Ann Huang

About

Ann is a PhD student in the Neuroscience graduate program. She works at the intersection of computational neuroscience, machine learning, and dynamical system theory to understand neural computations. She completed her undergraduate studies in Neuroscience, Mathematics, and Computer Science at McGill University in Canada, where she worked on deep reinforcement learning, hippocampal representational dynamics, and modelling decision-making processes under limited cognitive resources. In her free time, Ann enjoys staying active through traveling, hiking, skiing, and climbing.

Research Focus

Ann’s research focuses on understanding the computations performed by biological and artificial neural networks through the lens of dynamical systems theory, control theory, and machine learning. Her work revolves around two core questions.

First, what is the structure of neural computation, and how does it vary across brain regions, individuals, tasks, contexts, and over learning? To address this, Ann develops quantitative frameworks and comparison tools for analyzing and relating neural dynamics across different networks and systems.

Second, given time-series data of neural activity, how can we best reconstruct and interpret the underlying dynamical system? To this end, Ann develops data-driven system identification methods that yield interpretable, control-theoretic descriptions of network dynamics from noisy, partially observed, and multi-region neural recordings. Ultimately, Ann wants to integrate data-driven methods with theoretical insights from dynamical systems and control theory to understand learning and multi-area interactions in the brain.