Most natural phenomena are governed by dynamic processes, resulting in observed time-varying data. I am developing methods that can better capture the dynamic processes embedded in observed data. Focusing on brain network data, I am identifying latent embedding models for learning dynamics of networks, including tensor factorization, sparse regression, and autoencoder models. This enhances methods at the edge of machine learning and dynamical systems theory.
