Curriculum Vitae

Project Summary: Occupying close to a third of the average person's lifetime, sleep is paramount for human functioning and well-being. Lack of sleep can not only adversely impact cognition and performance through decreased alertness, but it has also been shown to play a crucial role for long-term health due to its reciprocal interactions with many chronic diseases, including but not restricted to depression, cardiovascular disease and dementia.

While scalp EEG is the most commonly used tool used in sleep research, we leverage the finer temporal and spatial resolution that invasive neurophysiological recording modalities can provide. We use unsupervised machine learning methods to distinguish periods of sleep and wake, as well as to characterize the architecture of sleep from long-term recordings obtained using subdural and stereotactic depth electrodes in refractory epilepsy patients. Some of the goals of my project include studying the structure of sleep in terms of clusters of activation patterns across the brain, the local organization of sleep, as well as potential correlates between demographics and these properties of sleep.