As a Postdoctoral Associate at Duke University working in the lab of Michael Zavlanos, I lead efforts in the development of new theories, algorithms, and software for the analysis of networked autonomous systems, including the development of relevant data-driven and machine learning techniques. I interact with and supervise graduate and undergraduate students in our group working on relevant research topics.

My other research interests are geometric and topological deep learning, applied algebra, and applied topology.

I am on the job market. Please contact me about any openings in academia or industry.


In my Ph.D. thesis, I argue that lattice-based multi-agent systems constitute a broad class of networked multi-agent systems in which relational data is passed between nodes. Mathematically modeled as lattice-valued sheaves, I initiate a discrete Hodge theory with a Laplace operator, analogous to the graph Laplacian and the graph connection Laplacian, acting on assignments of data to the nodes of a lattice-valued sheaf. This Laplace operator, we call the Tarski Laplacian in deference to the Tarski Fixed Point Theorem, a classic result about the fixed points of a monotone operator on a complete lattice.

Link: “Lattice Theory in Multi-Agent Systems”