Research & Publications

My doctoral research focused on statistical and machine learning methods for understanding neural population dynamics from large-scale recordings. I worked on methods for estimating neural population burst timing, cross-area coupling, trial-level variability, denoising, and interpretable timing motifs across brain regions.

This work reflects a broader interest in developing models that are statistically principled, computationally practical, and useful for interpreting complex biological and time-dependent systems.

Selected Publications