About
I am a researcher in the Brain-Inspired AI Lab at Vanderbilt University, where I study adaptive learning across human cognitive development and artificial neural networks. My work examines how learning dynamics unfold over time in both brains and models, with a particular focus on curriculum learning, behavioral alignment, and critical periods in representation learning. I am especially interested in how biological and artificial systems respond to different forms of disruption across development or training, and how temporally-sensitive phases of learning influence what is acquired, what is lost, and what can be recovered. I bring prior experience in neuroimaging and clinical psychology research, including translational work in schizophrenia, which informs my interest in linking neural, behavioral, and computational levels of analysis to better understand brain function and dysfunction.
Research Interests
Current Research Projects
Probing Critical Periods in Model Learning
Investigating learning dynamics of models using disruptive perturbations at various training stages to identify critical periods that significantly influence model performance and representation formation. By applying targeted disruptions during training, we aim to understand how these interventions affect learning trajectories, generalization capabilities, and the development of internal representations. This research seeks to uncover parallels between artificial learning processes and biological critical periods, providing insights into optimizing training protocols for enhanced model robustness and adaptability.