research

Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
We extend the stochastic approach to heterogeneous self-organizing particle systems made up of particles of different color classes. We show that by biasing random particle movements based on the number of same-color neighbors, these systems can collectively separate or integrate.
Cite Project DOI arXiv
Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
Improved Leader Election for Self-Organizing Programmable Matter
We present a randomized algorithm for leader election in the amoebot model. It is the first of its kind with rigorous correctness and runtime guarantees, and can tolerate holes in the system.
Cite Project DOI arXiv
Improved Leader Election for Self-Organizing Programmable Matter
On Your Marks, Get Set, Coat!
In this post, I break down some key ideas from my new journal paper “On the Runtime of Universal Coating for Programmable Matter”. TL;DR: the paper is all about programming materials that don’t exist yet to coat other objects in even layers as (relatively) fast as possible.
December 11, 2017 8 min read research
Project Paper
On Your Marks, Get Set, Coat!
On the Runtime of Universal Coating for Programmable Matter
In this sequel paper on the universal coating problem, we analyze the runtime of an algorithm for self-organizing particle systems that coats 2D objects as evenly as possible. We show this algorithm terminates in a linear number of rounds with high probability, and is competitively optimal.
Cite Project DOI arXiv Blog
A Stochastic Approach to Shortcut Bridging in Programmable Matter
In this talk, I present a deep dive of our stochastic algorithm for shortcut bridging based on the bridging behavior of Eciton army ants.
October 6, 2017 1:00 PM ACO Student Seminar
Project Slides Paper
A Stochastic Approach to Shortcut Bridging in Programmable Matter
A Stochastic Approach to Shortcut Bridging in Programmable Matter
We extend the stochastic approach to self-organizing particle systems used in compression to shortcut bridging, in which particles self-assemble bridges over gaps that balance a tradeoff between bridge length and cost. This work is inspired by the bridging behavior of Eciton army ants, and demonstrates how local interactions can guide a system to globally optimal configurations.
Cite Project DOI arXiv Journal Version
A Stochastic Approach to Shortcut Bridging in Programmable Matter
Local Stochastic Algorithms for Compression and Shortcut Bridging in Programmable Matter
In this talk, I present algorithms for compression and shortcut bridging designed using the stochastic approach to self-organizing particle systems.
July 28, 2017 2:05 PM BDA 2017
Project Slides
Local Stochastic Algorithms for Compression and Shortcut Bridging in Programmable Matter
Convex Hull Formation for Programmable Matter
In this talk, I present preliminary work on an algorithm for self-organizing convex hull formation, including an general outline of runtime analysis.
July 28, 2017 1:40 PM BDA 2017
Project Slides
Convex Hull Formation for Programmable Matter
A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems
We introduce the stochastic approach to self-organizing particle systems in which we use a Markov chain to describe how the system evolves over time as particles move randomly. We show that by biasing particle moves towards positions where they have more neighbors, the system converges to compressed states; we also show that when this bias is not strong enough, the system converges to expanded states.
Cite Project DOI arXiv
A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems
Compression in Self-Organizing Particle Systems
This undergraduate honors thesis treats the problem of compression in self-organizing particle systems, where the goal is to gather particles as tightly together as possible. Three algorithms are proposed and compared with rigorous proofs and simulations.
PDF Cite Project Project ASU Digital Repository Defense
Compression in Self-Organizing Particle Systems