Welcome to my personal site! I’m a fifth-year PhD student in the Decision, Risk, and Operations (DRO) division at Columbia Business School, advised by Profs. Yash Kanoria and Hongyao Ma. My research examines college admissions and prediction markets through an empirical lens, using tools from econometrics and network analysis.

Prior to starting my PhD, I was a data scientist at Wealthfront, where I worked on advanced analytics, causal inference, and A/B experimentation. My undergraduate degree is from the University of Pennsylvania, where I majored in Mathematical Economics.

Here is my CV. My email address is as6383 [at] columbia.edu.

Working Papers

Network-Based Detection of Wash Trading with H. Ma, Y. Kanoria, R. Sethi

Media: Bloomberg, CoinDesk, Decrypt

Wash trading refers to the practice of buying and selling securities without taking a net position, for the purpose of artificially inflating recorded volume. It is prohibited by law in the United States, but evidence suggests that it is widespread on some exchanges, especially those involving cryptocurrencies where trader identities can be shielded. The reliable detection of wash trading is challenging because it can be implemented using a variety of different approaches, some of which resemble authentic and lawful strategies such as automated market making. We propose an iterative network-based procedure for detection based on the idea that wash traders form approximately closed clusters of colluding counterparties, seldom transacting with other market participants. Applying this method to the Polymarket exchange, we estimate that transaction patterns indicative of wash trading began to trend upward in July 2024, peaking at nearly 60 percent of volume in December 2024. This activity persisted through late April 2025 before subsiding substantially, and once again increased to about 20 percent of volume in early October 2025.

Submitted, Management Science.

The Impact of Race-Blind and Test-Optional Admissions on Racial Diversity and Merit with H. Ma, Y. Kanoria

How significant was the role of racial preferences in U.S. college admissions before the Supreme Court's 2023 decision to ban race-based affirmative action? How much might test-optional admission policies impact racial diversity and academic merit? In this work, we estimate a simple model of college admissions decisions from 2012-2021, leveraging a novel dataset of applicant profiles and admissions outcomes across the full spectrum of college selectivity. We find that, broadly, the impact of race and testing policies on diversity and merit of admits decreases by college selectivity. For America's less selective colleges that collectively enroll over three-quarters of students, fully eliminating racial preferences—expressed either directly or via unobserved correlates—has little impact on the proportion of underrepresented minorities (URM) and on the average SAT score of admitted students. In contrast, for the 34 most selective colleges accounting for 3 percent of total enrollment, our estimates suggest that admissions going "race blind"—absent any compensating changes in admissions criteria—could reduce URM admission by one-third while increasing the average SAT score of admits by no more than 10 points. We also estimate that universal test-optional admission does not materially affect the proportion of URMs at elite colleges, and may decrease the average SAT score by up to 10 points. At less selective institutions, the effects are estimated to be negligible.

Major Revision, Management Science.

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