Graduate Student Researcher
- Developed and compared novel supervised machine learning models to assess aspects of cognitive function using features derived from passive smartphone typing behaviors.
- Engineered features from smartphone typing and accelerometer metadata relating to diurnal patterns and sleep for use in predictive models of cognition.
- Designed unsupervised machine learning method to process accelerometer metadata collected during smartphone typing to compare to patterns in mood and cognition.
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- Explored influence of time of day on response inhibition as measured by Go/No-Go task in those with mood disorders using mixed effects models.
- Identified individual characteristics in diurnal smartphone orientation through accelerometer metadata for detection of mood changes.