March 16, 2026
AI Tools for Biodiversity: Beginner Workshops with INSECT NET throughout April 2026
Curious how artificial intelligence can help scientists study biodiversity? Join the INSECT NETwork for a series of free, beginner-friendly workshops exploring object detection, interactive data visualization, and image classification—using insects as our model system. No coding experience required!
Join the INSECT NETwork for a series of free, beginner-friendly workshops introducing artificial intelligence and data science tools used in biodiversity research. These sessions are open to faculty, students, and staff from any discipline, and no prior coding experience is required. Using insects as our focal organisms, participants will explore how modern tools such as object detection, interactive data visualization, and image classification can help scientists monitor and understand the natural world.
Object Detection for Beginners
Thursday, April 9 | 3:30–6:00 PM | W-201 Millennium Science Complex
Learn how computers can automatically detect insects in images. Participants will use provided Python code in a Jupyter Notebook to explore how existing object detection algorithms identify objects within photos. Attendees are welcome to experiment with their own images while learning the basics of this powerful AI technique used in ecological monitoring and automated species detection.
Register Here!
RShiny for Beginners
Wednesday, April 15 | 3:30–6:00 PM | 209 Thomas Building
Discover how to turn large datasets into interactive visualizations. In this workshop, participants will learn the basics of RShiny, a framework for building interactive dashboards in R. Using open-source data from iNaturalist, attendees will create visual tools to explore biodiversity patterns and trends in insect observations and other taxa of interest.
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Image Classification for Beginners
Wednesday, April 22 | 3:30–6:00 PM | 209 Thomas Building
Explore how artificial intelligence can learn to recognize species from images. Participants will use Python and Google Colab to train and evaluate a simple image classification model using user-submitted iNaturalist photos. This hands-on introduction walks through the key steps of building a machine learning model—from preparing training images to testing how accurately the model identifies species.
Register Here!