Dr. Robert Wagner received his Ph.D. in Cognitive and Neural Systems from Boston University in 2004 and his B.Sc. in Computer Science from Purdue University in 1989. His research focused on both biological vision as well as computer vision to develop novel algorithms for robot navigation and control. While earning his doctorate, he also worked as a consultant to the Naval Research Lab where he developed a face detection and recognition system so that robots would turn towards and listen to spoken commands from a known person. Since 2004, Dr. Wagner has developed computer vision systems using both classical computer vision and deep learning for a variety of applications including: video fidelity enhancement, video surveillance, and large-scale visual search and retrieval. Prior to joining VSI, he focused his efforts on real-time object detection and tracking in the video surveillance domain. As part of this effort, he developed a deep learning data collection and image generation workflow for training a CNN-based weapon detector that is agnostic to background, pose, scale, and blur. He also created a test harness to compare different CNN models on a curated video corpus from in-house and YouTube sourced video. The best model was found by finding the model with the minimum inference time that provided high recall with low false alert rates. He is currently investigating the production of large-scale models of the surface of the earth.