Flu pandemic, political microtargeting, behavioral response to predator presence, species genetic diversity. Populations contain intricate connections that occur on time scales ranging from milliseconds to generations. At the Laboratory for Computational Population Biology, we explore the growing interface between Population Biology and Computer Science, from genetics to social interactions.




Baboons

Animals spend their lives on the move, creating networks of global connectivity that shape and increasingly are being shaped by ecosystem health. From the local quest to find food, shelter and mates, to the long-distance traverses of global migrations, movement provides a biological framework link- ing individual processes to population dynamics in organisms ranging from bacteria to humans.

 

Yet, animal movement remains a black box, limiting our ability to model and predict important processes, including potential impacts of human-induced rapid environmental change, spread of diseases, and sustaining biodiversity. Resolving these questions is computationally challenging because of the complex interplay between individual, social and environmental processes. [project page]



IBEIS

Image-Based Ecological Information System (IBEIS) is a large autonomous computational system that starts from image collections and progresses all the way to answering ecological and conservation queries, such as population sizes, species interactions, and movement patterns. IBEIS can detect various species of animals in those images and identify individual animals of most striped, spotted, wrinkled or notched species.  It stores the information about who the animals are, where and when they are there in a database, and provides query tools to that data for scientists and curious people to find out what those animals are doing and why they are doing it.[project page]




 


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Computational Animal Ecology
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Identifying zebras by their stripes

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The winning entry to the 2010 UIC Image of Research contest, describing the image preprocessing stage of an algorithm to automatically identify individual zebras from photographs. See the Projects page for more information.
Credit: Mayank Lahiri · License: Creative Commons Attribution-ShareAlike 3.0 Unported License.

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