The Sonoma State University Department of Mathematics and Statistics presents Mario Bañuelos, Fresno State. Genomic anomalies, or variations, are often shared between members of the same species. Although rare, these changes may result in disease or an increase in host fitness. Most approaches for detecting structural variation rely on high quality data and are typically limited to one type of structural variant such as deletions or in- versions. These genomic changes are often difficult to detect. Standard approaches for identifying such variation involves comparing fragments of DNA from the genome of interest to a reference genome. This process is usually complicated by errors produced in both the sequencing and mapping process which may result in an increase in false positive detections. In this work, we describe gradient boosting, neural network, and recommendation systems approaches in the context of ge- nomic variants. Series supported by Instructionally-Related Activities Funds. For more information about this series, go to www.sonoma.edu/math