Joshua J. Daymude

Joshua J. Daymude

PhD, Computer Science

Arizona State University


I research algorithmic theory for the efficient control, coordination, and characterization of collective emergent behavior. I leverage expertise in several areas of computer science — including distributed computing, stochastic processes, swarm intelligence, and bio-inspired algorithms — to participate in interdisciplinary research on programmable matter systems, active granular matter physics, and (most recently) political science. I completed my PhD in the Self-Organizing Particle Systems lab at Arizona State University in March of 2021 and am now a postdoc in the Biodesign Center for Biocomputation, Security, and Society.

My faith as a Christian shapes how I see the world and my place in it. I view my position as a researcher as a venue to ask questions that matter, to seek solutions that serve others’ needs instead of exploiting their wants, and to foster environments of discourse that value reasoning and mutual respect above the weaponization of knowledge. I look forward to a career of commitment to students of diverse backgrounds and to a holistic integration of science, faith, and human flourishing.


  • Distributed Computing
  • Complex Systems Science
  • Programmable Matter
  • Emergent Behavior


  • PhD in Computer Science, 2021

    Arizona State University

  • B.S. in Mathematics, 2016

    Arizona State University

  • B.S. in Computer Science, 2016

    Arizona State University


A Distributed, Stochastic Framework for Active Matter

Harnessing phase changes from statistical physics in algorithm design. Incorporating algorithmic theory in robot design and manufacturing. Making analogies between macro-scale robot swarm dynamics and active matter systems.

Self-Organizing Particle Systems (SOPS)

Abstracting programmable matter using the amoebot model. Developing distributed algorithms for tasks of self-organization. Rigorously analyzing these behaviors for provable guarantees on correctness, runtime, and reliability.

Recent Publications

Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

In this paper, we study the collective aggregation and dispersion of BOBbots. We demonstrate that a rigorously analyzed algorithm can be translated to these simple robots using physical embodiment, yielding the desired behaviors without digital computation, communication, or sensing.

Preventing Extreme Polarization of Political Attitudes

We present the Attraction-Repulsion Model, an agent-based model of opinion dynamics. It has two rules: actors are more likely to interact with others with similar opinions, and interaction between similar (resp., dissimilar) actors reduces (resp., increases) their difference. We investigate the effects of core parameters such as tolerance and possible interventions for preventing extreme polarization.

Bio-Inspired Energy Distribution for Programmable Matter

In this paper, we extend the amoebot model to consider energy constraints and introduce an algorithm that distributes energy to all particles that need it to participate in the system’s collective behavior. This generalizes existing algorithms in the amoebot catalogue to also respect energy constraints.

Recent Talks

Bio-Inspired Energy Distribution for Programmable Matter

In this talk, we present our algorithm for energy distribution in programmable matter. We also detail our self-stabilizing extension of the amoebot model’s well-established spanning forest primitive. We conclude by showing how these algorithms generalize previous work to also consider energy constraints.

A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

In this talk, I present our stochastic algorithm for separation and integration in heterogeneous particle systems. I detail how our analysis techniques from compression break down in the heterogeneous setting and how we use new tools like the cluster expansion to address these obstacles.

Stochastic Algorithms for Programmable Matter

In this talk, I give an overview of the stochastic approach to self-organizing particle systems, including Markov chain design and analysis for the compression, shortcut bridging, and separation problems.

Recent Courses

CSE 598: Markov Chain and Monte Carlo Methods
Fall 2019, Arizona State University. Instructor of Record.

Recent Press

Greater Than the Sum of Its Parts
Gary Werner. ASU FullCircle, April 2021.
SSS 2020 Report 2: Compression of programmable matter
Laurent Feuilloley. Discrete Notes, November 2020.
2020 ASU ARCS Scholars Showcase
Angie Mitchell. ASU Graduate Insider, October 2020.

From the Blog

Pool Testing is k-ary Search for COVID-19

“Pool testing” has been getting media attention lately as a technique for stretching our limited supply of COVID-19 test kits while delivering accurate results. In this post, I connect pool testing to the classical k-ary search algorithm and analyze its advantages over traditional testing.

Announcing Reviews

I’m happy to announce that art and entertainment reviews have been added to the site. This post is a brief look at why I review things and my experience with hacking the Hugo Academic theme to support reviews.

Solving the Molecube by Reduction

In this post, I give a reader-friendly primer on “reduction”, a computer scientist’s tool for relating problems to one another. I then solve the Molecube puzzle by reducing it to the Rubik’s cube.

Recent Reviews

Jedi: Fallen Order

Jedi: Fallen Order delivers a tight, linear, story-driven experience that puts the player in the shoes of a young Jedi during The Purge. The slick combat and Force-enhanced platforming are thrilling, but the real heroics are found in how the protagonists believe in one another and heal in the face of trauma. 8 / 10.


This is a game about caring and compassion backed by vibrant, hand-drawn art that feels like a living storybook. Many of the narrative’s reflections on life and dying are uniquely engaging for a videogame, though they don’t always land consistently. Like those in the lives it portrays, Spiritfarer‘s best moments are the small ones: a vista, a joke, a hug. 7 / 10.

Ori and the Will of the Wisps

In a triumphant return, Ori and the Will of the Wisps does everything a sequel should and more, bringing additional depth to all aspects of the experience. With the exception of some frustrating bugs, Ori‘s beautifully rendered environments, lovingly crafted characters, and slick platforming and combat mechanics have set the bar for metroidvania so much higher. 8 / 10.


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