Joshua J. Daymude

Joshua J. Daymude

Assistant Professor, SCAI & CBSS

Arizona State University

Welcome!

I am an Assistant Professor in the School of Computing and Augmented Intelligence and the Biodesign Center for Biocomputing, Security, and Society at Arizona State University. I research algorithmic theory for the efficient coordination and characterization of collective emergent behavior in biological, social, and engineered complex systems. I leverage several areas of computer science — including distributed computing, stochastic processes, swarm intelligence, and bio-inspired algorithms — to participate in interdisciplinary research on theoretical immunology, microbiomic ecology, active matter physics, dynamic networks, and programmable matter systems.

My faith as a Christian shapes how I see the world and my place in it. I view my research 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 curiosity 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.

If you’re interested in working with me, take a look at our open positions.

Interests
  • Distributed Computing
  • Dynamic Networks
  • Stochastic Processes
  • Programmable Matter
  • Immunology
Education

  • 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

Projects

Self-Organizing Particle Systems (SOPS)

Self-Organizing Particle Systems (SOPS)

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

GitHub SOPSLab@ASU
Adaptive Self-Organization in Anonymous Dynamic Networks

Adaptive Self-Organization in Anonymous Dynamic Networks

Reimagining graph-based models for moving and interacting entities. Designing concurrent and distributed algorithms that are robust to highly dynamic environments. Coordinating anonymous entities across time-varying tasks.

SAND Conference
A Distributed, Stochastic Framework for Active Matter

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 the physics of granular active matter systems.

SOPSLab@ASU CRABLab@GaTech

Recent Publications

The Canonical Amoebot Model: Algorithms and Concurrency Control
We present the canonical amoebot model, an updated formalization of the amoebot model of programmable matter that standardizes a hierarchy of assumption variants and addresses concurrency. We then demonstrate two approaches to concurrent algorithm design: embedding concurrency control directly, and leveraging a novel concurrency control framework.
Cite Project DOI arXiv Conference Version
The Canonical Amoebot Model: Algorithms and Concurrency Control
Invited Paper: Asynchronous Deterministic Leader Election in Three-Dimensional Programmable Matter
This paper formally extends the amoebot model of programmable matter to three-dimensional (3D) space using the face-centered cubic lattice. We then give a deterministic distributed algorithm for 2D/3D leader election that elects a leader from among $n$ amoebots in $\mathcal{O}(n)$ rounds in the sequential setting. Finally, we show that our algorithm can be extended to the asynchronous setting, a first for amoebot leader election algorithms.
Cite Project DOI arXiv
Invited Paper: Asynchronous Deterministic Leader Election in Three-Dimensional Programmable Matter
Cutting Through the Noise to Infer Autonomous System Topology
Public data on the topology of autonomous systems (ASes) participating in the Border Gateway Protocol (BGP) are notoriously incomplete and subject to error. This paper describes a method for reliably inferring AS-level connectivity using Bayesian statistical inference acting on BGP routing tables from multiple vantage points. We use the method to evaluate existing reconstructions and identify locations for new route collectors.
Cite Code DOI arXiv
Cutting Through the Noise to Infer Autonomous System Topology
See all publications

Recent Talks

Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems
This talk presents a randomized distributed algorithm for local mutual exclusion, a new variant of mutual exclusion tasking nodes to acquire locks over themselves and their “persistent neighbors” even as their connections change over time. I then discuss its applications to populations protocols and the canonical amoebot model.
March 29, 2022 5:20 PM SAND 2022
Project Slides Paper
Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems
Deadlock and Noise in Self-Organized Aggregation Without Computation
In this talk, we present new results for the algorithm for swarm aggregation by Gauci et al. (2014). We first identify deadlock configurations for $n > 3$ robots that never aggregate. We then show the algorithm’s robustness to noise and error. Finally, we demonstrate how generalizing the algorithm with cone-of-sight sensors can improve runtime.
November 17, 2021 9:40 AM SSS 2021
Slides Video Paper
Deadlock and Noise in Self-Organized Aggregation Without Computation
The Canonical Amoebot Model: Algorithms and Concurrency Control
This talk introduces the canonical amoebot model of programmable matter, a revision of the amoebot model that standardizes a hierarchy of assumption variants and treats all amoebot communication as message passing to facilitate studies of concurrency. I then present two complementary approaches to designing concurrent amoebot algorithms.
October 5, 2021 6:20 PM DISC 2021
Project Slides Video Paper
The Canonical Amoebot Model: Algorithms and Concurrency Control
See all events

Recent Courses

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

Featured Press

Researchers take a computational lens to theoretical immunology
Santa Fe Institute, June 2022.
Reducing extreme polarization is key to stabilizing democracy
Stephanie Forrest and Joshua Daymude. Brookings TechTank, January 2022.
Smart Swarms Seek New Ways to Cooperate
Kevin Hartnett. Quanta Magazine, February 2018.
See all press

From the Blog

Elegant Derivatives of Large Products
In math, there are often many roads to the same destination, and some roads are much nicer than others. In this post, we find the first derivative of $\prod_{i=1}^n f_i(x)$ two different ways: one direct and tedious, the other initially unintuitive but elegant.
November 30, 2021 5 min read teaching
Elegant Derivatives of Large Products
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.
July 9, 2020 11 min read teaching
Pool Testing is k-ary Search for COVID-19
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.
May 29, 2020 4 min read creative
Announcing Reviews
See all posts

Recent Reviews

Fable: Anniversary
At the time of this writing, Fable: Anniversary is an eight year-old remaster of an eighteen year-old classic, and that’s exactly how it plays. While clumsy mechanics and a bland story drag the game below enjoyable levels, its enduring humor and early innovations in player choice continue to shine. 6 / 10.
June 23, 2022 4 min read videogame-review
Fable: Anniversary
Donut County
Donut County taught me that destroying civilization as a hole in the ground is the most satisfying way of channeling your chaotic evil. This short, colorful, and hilarious game is a must-play, especially if you’re looking for an afternoon pick-me-up or a palette cleanser between longer games. 8 / 10.
May 23, 2022 2 min read videogame-review
Donut County
Bugsnax
This whimsical but bland adventure sets you, a struggling journalist, on the path of legendary explorer Elizabert Megafig and her search for bugsnax—part bug, part snack! Unfortunately, the hit-or-miss fun of snak-hunting, the bizarre narrative, and characters' over-the-top personalities never gel to make a cohesive whole. 6 / 10.
May 22, 2022 3 min read videogame-review
Bugsnax
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Contact

  • 727 E. Tyler St., Tempe, AZ, United States