Mapping Ant Transportation Networks

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.

Tree Mapping. One important part of this work is the ability to efficiently describe real transportation networks created by turtle ants in their natural habitat: mangrove trees in the Florida Keys. Our first goal is to map out the spatial location of all ant nests in a tree, and describe the potential pathways between them. While this can be done by hand, we are working to develop a method for documenting the 3D structure of a tree using terrestrial laser scanning and photogrammetry with an iPhone 12, based on existing methodology known as quantitative structural modeling. A student working over the summer was able to represent a physical tree as a (1) point cloud, (2) geometrical model of interlocking cylinders, and (3) a network as a proof of concept. The next step is to refine these processes to make them more accurate and consistent, and then (if travel restrictions allow) collect data on real trees. This project requires strong programming skills in Python and/or MATLAB as well as experience working with networks / graphs (e.g. completion of CS70 and Discrete Math). 

Ant Tracking. Our second goal is to quantify the movement of ants along specific paths in laboratory experiments. We have already developed a software pipeline to automatically track ant movement from videos taken in the lab. A student working over the summer enhanced the pipeline to accurately and consistently detect specific regions of interest within videos; the next step is to test and improve the tracking of ants within those regions of interest, and use the tracker to gather and process data from the summer's experiments. This project requires strong programming skills in Python, some knowledge of computer vision, and familiarity with Github (e.g. completion of CS70 and CS121).

Name of research group, project, or lab
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. These research projects are part of a larger NSF-funded collaborative research project, so you will interact with a larger research group including graduate students and postdoctoral researchers at George Washington University and the University of York. 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 stemming from ongoing work.

Logistics Information:
Project categories
Computer Science
Computational Biology
Computer Vision
Data Science
Machine Learning
Student ranks applicable
Student qualifications

Students should have an interest in the complexity of the natural world and in the application of quantitative tools and models to understand it. For these projects, I am looking for advanced students in Math, CS, Engineering and Biology looking to apply their skills to practical problems across disciplinary lines.

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

  • Programming in MATLAB and/or Python 
  • Linux/UNIX command line and shell scripting, high performance computing 
  • Computer vision and image processing, e.g. using MATLAB or OpenCV
  • Data science and visualization, particularly with R
  • Graph theory, geometry, networks
Time commitment
Fall - Part Time
Academic Credit
Paid Research
Number of openings
Techniques learned

Students will create and execute a plan for validating and testing a software pipeline, interpret the results, and apply the knowledge gained to tune and improve the software's performance on relevant biological inputs. Students will use the pipeline to extract biological data and will learn analyze and visualize the results in R. Students will develop communication skills needed to effectively bridge the disciplines of experimental biology and software engineering.

Contact Information:
Mentor name
Matina Donaldson-Matasci
Mentor email
Mentor position
Associate Professor of Biology
Name of project director or principal investigator
Matina Donaldson-Matasci
Email address of project director or principal investigator
2 sp. | 3 appl.
Hours per week
Fall - Part Time
Project categories
Computer Vision (+6)
BiologyComputer ScienceEngineeringComputational BiologyComputer VisionData ScienceMachine Learning