Dengue & Nipah Virus Eco-Epidemiology

Project Description

Our research integrates artificial intelligence, computer vision, and One Health principles to enhance biosurveillance and ecological forecasting. The project focuses on two interrelated efforts: (1) developing automated image detection models to identify bats in photographs and video frames for abundance, behavior, and conservation studies, and (2) using high-resolution macro imagery of medically important arthropods (e.g., mosquitoes, ticks) captured via Cognysis StackShot equipment to generate 3D reconstructions.

Interns contribute to training and validating deep-learning image detection models, annotating imagery datasets, processing macro-stacked imagery, and supporting open-source documentation. These technologies support public health education and predictive modeling of zoonotic spillover risks such as Nipah virus in Bangladesh.

Mentor

Johnny Uelmen

Interns

• Allie Hannum (Spring 2026)

Lightning Talks

• No talks available yet.