Research

My Research:

Startup Acquisition and the Redirection of Disruptive Innovation (May 2026) [Paper] [Slides]

Accepted to Present at the 2026 Georgetown Carroll Round Conference (Washington, DC)

Abstract:  This paper examines how the shift in startup exits from IPOs toward acquisitions alters the trajectory of disruptive innovation. I extend a heterogeneous-innovation quality-ladder framework by allowing incumbents to buy external disruptive ideas and subsequently choose how intensively to integrate the acquired knowledge into the in- cumbent’s existing cluster. To test this mechanism, I link U.S. M&A transactions to PatentsView data (1990–2022), construct patent-level disruptiveness using the second- order citations, and combine these measures with acquirer product-market centrality and markup-based productivity from a GHL demand model. I show that acquisitions sharply increase internal use of target knowledge, and that this increase is dispropor- tionately concentrated in disruptive target patents acquired by competitively exposed incumbents. These findings imply that M&A can delay creative destruction without terminating innovation by disproportionately channeling disruptive invention toward within-firm exploitation, an outcome relevant to merger policy focused on dynamic competition and innovation.

Threat Credibility & Plea Acceptance: An Empirical and Theoretical Analysis (May 2024) [Paper] [Slides] [Poster]

Presented at 2024 Undergraduate Research Symposium (Madison, WI)

Abstract: This paper examines the factors that contribute to plea acceptance, and the potential strategies used by prosecutors to extract plea bargains without credible threats of trial. We find that the prioritization and rate of conviction of an offense are factors associated with a decrease in the likelihood of plea acceptance. We aim to explain this relationship by modeling plea bargaining behavior with simultaneous offers and opaque prioritization. Our model suggests that prosecutors under a resource constraint can gain from making discriminatory offers, specifically by inducing defendants with credible threats to reject their plea offers, and offering more lenient sentences to defendants who would not be taken to trial otherwise. We propose that Artificial Credibility may be a strategy that resource constrained prosecutors can use to further extract sentencing, where prosecutors build reputation by temporarily restricting the set of defendants they engage with, then relaxing the restriction while maintaining that reputation. We show Artificial Credibility is preferred to a variety of bargaining strategies, with and without resource constraints.

Searching Search: A Review of Research on Search Models (August 2024) [Review]

Directed Study in Search Theory with Professor Dan Quint

Research Assistance & Articles:

Research Intern with the Federal Reserve Bank of New York (June 2025 - August 2025)

PI: Dr. Ozge Akinci, Federal Reserve Bank of New York

Research Assistant with the CALL-ECL Project, Wisconsin Center for Education Research (September 2023 - May 2025) [Website]

PI: Dr. Rich Halverson, University of Wisconsin-Madison

Research Assistant with the CALL-MEI Project, Wisconsin Center for Education Research (January 2024 - May 2025) [Website]

PI: Dr. Chris Saldaña, University of Wisconsin-Madison

Lemons & Degrees: Asymmetric Information in Higher Education (May 2023) [Article]

Published in Equilibrium Vol. 13 (12-15)

Interview with Dr. Katy Milkman (January 2024) [Article]

Published in Equilibrium Vol. 14