A Distributed, Stochastic Framework for Active Matter

We develop a theoretical framework for task-oriented active matter that combines distributed computing, stochastic processes, statistical physics, active matter physics, and robophysics. By harnessing phase changes from statistical physics in our formal modeling and algorithm design, we obtain robust and provable self-organizing behaviors. We then incorporate this theory into swarm robotics platforms, establishing tight analogies between (but not necessarily strict implementations of) the algorithms' rules and our robots' designs. This allows us to critically examine our theoretical algorithms' robustness to the errors and uncertainties of physical environments. Further, we can treat robot swarms as macro-scale active matter systems, studying the inter-robot dynamics as an analogy to particle interactions.

Andréa W. Richa
Andréa W. Richa
Professor of Computer Science
Dana Randall
Dana Randall
Professor of Computer Science
Daniel I. Goldman
Daniel I. Goldman
Professor of Physics
Sarah Cannon
Sarah Cannon
Assistant Professor of Mathematics
Joshua J. Daymude
Joshua J. Daymude
Assistant Professor, Computer Science

I am a Christian and assistant professor in computer science studying collective emergent behavior and programmable matter through the lens of distributed computing, stochastic processes, and bio-inspired algorithms. I also love gaming and playing music.

Ram Avinery
Ram Avinery
Postdoctoral Researcher, Physics
Enes Aydin
Enes Aydin
Research Engineer, Physics
Bahnisikha Dutta
Bahnisikha Dutta
PhD, Electrical Engineering
Shengkai Li
Shengkai Li
PhD Student, Physics
William Savoie
William Savoie
PhD, Physics
Ross Warkentin
Ross Warkentin
Advanced Software Engineer
Cem Gökmen
Cem Gökmen
Master’s Student, Computer Science

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