Undergraduate Research: An Archive - 2022 Program

Yael Stochel ’22
COMPUTER SCIENCE
Certificate in Environmental Studies
CONSERVATION
AND BIODIVERSITY
THESIS TITLE
Making Models and
Mining Mimics: Insights
From Computer Vision
Into How Biological
Systems Solve Visual
Problems
ADVISER
Daniel Rubenstein,
Class of 1877 Professor
of Zoology, Professor of
Ecology and
Evolutionary Biology
The butterfly genus Heliconius exhibits
Mullerian mimicry, in which unrelated toxic
species evolve to share one another’s warning
signals as a defence against predators. A
common point of contact between biology and
computer science uses machine learning and
computer vision to classify species. Building
upon previous work in this field, my research
sought to expand classification to capture the
biological mechanisms underlying mimicry. By
modifying the training methods and inputs used
in machine learning, computer vision is capable
of creating representations of natural systems of
mimicry. One approach — which modified the
training method — trained classification on one
Heliconius species before testing on its mimic in
order to approximate the training and learning
process undertaken by avian predators in the
wild. The other sought to account for the visual
complexities of butterfly mimicry by adjusting
the visual acuity of the images to better represent
butterfly and bird vision. These methods were
successful, with significant results indicating
that the model effectively represented the
mimicry system.
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