Keynote Speaker:

  • Andrea W. Richa, Arizona State University (ASU), USA
  • Biography:

    Andrea W. Richa was inducted as 2022 President's Professor at Arizona State University (ASU), one of the most prestigious faculty honors bestowed by the university. She is a Professor of Computer Science and Engineering at the School for Computing and Augmented Intelligence (SCAI) and an associate faculty at the Center for Bio-computing, Security and Society at the Biodesign Institute at ASU. She recently served as SCAI's Interim Associate Director. Her main areas of expertise are in distributed and network algorithms and computing in general. More recently, she has focused on developing the algorithmic foundations on what has been coined as programmable matter, through her work on self-organizing particle systems (SOPS). Her work has been widely cited, and includes, besides SOPS, work on bio-inspired distributed algorithms, distributed load balancing, packet routing, wireless network modeling and topology control, wireless jamming, data mule networks, underwater optical networking, and distributed hash tables (DHTs). She received the 2024 ASU Fulton Undergraduate Research Initiative Outstanding Faculty Mentor Award, the 2021 ASU Faculty Women Association Outstanding Mentor Award and the 2017 SCAI Best Senior Researcher award. She is currently the recipient of a DoD MURI award and was the recipient of an NSF CAREER Award, among others; she was also the keynote speaker and program and general chair of several prestigious conferences. For more on her work and that of her students, please check http://sops.engineering.asu.edu .

  • Title: Algorithmic Programmable Matter: From Local Markov Chains to "Dumb" Robots
  • Abstract:

    Many programmable matter systems have been developed, including modular and swarm robotics, synthetic biology, DNA tiling, and smart materials. We describe programmable matter as an abstract collection of simple computational elements (particles) with limited memory that each execute distributed, local algorithms to self-organize and solve system-wide problems, such as movement, reconfiguration, and coordination. Self-organizing particle systems (SOPS) have many interesting potential applications like coating objects for monitoring and repair purposes, and forming nano-scale devices for surgery and molecular-scale electronic structures. We describe some of our work on the algorithmic foundations of programmable matter, investigating how macro-scale system behaviors can naturally emerge from local micro-behaviors by individual particles: We utilize tools from statistical physics and Markov chain analysis to translate Markov chains defined at a system level into distributed, local algorithms for SOPS that drive the desired emergent collective behavior for the problems of compression, separation, and foraging, among others. We further establish the notion of algorithmic matter, where we leverage standard binary computation, as well as physical characteristics of the robots and interactions with the environment in order to implement our micro-level algorithms in actual testbeds composed of robots that are not capable of any standard computation. We conclude by addressing full concurrency and asynchrony in SOPS.