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Hello! Welcome to my page.

I am currently a PhD student in the Industrial Engineering and Operations Research (IEOR) department at the University of California, Berkeley. I study scientific and technological innovation with a focus on empirically understanding how these innovations develop and can be improved from a managerial and economic perspective, using mathematical modeling and frontier data techniques such as text analysis.

My methodological research interests are primarily in text analysis, particularly applications including topic modeling, LLMs, and computational and modeling aspects. I'm also very interested in Bayesian statistics and encouraging adoption of Bayesian methods for scientific analysis, including Variational Inference (VI) and its intersection with optimization. I believe the flexibility and potential for faster inference via VI will unlock adoption of Bayesian methods more generally.

I previously worked as a Research Associate at Harvard Business School and at Yale School of Management, advised by economists, and so I am also generally interested in economics as a field and employ economic thinking as a primary lens for interpreting the world. Through this exposure I am familiar with causal inference methods and designs like regression discontinuity, differences-in-differences, and others, and I incorporate these methods into my research on innovation. I'm also generally interested in bringing text methods to other fields of economics where there is rich text data to help unlock more insights; areas that have a lot of potential in my view are Law & Economics and Financial Economics.

I hold a double undergraduate degree from Caltech (BS Applied and Computational Mathematics, BS Business, Economics, and Management); an MBA from Yale School of Management; and an MS in Data Science from Harvard's School of Engineering and Applied Sciences (SEAS).

Outside of research I care a lot about social issues and conversations about those issues, particularly opportunity for social mobility and leveling the playing field so that the privileges people inherit from parents/family play less of a role in keeping out those without similar privileges. This is informed partially by an economic perspective on allocation of talent, but also from personal experiences — where, going into college, I realized I was behind my peers not due to lack of innate ability or willingness to work hard, but because I had weaker K–12 mathematical preparation than they had access to through their parents guiding them to appropriate opportunities.

Based on my experience coming from a weaker math background and eventually surviving through it to reach competence, I've had a lot of exposure across mathematical topics and developed a potentially unique view on how to make math topics more accessible while still preserving the rigor. I'm working on Math Boot Camp notes for Operations Research and related PhD programs, in the spirit of the longstanding PhD Economics Math Boot Camp.