Research

Neuroscience: My research focuses on modeling and analysis of emergent phenomena in complex networked systems. I am particularly excited by mathematical and engineering challenges lying on the intersection of nonlinear control theory and neuroscience. The central question of my current research is probabilistic physical modeling of neural behaviors as large-scale interconnected neural circuits. Apart from enunciating a deep mathematical structure of the underlying neurophysiological behavior, such an approach aims at drawing conclusions about macroscopic behavior of populations of neurons. My goal is to develop theory and practical methods that marry control networked systems and quantitative neuroscience, aiming toward a more robust and scalable understanding of neural behaviors. Some of the challenges that I find particularly interesting can be found here.

Nonlinear control: with the main research objective to develop new techniques for modeling, analysis and control of physical systems.

Systems biology: my main interests pertain to the interplay of dynamical systems theory and chemical reaction networks.

Distributed-parameter systems: developing numerical integrators with good global behaviors for structure-preserving discretization of distributed-parameter systems.

Topological Data Analysis: using persistent homology for robust analysis of scientific data.

Complex networks: employing discrete Hodge theory to study complex network landscapes.

Symmetry reduction: investigating the reduction of Poisson and Dirac structures and studying the role of symmetry in mathematical physics and control theory.

A more formal and longer description of my research interests can be read in the extended research statement.

A short semi-technical article that explains some of the above mentioned interests to a wider science-interested audience can be found here.