I'm a digital health researcher exploring how fintech might help people living with mental illness, specifically bipolar disorder.
The American Psychiatric Association (APA) considers “engaging in unrestrained buying sprees or foolish business investments” as part of the diagnostic criteria for bipolar disorder.
I'm working to understand how fintech-equipped interfaces might support the financial lives of people living with this condition. For example, what would financial advance care planning look like for people who wanted it and how might software incorporate open banking data in order to support those plans? How do certain adverse financial life events influence whether people are comfortable involving trusted others in financial interventions?
I've collaborated with clinicians, digital health researchers, and people with lived experience to understand what is (and what's not!) feasible, acceptable, and possible if we involve financial data into clinical contexts.
I take a quant UX approach to create usable evidence to inform financial intervention design.
Python, R, lme4 for multilevel modeling, marginaleffects for posthoc analysis, altair for visualization, hmmTMB for hidden Markov models in a multilevel framework. Sawtooth Software (who kindly awarded me a grant) for survey deployment. At home in Linux/Unix environments.
The Currency of Mood: Assessing Acceptance and Privacy Preferences of Third-party Financial Data Sharing in Bipolar Disorder · First author · Under review, 2026
A factorial vignette survey (N=500) to examine level of comfort with hypothetical scenarios involving third-party financial interventions during symptomatic and euthymic periods.
Mapping Financial Behavior in Bipolar Disorder: Development of a Dataset Linking Spending Habits to Mood Fluctuations · First author · Accepted, Conference of the International Society for Bipolar Disorders, 2026
Clinical assessment of financial behavior is often limited to self-report. This study (N=50) used open banking technologies to collect 24 months of transaction data and self-report mood logs from individuals with BD.
Evidence-based Digital Design: Utilizing MaxDiff Findings to Guide the Development of Financial Interventions in Bipolar Disorder · First author · Accepted, Conference of the International Society for Bipolar Disorders, 2026
A MaxDiff survey deployed to 150 individuals with BD in order to rank 6 digital health intervention features intended to help promote financial stability in this population. The MaxDiff design had not been used in the field of mental health before.
Eight Years of Autonomic Monitoring: An N-of-1 Longitudinal Study of Wearable-derived HRV Anomalies and Self-reported Mood Logs
An intensive N-of-1 analysis examining how wearable-derived nocturnal physiological anomalies and multidimensional mood states interact over an uninterrupted eight-year period.