Ph.D., Clinical Psychology, University of Wisconsin-Madison, in progress
M.S., Clinical Psychology, University of Wisconsin-Madison, in progress
M.A., Computational Social Science, University of Chicago, 2023
B.S., Clinical/Community Psychology, University of Illinois at Urbana-Champaign, 2021
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Research Interests
My research interests center around leveraging big data and cutting edge computational methodology to predict temporally precise mental health risks and optimize personalized, just-in-time treatment recommendations. I am particularly interested in exploiting lower-burden personal sensing technology to collect data, such as text messages acquisition. Currently, I am working on a project to predict alcohol lapse risks via text analysis on SMS messages. My work aspires to pursue the goals of 1) building fair, precise estimations of time-varying alcohol lapse risks; 2) uncovering current gaps in knowledge of alcohol relapse antecedents; and 3) learning from high-importance features that might have crucial implications of treatment recommendations.
My ultimate aim is to inform real-world implementation of such algorithms in digital therapeutics and promote them as a valuable toolkit alongside traditional in-person therapies. I am also a big advocate for health equity. I am committed to devoting my work to help address extant barriers in accessing healthcare resources commonly faced by disadvantaged demographic subgroups. In particular, my work seeks to assess and refine models so that they perform well across various subgroups.
Current Projects
Predicting Alcohol Lapse Risks via Text Analysis on SMS Messages
Assessing Demographic Variability in Machine Learning Model Performance to Predict Alcohol Lapses