- PhD in Computer Science at University of California, Santa Barbara. Fall 2017 - June 2022 (expected)
- BS in Computer Science at University of Tennessee, Knoxville. Class of 2017
- Point Supervised Semantic Segmentation: Point supervision describes an environment where annotators are asked to label one pixel of each instance of any class of interest. With this small amount of additional supervision, we can achieve impressive semantic segmentation results comparable with fully supervised methods by using convolutional neural networks to learn semantic affinity and localization cues.
- Semantic Segmentation of Underwater Images of Sessile Organisms: The Marine Science Institute at UCSB has worked to photograph the sea floor near the Channel Islands and annotate their data. We are adapting state of the art algorithms for segmenting natural images to work with their data.
- Scene Recognition in Underwater Vehicle Footage: Marine Applied Research and Exploration (MARE) has collected hundreds of hours of footage using their unmanned underwater vehicle and annotated each video with species and scene labels. We are working on adapting scene recognition algorithms to their video, and, later, species recognition and detection.
- Digital Image Processing
- Computer Imaging
- Information Theory
- Matrix Analysis
- Machine Learning
- Pattern Recognition
- Computer Vision
- Topics in Cybersecurity
- Operating Systems