Joshua J. Daymude

Joshua J. Daymude

Assistant Professor, Computer Science

Arizona State University

Welcome!

I am an incoming Assistant Professor of Computer Science 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.

I’m currently recruiting students at all levels (PhD, Master, undergraduate, and high school).

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.

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.

Recent Publications

Recent Talks

Recent Courses

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

Featured Press

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.

Contact

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