Kendra Wyant, B.A.
Office: 325 Psychology
M.S., Clinical Psychology, University of Wisconsin-Madison, in progress
B.A., Psychology, California State University, Fullerton, 2020
Broadly, I am interested in machine learning and predictive methods in the context of addictive behaviors. My research focuses on developing models to predict when a drug or alcohol lapse may occur using an individual’s smartphone data. Specifically, my goal is to identify low-burden measures with enough signal to accurately predict when someone might lapse and use these models to advance treatment and intervention for drug and alcohol addiction. For example, through digital phenotyping, we can collect various passive data sources (via a smartphone application) at a low cost and burden to the individual. Passive sources, such as GPS location and SMS logs, are dynamic measures that, when in concert with machine learning methods, can effect increased precision in lapse risk prediction on a moment-to-moment basis. Clinicians can then use these data to implement just in time interventions when individuals are most likely to relapse, thus creating a more efficient use of treatment resources and reaching the most vulnerable populations.