Our lab performs research in the general areas of applied dynamical systems, robotics, and pattern recognition. The central theme of our research is to model and control complex systems comprising robotic, animal, and/or virtual agents. Applications of our work range from crowd management, environmental monitoring, quantifying animal behavior, and bioinspiration in robotic design and autonomy.
Causal relationships underlying the collective dynamic behavior of swarms, National Science Foundation, Subaward from New York University: Living in groups affords several benefits for animals such as better feeding opportunities and reduced predation risks. In both instances-foraging and predator avoidance-critical information is transmitted nonverbally throughout the group, at different time scales. This project, carried out in collaboration with Dynamical Systems Laboratory, New York University, seeks to demonstrate that an information-theoretic approach can be used to measure social animal behavior. The research objective is to establish a rigorous model-free framework to study causal relationships in animal interactions validated by a series of hypothesis-driven experiments on zebra fish to emphasize unidirectional information transfer.