CDI Type I: Collaborative Research: Machine Learning in Taxonomic Research

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Intellectual MeritIt is estimated that less than 10 percent of the world's species have been described, yet species are being lost daily due to human destruction of natural habitats. Considering the fast pace of habitat destruction, experts fear that many species will become extinct before they can be discovered and formally described. The job of describing the earth's remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In describing new species of animals, taxonomists typically rely on specimens deposited in natural history museums. They have to make careful counts and measurements on large numbers of specimens from multiple populations across the geographic ranges of both known and newly discovered species, in order to diagnose the new species as distinct from all of its known relatives. The process is laborious and can take years or even decades to complete, depending on the geographic range of the species. In this project, the research team will develop new machine learning methods for taxonomic research, with the specific aim of fundamentally increasing the pace of taxonomic revision.The scientific research will focus on two areas: species identification and new species discovery. In distinguishing a species from others, taxonomists must identify a set of diagnostic characters that distinguishes the species in question from all of its known relatives. To automate and expedite this laborious process, the team will explore existing feature subset selection techniques, and will develop new ones. Images of categorized specimens will be used to train a collection of statistical models representing the known taxonomic grouping of organisms. An "optimal" set of body shape characters will be automatically identified. New species discovery is the most important research objective in taxonomy. From a machine learning point of view, detecting new species is fundamentally different from the problem of recognizing known species because by definition, the training set does not contain prior knowledge of a new species. The research team will formulate new species discovery as a novelty detection problem, and will develop an efficient novelty detection framework for taxonomic tasks.Broader ImpactThe project, if carried out successfully, will demonstrate the fruitfulness of fusing technologies from different fields. From the biology side, the project will demonstrate that machine learning techniques can assist taxonomists and evolutionary biologists in various research tasks, hence fundamentally accelerate the pace of taxonomic revision. From the computer science side, researchers will benefit from the computational challenges motivated from real-world biological problems and the database created in the project. New machine learning algorithms will be developed to impact taxonomic research. The PIs will integrate the research with their educational activities at both University of Mississippi and Tulane University. The PIs will devote additional efforts to mentoring female and underrepresented minority students involved in exploring cutting-edge interdisciplinary research.