Tracking Ant Movement in Artificial Trees

What do ant colonies and railroad systems have in common? Both serve to transport goods and individuals from place to place, and need to balance the often competing goals of doing so efficiently and at low cost, while also remaining robust to potential disruptions to the network. We are currently working to understand how simple organisms can work together in groups to create and maintain such transportation networks, using a multidisciplinary combination of field and laboratory experiments with turtle ants, along with mathematical and computational models.

Last summer, we conducted a series of experiments designed to discover how turtle ants move within a tree-like branching structure, and how individual movement choices lead to collective decisions about where to nest within that structure. We now have a good understanding of how individual ants make choices at branching junctions; the next step will be to describe how these choices are modified when many ants are exploring together. To do this, we need to test and improve our existing software pipeline, which uses computer vision to automatically track ant movement from videos taken in the lab. This will allow us to collect much more data on ant turning choices in the challenging context when multiple ants are navigating the same structure. With this data, we will be able to describe how turtle ant colonies use communication to coordinate collective decisions.

Name of research group, project, or lab
The HMC Bee Lab
Why join this research group or lab?

You will be part of a team of students working on a set of related interdisciplinary projects, using mathematics, computation and engineering to solve problems of biological interest. The variety of techniques and approaches will give you an opportunity to explore your interests and develop new skills. This project has connections to the study of complex systems and artificial intelligence, and has potential applications to the development of computational optimization algorithms. There may be opportunities to continue the work in a senior thesis, present at a regional or national conference, and/or co-author future publications. The work is funded by the National Science Foundation, and could lead to funded travel opportunities including an ant collecting trip to the Florida Keys or summer research at the University of York.

Representative publication
Logistics Information:
Project categories
Computer Science
Computer Vision
Data Science
Student ranks applicable
Student qualifications

Strong programming experience in Python is required.

Here is a list of other skills/interests that could be useful, or which you might develop along the way. They're not requirements, but if you already have experience or interest in any of them, be sure to mention it in your application.

  • Linux/UNIX command line and shell scripting, high performance computing
  • Computer vision and image processing, e.g. using OpenCV
  • Machine learning and classification
  • Data science and visualization, particularly with R
  • Ecology and animal behavior
Time commitment
Fall - Part Time
Academic Credit
Number of openings
Techniques learned

In this project, you will

  • contribute to a software pipeline written in Python
  • test and optimize common computer vision tasks such as object recognition and tracking
  • extract, analyze and visualize data resulting from the pipeline

You will also learn to read and discuss scientific literature, and to communicate across disciplinary boundaries and with the public about your work.

Contact Information:
Matina Donaldson-Matasci
Associate Professor of Biology
Name of project director or principal investigator
Matina Donaldson-Matasci
Email address of project director or principal investigator
2 sp. | 7 appl.
Hours per week
Fall - Part Time
Project categories
Computer Science (+3)
BiologyComputer ScienceComputer VisionData Science