I am a Ph.D. Candidate in Finance at the University of North Carolina Kenan-Flagler Business School, Chapel Hill. I am in my fifth year of the Ph.D. curriculum. My work focuses on private capital and applications of machine learning in finance.
Previously, I worked as a Research Associate at the Indian School of Business, Hyderabad, and as an ML R&D analyst at American Express. I graduated from the Indian Institute of Technology Kharagpur in 2017.
My research uses machine learning to study decision-making in private capital and financial markets — from measuring dynamic risk exposures in venture capital, to optimizing exit strategies in anomaly-based trading, to understanding how team composition shapes outcomes inside regulatory institutions.
Job Market Paper
Embedding Venture Capital: A Machine Learning Approach to VC Risk Exposures
Anomaly premiums are traditionally based on long-short portfolios held passively for a fixed horizon. We estimate premiums when exits are instead optimized using machine learning with a computationally light architecture. We use randomized kernels to generate high-dimensional representations of time-series inputs. A classifier ingests the inputs and predicts optimal exits. In a conservative universe with only large-cap value-weighted portfolios, dynamic exits generate premiums of about 100 basis points per month for momentum, profitability, and value. These premiums are not replicated with static or random exits, without random re-representations, in non-anomaly portfolios, and are robust to reasonable transaction cost hurdles.
with Joseph Kalmenovitz, Abhinav Gupta & Kairong Xiao · SSRN ↗
Presented at MFA 2026 & SWFA 2026
Can gender diversity influence regulatory outcomes? We explore this question in the context of U.S. federal rulemaking, leveraging a novel dataset that tracks 15,000 rulemakers responsible for drafting and advancing 38,000 regulatory projects. We find that teams including women deliberate longer and extensively revise their initial drafts, yet are ultimately less likely to see their projects approved by the agency leadership and issued as final rules. To explain these patterns, we develop a simple model of rulemaking in which output gaps arise from four sources: selection (women are assigned lower-quality projects), cost (differences in the cost of exerting effort), incentive (differences in the return to effort), and evaluation (differential treatment by agency leadership). Reduced-form estimates, aided by a machine-learning C-BERT model, reveal that women are assigned to lower-quality projects and face harsher evaluations, but receive stronger incentives tied to project completion. Structural estimates confirm that disadvantages in selection, cost, and evaluation each contribute meaningfully to the observed gender gap, partially offset by females receiving stronger incentives. Our findings shed light on how female representation shapes regulatory performance and contribute to the broader debate over whether diversity enhances or hinders the effectiveness of public institutions.
