Research and Teaching Statement

I am particularly interested in emergent patterns in complex multi-agent systems. I study both how to design effective autonomous decision-making systems as well as the rationale behind decisions of living agents in natural systems. Consequently, my transdisciplinary work connects engineering, computer science, and behavioral ecology. In general, I use theories from optimization, parallel computing, networks, dynamical systems, stochasticity, and formal logic to explain or design emergent system-level phenomena. My past work focused on the interactions of individuals with complex environments [3, 6–9]. In those cases, the study of animal foraging behavior was instructive when designing control strategies for autonomous air and ground vehicles. My more recent work considers systems of many interacting individuals. For engineering, I am interested in decentralized implementations of collective group behavior that rely on little communication or coordination between individuals. For behavioral ecology, I am interested in the individual proximate mechanisms that, when combined with the environment, lead to group synergy. Task-processing networks: When a number of agents with similar capabilities are connected on a network, each agent may share its own task-processing resources with its neighbors. To generate an adaptive and decentralized cooperation strategy, I implemented a fictitious trading economy that induces individual agents to dynamically ad-just cooperation frequency based entirely on locally received incentives [4, 10]. The individual-level utility functions couple the actions of neighboring individuals so that good performance is possible without explicit communication