Research and Teaching Interests 

Dr. Beniamino Hadj-Amar's research lies at the intersection of statistics, machine learning, and scientific application, with a focus on Bayesian methods for analyzing complex time series data. He develops flexible models to capture non-stationary, non-linear, and sparse patterns, leveraging tools such as switching models, change-point detection, Gaussian processes, Bayesian nonparametrics, graphical models, and spectral analysis. He applies these methods across diverse scientific domains, including neuroscience, respiratory medicine, and wearable health monitoring. Much of his work focuses on data collected from wearable devices-such as actigraphy, skin temperature, and circadian monitoring-to study behavioral and physiological rhythms in individuals with epilepsy and other clinical populations. He also collaborates on neuroscience studies involving neuromodulator dynamics, analyzing neural signals from fMRI, EEG, and electrochemical recordings. In respiratory research, he contributes to the modeling of airflow traces to better understand disordered breathing during sleep.