Bio
Physiological signals as heart rate variability and EEG, continuous measures of physical activity collected by wearable devices such as Fitbits, and real time self-reported emotional states prompted and recorded by cell phone apps are just some examples of the massive amount of dynamic, time-varying data currently collected by researchers and clinicians. Sophisticated statistical and machine learning methods are needed to unlock the nuanced information contained in these data while avoid spurious claims.
The mission of our research group is to
(1) develop statistical methods and algorithms for the analysis of time series, longitudinal, signal, and functional data, and
(2) to apply these methods through interdisciplinary collaborations to gain a better understanding of biological underpinnings of mental, behavioral and social health, which can be used both for personal monitoring and treatment and for developing population-level interventions.
Areas of Interest
- Behavior and Health
- Imaging
- Mental Health
- Biostatistics
- Health Disparities
- Machine Learning
Education
- BS, State University of New York at Stony Brook
- MA, University of Pennsylvania
- PhD, University of Pennsylvania
Courses Taught
- BIOS 508 - Biostatistical Methods
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