Dr. Yousefi’s research focuses on developing methodological solutions to problems in neuroscience data analysis. His work can be divided into two categories: first, a methodological element, which focuses on developing a statistical framework for linking neural activity to biological and behavioral signals, as well as creating statistical estimation and inference algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to different modalities of neural data; and second, an application element, where these methods are applied to neural data recorded from neural systems to dynamically model the neural activity of individual neurons, characterize how neural ensembles maintain representations of associated biological and behavioral signals, and reproduce these signals in real time. In his research, he has worked to integrate methodologies related to model identification, statistical inference, signal processing, Bayesian analysis, and stochastic estimation and control, expanding these methodologies by incorporating neural analysis models to make them more suitable for modeling the dynamics of neural systems observed through neural data, such as local field potential and spike train data.