Research Interests

I completed my PhD at the Salk Institute at UCSD, under the supervision of Dr. Saket Navlakha. My research focuses on the emerging field of biological distributed algorithms. Nature is abound with examples of biological systems, such as insect colonies, networks of neurons, slime molds, and bacteria swarms, that manage to overcome environmental obstacles and challenges in a distributed manner. Rather than relying on a centralized processor to direct control flow, the systems use limited local communication to produce complex collective behavior. Not only are these systems quite elegant in their emergent properties; the challenges they overcome often have direct analogues to problems in computer science and combinatorial optimization. How do these systems succeed under limited constraints? It is often said that necessity is the mother of invention. Biological systems without the luxury of central control have had the opportunity to innovate and refine methods on evolutionary time-scales. It thus stands to reason that if we can understand how these systems behave, we will arrive at a new, and perhaps improved, understanding of distributed computing. My research seeks to understand how these systems operate, and reverse-engineer efficient distributed optimization algorithms. To this point I have focused on three particular systems: ant colonies, individual neural arbors, and tomato plant root architectures.

Turtle Ant Foraging Algorithms

Unlike the terrestrial ants that invade your kitchen, Cephalotes, colloquially known as “turtle ants”, are an arboreal ant genus - that is, they nest and forage in trees. The available foraging paths are constrained by the overlapping branches, vines, and junctions in the vegetation. This makes their foraging directly analogous to pathfinding and other optimization problems in discrete graphs. In my research I seek to understand the behavior of turtle ants from an algorithmic perspective. My research, in collaboration with Dr. Deborah Gordon, focuses on the following questions:

  • What is an appropriate model of computation for characterizing how the colony collectively communicates and maneuvers?
  • What distributed algorithm do turtle ants use to construct, maintain, and repair trail networks?
  • What objectives do turtle ants prioritize when constructing trail networks?

Biological Network Growth Algorithms

Many biological systems depend on transporting resources between a central hub and several peripheral locations. Such systems can be seen as transport networks rooted at the central hub: the hub and peripheral locations represent nodes and the connective material represents edges. These transport networks must balance trade-offs between wiring cost (conserving the material needed to construct the network) and conduction delay (minimizing the time need to transport resources across the network). My research studies how biological networks optimize trade-offs between these competing objectives through the following questions.

  • Given locations of the central hub and peripheral locations, how do we algorithmically find the optimal topology for wiring the network?
  • Do biological networks achieve optimal trade-offs between wiring cost and conduction delay? Do they optimize these objectives better than would be expected by chance?
  • Do certain types/categories of networks optimize tradeoffs differently from (and perhaps, better than) other types of networks?
  • What is an appropriate model of computation for characterizing how biological networks grow?
  • What distributed growth algorithm do biological networks use to achieve optimal wiring morphologies?
  • To this point I have focused on network design trade-offs in the following biological systems:
    • Neural arbor cells: The neural arbor consists of a cell body that is connected to pre- and post-synaptic partners through axonal and dendritic wiring. In collaboration with Dr. Saket Navlakha, I have studied how neural arbor cells balance trade-offs between wiring cost (conserving wiring material) and conduction delay (minimizing the time required to transmit signals between the cell body and synapses), and how neurons from different cell types, brain regions, and animal species optimize objectives differently
    • Tomato root architecture: The root architecture of wild tomato plant, Solanum Pimpinellifolium, comprises a main root that grows downward, with lateral roots branching off of the main root in search of nutrients to be transported to the main root base. In collaboration with Dr. Magdalena Julkowska, I have studied how S. Pimpinellifolium balances trade-offs between wiring cost (conserving root material) and conduction delay (minimizing the time required to transport nutrients to the main root). I have also studied genetic variation on network design trade-offs, as well as how plants modify trade-offs in response to environmental stressors. This work is supported by NSF Grant DMS-2244735. This project is jointly funded by the Division of Mathematical Sciences, Mathematical Biology Program and the Division of Integrative Organismal Systems, Plant Genome Research Program (PGRP) in the Directorate for Biological Sciences.

    BDA Workshop

    Since 2023, I have served as a co-chair of Workshop on Biological Distributed Algorithms (BDA). This annual(-ish) workshop, which takes place in conjunction with the Principles of Distributed Computing (PODC) conference, brings together the community of researchers who study the interface between systems biology and distributed computing algorithms. BDA 2024 will take place on Monday, June 17th at La Cité des Congrès in Nantes, France.

    Past students

    aka a list of people who have done all of the work to make me look good.

    • Alley Koenig (Southwestern University class of 2024)
    • Kathryn Altman (Southwestern University class of 2024)
    • Samy Lokanandi (Pitt class of 2022)
    • Graham Zug (Pitt class of 2021)