"Identifying individual animals in the wild is a fundamental step in ecological analysis, used for everything from population size estimation to social network analysis. Animals are usually identified manually from photographs taken in the wild, which is a painstaking, error-prone process. To speed up this process and reduce identification errors, I have developed a computer-aided identification algorithm for wild zebras, similar to a fingerprint or face recognition system for humans.* Using photographs captured in the wild, a conservation worker can determine if a zebra has been sighted before, and bring up various types of information on prior sightings. The program is freely available to anyone, thanks to a UIC Provost's Award, and is currently in use at two field sites in Kenya: the Ol'Pejeta Conservancy and the Mpala Research Center. I captured this image in December 2010 on the Ol'Pejeta Conservancy in Laikipia province, Kenya. It shows a “zebraprint,” or how my computer program perceives the stripes of a zebra for identification purposes. Although the computer representation of zebra stripes looks similar to human fingerprints, the identification algorithm is more closely related to algorithms that are used to analyze DNA sequences. *This work was part of a project in the joint Princeton-UIC Computational Population Biology Course in Spring 2010 with co-instructors Tanya Berger-Wolf (UIC), Daniel Rubenstein and Iain Couzin (Princeton University). Funding for UIC Computer Science students was provided by a generous contribution from Bill Unger, and a UIC Provost’s Research Award for Mayank Lahiri, as well as NSF awards IIS-CTX-0705822 and NSF IIS-CAREER-0747369 (Berger-Wolf). We thank the Kenya Ministry of Education, Science and Technology, the staff at Mpala Resarch Centre and Ol’Pejeta Conservancy, Kenya and fellow graduate students at EEB-Princeton University and Computer Science at UIC."