, I am scheduled to give talks at Harvard, Yale, Rutgers, NUS, and the Institute for Mathematical and Statistical Innovation (IMSI) workshop.
• In October 2025, I will give a talk at Tufts University.
• In February 2025, I gave a talk on multi-site studies for external validity at the Marketing seminar in Columbia Business School.
• In October 2024, I gave a talk on how to use large language models (LLMs) in the social sciences at the Department of Political Science in the Ohio State University.
• In June 2024, I gave a talk on how to use large language models (LLMs) in the social sciences at the NLP+CSS workshop at NAACL 2024.
• In May 2024, I gave a talk on how to use large language models (LLMs) in the social sciences and organize a session on external validity at the American Causal Inference Conference.
• In December 2023, I gave a talk at Waseda University about my paper on multi-site studies for external validity.
• In December 2023, I gave a talk at University of Tokyo about my paper on multi-site studies for external validity.
• In July 2023, I gave talks at PolMeth about my paper on multi-site studies for external validity and my paper on large language models.
• In May 2023, I gave a talk at MIT about external validity.
• In April 2023, I gave a talk at Harvard about external validity.
• In March 2023, I gave a talk at the University of Tokyo about my paper on causal inference with texts.
• In February 2023, I gave a talk at the University of North Carolina at Chapel Hill about my paper on external robustness.
• In October 2022, I gave a talk at the Quantitative Methods in the Social Sciences Program at Columbia University about my paper on external robustness.
• In October 2022, I gave a talk at the INFORMS Annual Meeting about my paper on causal peer effects.
• In April 2022, I gave a talk at Stanford about a new paper on external robustness.
• In March 2022, I gave a talk at the Department of Economics in Columbia about my paper on causal peer effects.
• In February 2022, I gave a talk at Yale about my paper on causal peer effects.