Research and technical advisory for digital mental-health teams studying treatment response, remission, or behavioral sensing in defined study datasets. Work can cover interpretable modeling, mobile data, and study evaluation, drawing on published research in digital phenotyping and treatment-response modeling.
Selected publications, talks, and projects related to this area. Each link opens the work and its original source.
A 2025 preprint applies interpretable machine learning to predict treatment response and remission in body dysmorphic disorder. It examines which baseline factors drive those predictions.
A 2024 analysis of a randomized trial asks who is more likely to benefit from smartphone-delivered cognitive behavioral therapy for body dysmorphic disorder.
A 2023 preprint tests whether phone-sensor data can forecast changes in schizophrenia symptoms and explain them with counterfactual explanations. It is evaluated in a simulated monitoring setting.
A study of people with schizophrenia combines smartphone sensing, brief self-reports (EMA), and individual-level network analysis. It examines how behavior and reported experience relate over time.
Interpretable machine learning shows that higher baseline anxiety predicts lower odds of improvement in major depressive disorder. The trial compared three smartphone-delivered interventions.
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