Ben is a doctoral student in the Cognitive Sciences department at UC Irvine. Coming from an engineering background, Ben's work blends Bayesian modeling and machine learning techniques with memory and sleep data.
His initial foray in the sleep field involved applying machine learning and signal processing techniques on EOG data to detect the presence of Rapid Eye Movements.
More recently, his work on sleep dynamics and big data has quantified the influence of individual differences on the sequence of sleep states. Other projects of note include developing an online sleep data collection tool (www.modasleepscoring.com) and investigating how memories encoded close in time might become integrated, such that layer manipulations of one effect the other.
Current interests include examining trait-level individual differences of sleep electrophysiological markers as predictors of cognitive outcomes. For example, are there sleep features that more stable within an individual than across people, and can they be used to predict workplace performance?