About

I am a neuroscience graduate student in the Center for Theoretical Neuroscience at Columbia University. My research focus is artificial and biological learning and computation.

Before starting my PhD, I worked at the Flatiron Institute in the NeuroStatsLab, co-supervised by Alex Williams and Cristina Savin, where I worked on metrics for comparing dynamic representations. I completed my master’s thesis in the Savin Lab at NYU’s Center for Neural Science, combining control and learning theory to develop a novel algorithm for training recurrent neural networks. Earlier, I completed lab rotations in the Macke Lab on inferring the dynamical systems underlying data and in the Dayan Lab on three-factor Hebbian learning. During my bachelor’s degree, I worked with Neslihan Serap Şengör on spiking neural networks for perceptual decision-making. I am deeply grateful for her mentorship since my undergraduate years, she not only guided my research but also helped me appreciate the beauty of mathematics and neuroscience.