Currently, I am a second year PhD student at the University of California, San Diego studying physics under the supervision of Gabriel Silva in the Center for Engineered Natural Intelligence. My research interests are in the application of statistical mechanics to neural systems and machine learning non-euclidian data. As an undergraduate student, I worked as an experimental condensed matter physcists advised by Ivan K. Schuller at the Center for Advanced Nanoscience.
As an undergraduate, I became fascinated by how seemingly simple systems interact and exhibit emergent behavior. Naturally, my interest in these systems drew me to the field of condensed matter physics where atoms conspire to produce extraordinary complex phenomena. Under the supervision of Ivan Schuller, I worked on the nonequilibrium phase transition of vanadium oxides in which a strong electrical perturbation can result in dramatic macroscopic responses.
Entering graduate school, I decided to work on complex networks, systems with large degrees of heterogeneity. Since then, my interests have diverged into two research directions: the application of statistical mechanics and field theory to neural systems and machine learning on data structured as a graph. While these two research directions appear disconnected, my hope is that the need for a deeper understanding of machine learning algorithms and for robust large scale analysis of neural systems will necessitate the application and inclusion of both research directions.
Current (broad) interests: universality, renormalization group, graph neural networks, variational autoencoders