Research

Thermodynamic Binding Networks (TBNs) are equilibrium models of molecular systems that focus on which assemblies are energetically favored, rather than the order in which they form. In a TBN, monomers bind through complementary domains to form polymers, and stable outcomes are determined by the competition between enthalpy, which favors additional bonds, and entropy, which favors larger numbers of separate complexes. This makes TBNs a useful abstraction for reasoning about self-organization and DNA nanotechnology at thermodynamic equilibrium. My work investigates the expressive limits of TBNs, the complexity of predicting stable and reachable configurations, and their relationship to more abstract thermodynamic models such as Thermodynamic Affinity Networks.

My work on robot motion planning focuses on highly constrained settings where large numbers of simple robots move under uniform global control signals rather than independent commands. In these models, every robot receives the same instruction at the same time, so geometry, obstacles, and swarm interactions determine what collective behaviors are possible. These systems are motivated by applications in microrobotics, programmable matter, and large-scale swarm control, and they raise natural questions about reachability, relocation, reconfiguration, and computational hardness. My research studies both the algorithmic possibilities and the complexity-theoretic limitations of such globally controlled robotic systems.