This collaborative, multi-disciplinary project will exploit recent progress in genomics - the study of the complete genetic content of species and how it works - to greatly advance our knowledge of evolutionary relationships in the insect order Lepidoptera (moths and butterflies). A broad-scale "family tree" (phylogeny, genealogy) will be estimated using DNA sequences from approximately 250 species, representing all 126 families into which Lepidoptera are currently divided.

Rapid advances in DNA sequencing technologies are providing scientists with the ability to rapidly and cost-effectively decode the genomes of organisms. Current technologies, however, can only reconstruct a fragmented picture of a genome's chromosomes. Stitching the resulting fragments together into a complete genome currently requires costly and time-intensive laboratory experiments.

To apply machine learning to problems in the physical world, one needs models/algorithms that are faithful to physics. We consider understanding how the anatomical structure of the body and ears leads to the remarkable ability to localize a sound source in a complex and noisy environment that is innate in most animals and humans. The cues used in localization arise from the process of the acoustic wave scattering off the complex-shaped listener's body and ears. Numerically, these changes in the sound spectrum are characterized by the head-related transfer function (HRTF).

The automatic identification in images of people, places, objects, and especially object categories is a central and ongoing challenge within computer vision. This project addresses this problem using low-level image features to learn intermediate representations, ones in which objects in images are labeled with an extensive list of highly descriptive visual attributes. This work demonstrates this approach in three domains: faces, plant species, and architecture.