My research focuses on developing methodological solutions to problems concerning neuroscience data analysis. My research can be divided into two categories: first, a methodological element, focused on developing a statistical framework for linking neural activity to biological and behavioral signals as well as developing statistical estimation and inference algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to different modalities of neural data; 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, to characterize how neural ensembles maintain representation of associated biological and behavioral signal, and to reproduce these signals in real time. In my research, I have worked to integrate methodologies related to model identification, statistical inference, signal processing, Bayesian analysis, and stochastic estimation and control, and to expand these methodologies incorporating neural analysis models, making them more appropriate for modeling the dynamics of neural systems observed through neural data such as local filed potential and spike train data.