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.

To explore these questions, we are conducting a series of experiments designed to discover what influences individual ant movement choices as they explore artificial trees, and how these choices lead to collective decisions about where to nest within that structure. To analyze the data from these experiments, 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 augment the data and analyses already generated by previous experiments, and to analyze ant behavior in new experiments to be conducted this summer. With this data, we will be able to describe how turtle ant colonies use communication to coordinate group behavior and construct efficient transportation networks.

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, when completed, could contribute to pollinator health by helping researchers assess the quality of a habitat for bees. 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.

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
Summer - Full Time
Paid Research
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
1 sp. | 10 appl.
Hours per week
Summer - Full Time
Project categories
Computer Vision (+3)
BiologyComputer ScienceComputer VisionData Science