I want to know how collections of atoms, mere star dust, can be intelligent. To achieve this, we must first characterize what type of system the brain (an intelligent collection of atoms) is—what are its components and how do they interact and compute information? Towards this end, I study the brain as a complex biological network by analyzing functional and structural human brain images with computational techniques from graph theory, network science, and bayesian network analysis. In particular, I study the modular structure of the brain and how brain damage impacts network properties at both the local and global scale.

I am a National Science Foundation Graduate Student Fellow. I studied Philosophy and Psychology at Columbia University, where I graduated with honors in both majors and as the valedictorian. I am currently a second year PhD student.

I post all my code on Github, including a Graph-Growth module that takes a complete graph, and "grows" it from one node while measuring graph properties as the graph becomes larger. This is particularly useful for looking at problems of scalability, modularity, and holism in bayesian networks.