Biological systems, from embryos to social insects, get tremendous mileage by using vast numbers of cheap, unreliable components to achieve complex goals reliably. As we build embedded systems with similar characteristics --- programmable materials, self-assembling robots and robot swarms, sensor networks --- can we achieve the kind of complexity and reliability that nature achieves?

Our group is interested in self-organizing multi-agent systems, where large numbers of simple agents interact locally to produce complex and robust global behavior. We study programming paradigms for engineering such systems in robotics and sensor networks, drawing inspiration mainly from multicellular biology and social insects. We also investigate models of self-organization in biology, specifically how cells cooperate during the development of multicellular organisms. A common theme in all of our work is understanding the relationship between local and global behavior: how does robust collective behavior arise from many locally interacting agents, and how can we program the local interations of simple agents to achieve the global behaviors we want.

We work on three main areas:

• Bio-inspired Multi-agent Models and Theory:
  
  We explore artificial multi-agent models inspired by self-organising and self-repairing behavior in developmental biology. We also study global-to-local compilation and theory, i.e. how user-specified global goals can be translated into local agent interactions. The goal is to show how biological design principles can be formally captured, generalized to new tasks, and theoretically analyzed.
• Bio-inspired Distributed Systems in Robotics and Sensor Networks
  
  We study bio-inspired approaches for programming embedded systems that rely on large numbers of relatively cheap and simple agents, e.g. modular robots, robot swarms, and sensor networks. We design, analyze, and implement decentralized algorithms that have self-repairing, self-maintaining properties and can achieve wide classes of user-specified global goals. We also build prototype hardware systems, especially in robotics, to demonstrate the ideas.
• Multi-cellular Systems Biology
  
  We develop mathematical and computational models of cell behavior to investigate how system-level properties emerge in multicellular development. This work is in close collaboration with experimental biologists, and most of our current work is focused on epithelia and fruit fly development. Our goal is to elucidate the relationship between local cell decisions and tissue-level outcomes during development and disease.