My research focuses on how high-stakes decisions are made under conditions of uncertainty, incomplete information, and irreversible consequences—and how AI-mediated systems reshape judgment, confidence, and outcomes in these environments.

I am particularly interested in decision contexts where data is abundant yet difficult to interpret, incentives are misaligned, and traditional expertise alone does not resolve ambiguity. Across these settings, I study how individuals and institutions integrate algorithmic signals with human judgment, and how system design influences bias, trust, and action.

I study how women navigate fertility and reproductive health decisions under medical uncertainty, time pressure, and social constraint. This includes how clinical data, hormonal testing, and AI-enabled tools are interpreted, and how decision support systems can improve clarity, agency, and wellbeing without unnecessary medicalization.

This work is informed by my applied research through Genesis, an early-stage women’s health initiative that functions as a service and research lab for studying real-world decision processes in reproductive health.

In parallel, I examine how investors and organizations make capital allocation decisions under extreme uncertainty, and how AI-driven evaluation tools affect judgment, bias, and resource distribution. This stream focuses on venture capital and innovation systems as institutional decision environments where small differences in interpretation can have outsized consequences.

How AI-enabled diagnostics and recommendation systems translate complex data into actionable insight—and how their design shapes trust, behavior, and outcomes.

How institutional structures, incentives, and system design influence decision quality, access, and equity in both healthcare and financial markets.