Peer Reviewed Journal Articles

  1. “How Ideology Fuels Affective Polarization,” with Jon Rogowski. Political Behavior, 2016, 1-24. [Paper] [Supplemental Information]

Working Papers

  • “On the Comparison of Political Texts.” 2018.
  • “Communication at Scale in Elections, and the Importance of Costly Signaling to Voter Mobilization” with Donald P. Green. 2018.
  • “Have State Policy Agendas Become More Nationalized?” with Daniel M. Butler. 2018.
  • “Mansplaining the Law: The Effect of Gender on Interruptions in Congressional Testimony,” with Michael Miller. 2018. [Link]
  • “Are Governors More Likely to Keep Their Promises Under Unified Government? Agenda Speech and Legislative Outcomes.” 2017.

Research Notes

  1. “On the Use of Topic Modeling in Diff-in-Diffs.” 2018.
  2. “Scaling the Tower of Babel: What Multilingual Central Bank Communications Teach Us About Interest Rate Expectations.” 2015. [Note]
  3. “Political Surrogates, Minimal Effects, and Campaign Dynamics in the 2012 Presidential Election.” 2016. [Note]

Current Projects and Datasets

  • State of the State Addresses, 1880–Present (SOTS), with Daniel M. Butler. In development, 2015–Present. Gubernatorial State of the State addresses, like small State of the Union addresses for each U.S. state, are demonstrative of the policy goals of the Governor and her party. NSF Honorable Mention.

Conference Presentations and Invited Talks

  1. “On the Comparison of Political Texts,” Department of Political Science, Penn State, 2018 (Scheduled).
  2. “Mansplaining the Law: The Effect of Gender on Interruptions in Congressional Testimony,” Text-as-Data Conference, University of Washington, 2018 (Scheduled).
  3. “Introduction to Text-as-Data (Two-Day Workshop),” Data Science Connection, Atlanta, George, 2018 (Scheduled).
  4. “Are Governors More Likely to Keep Their Promises Under Unified Government? Agenda Speech and Legislative Outcomes,” APSA State Politics and Policy, Penn State, 2018.
  5. “Applying Artificial Intelligence and Machine Learning to Quantify Central Bank Sentiment,” National Association of Business Economists, Seattle, Washington, 2017.
  6. “Political Surrogates, Minimal Effects, and Campaign Dynamics in the 2012 Presidential Election,” Department of Political Science, Columbia University, New York City, New York, 2017.
  7. “Are Governors More Likely to Keep Their Promises Under Unified Government? Agenda Speech and Legislative Outcomes,” Northeast Political Science Association, Philadelphia, Pennsylvania, 2017.
  8. “Are Governors More Likely to Keep Their Promises Under Unified Government? Agenda Speech and Legislative Outcomes,” APSA State Politics and Policy, St. Louis University, Missouri, 2017.
  9. “Are Governors More Likely to Keep Their Promises Under Unified Government? Agenda Speech and Legislative Outcomes,” Midwest Political Science Association, Chicago, Illinois, 2017.
  10. “Introduction to R: How to Transition from STATA,” Applied Statistics for Social Science, Columbia University, New York City, New York, 2017.
  11. “Introduction to Text-as-Data,” Graduate Student Methods Workshop, Columbia University, New York City, New York, 2017.
  12. “Introduction to R and LaTeX,” Graduate Student Orientation, Columbia University, New York City, New York, 2017.
  13. “Introduction to Text-as-Data,” Institute for Data Science, Columbia University, New York City, New York, 2016.
  14. “Scaling the Tower of Babel: Divergent Policy Implications Among Translated and Native-Language Central Bank Communications,” Computational Approaches to Social Modeling, Seattle, Washington, 2016.
  15. “Microservice Architectures for Scalable Text Processing,” Open Data Science, Boston, Massachusetts, 2016.
  16. “Introduction to Text-as-Data,” Graduate Student Methods Workshop, Columbia University, New York City, New York, 2016.
  17. “Introduction to R and LaTeX,” Graduate Student Orientation, Columbia University, New York City, New York, 2016.
  18. “How I Learned to Let Go and Love Python,” Graduate Student Methods Workshop, Columbia University, New York City, New York, 2016.
  19. “Send in the Clones? The Effect of Surrogates on Candidate Support,” Midwest Political Science Association, Chicago, Illinois, 2014. Recipient of the Antoinette Dames Prize for Outstanding Honors Thesis in Political Science.
  20. “Send in the Clones? The Effect of Surrogates on Candidate Support,” National Council, Washington University in St. Louis, 2014.

Open-Source Software

doc2text: Extract messy text blocks from images with computer vision.

  • Open source software package that applies machine learning and computer vision to isolate, enhance, and extract text from hard-to-read documents. doc2text is different from other packages because it corrects for document layout problems with a novel statistical approach before running OCR, which results in significantly higher levels of accuracy. It gained notoriety as a top article on Hacker News and has been used for instructive purposes at Pennsylvania State University. This package is in the top 0.1% of projects on GitHub, with more than 1,000 stars (akin to Facebook “likes”) and 50 forks. [Link]

grunt-express-server: Grunt task runs an Express server.

  • Used widely by the MEAN-stack community to run Yeoman-generated applications. Manager since 2015. [Link]
  1. “Robots Want to Find Meaning, Too: The Super Bowl Indicator Has a Robot Equivalent,” Bloomberg, 2018. [Link]
  2. “Warren Buffet is Just an Average Employee,” Bloomberg, 2018. [Link]
  3. “Spurious correlations are kryptonite of Wall St’s AI rush,” Financial Times, 2018. [Link]
  4. “Visible CFOs Linked to Lower Stock Volatility,” CFO Magazine, 2018. [Link]
  5. “New analysis proves Trump’s tweets attacking companies are mostly just distractions,” Quartz, 2017. [Link]
  6. “Hawks and doves: a visual history of Fed sentiment,” Financial Times, 2017. [Link]