Sarah Sant'Ana

Credentials: M.S.

Position title: Graduate Research Assistant

Pronouns: she/her/hers


Office: 325 Psychology

PhD, Clinical Psychology, University of Wisconsin-Madison, in progress

M.S., Clinical Psychology, University of Wisconsin-Madison,  2019

B.S., Psychology, University of North Dakota, 2015

Curriculum Vitae

Follow me on Research Gate, Google Scholar, and Twitter @SarahJSantAna

Research Interests

My research focuses broadly on classification and prediction of addictive behaviors. Ultimately, I hope to 1) provide earlier feedback to individuals that they may have a substance use disorder 2) predict with temporal precision when individuals will engage in hazardous addictive behaviors (e.g. binge drinking episodes, re-initiation of substance use) and 3) develop and evaluate mobile interventions to deploy during these high-risk windows of time. I believe the best path to achieving these goals lies in application of machine learning techniques to large sets of novel, passively collected, and temporally dynamic data such as social media, cell phone use, location data, and more. Due to the rising demands within substance use treatment, using machine-assisted methods for classification and prediction of addictive behaviors may streamline the treatment process by encouraging individuals to seek treatment before substance use related problems become too severe, providing individuals with in-the-moment support that is not feasible within our current treatment system, creating more accessible means of support, and directing individuals to appropriate resources at times when treatment need is greatest or will have the most impact.

Additionally, I am committed to the support and dissemination of open science practices. I encourage all researchers to educate themselves on the importance of generating rigorously designed, transparent, and accessible research. There are many great resources available for information on how to start adopting open science techniques:

The Center for Open Science,   The Society for the Improvement of Psychological Science,   PsyArXiv,   Psychological Science Accelerator


Current Projects

Dynamic, real-time prediction of alcohol use lapse using mHealth technologies.

Social media and prediction of health behaviors.



Wyant, K*., Sant’Ana, S*, Fronk, G., & Curtin, J. (2023, In prep). Personal sensing for temporally precise prediction of lapse in alcohol use disorder.

*Co-first authors

Bradford, D.E., Shireman, J. M., Sant’Ana, S.J., Schneck, S.E., Curtin, J.J. (2022). Drinking with the devil you don’t know and the one you can’t control: Alcohol’s effects during uncertain and uncontrollable stressors in the laboratory. Clinical Psychological Science, 10(5), 885-900.

Moshontz, H., Colmanarez, A.J., Fronk, G.E., Sant’Ana, S.J., Wyant, K., Wanta, S., & Curtin, J. (2021). Prospective prediction of lapses in opioid use disorder: Protocol for a personal sensing study. Journal of Medical Internet Research: Research Protocols, 10(12):e29563.

Fronk GE, Sant’Ana SJ, Kaye JT, & Curtin JJ (2020). Stress allostasis in substance use disorder: Promise, progress, and emerging priorities in clinical research. Annual Review of Clinical Psychology, 16, 401-430. PMC7259491.

Bradford, D.E., Fronk, G.E., Sant’Ana, S.J., Magruder, K.P., Kaye, J.T., Curtin, J.J. (2018). The need for precise answers for the goals of precision medicine in alcohol dependence to succeed. Neuropsychopharmacology.

Curtin, J., Kaye,. J, Bradford, D., Sant’Ana, S., Hajcak, G., & Patrick, C. (2018). The use of differences vs residuals in individuals differences research: Key clarifications regarding reliability and sources of variance. Psychophysiology, 55, S7.

Looby, A., & Sant’Ana, S. (2018). Nonmedical prescription stimulant users experience subjective but not objective impairments in attention and impulsivity. The American Journal on Addictions.