About
I explain variance in X-ray imaging and run a computer vision workshop at UH. In my spare time I enjoy bouldering, cooking, and writing music.
Experience
Data Scientist Intern
(2019-Current)
End-to-end computer vision model creation, training, testing, refinement, and deployment for medical imaging. Fully integrated AI workflow with continuous integration, containerization, and deployment.
Education
B.S. Computer Science
(2018-Current)
Expected graduation Fall 2020 with minor in mathematics.
Research and Projects
Research: Deep Onset Detection
UTHealth School of Biomedical Informatics hosted a Machine Learning Hackathon in Sepetember 2019 for the purposes of using machine learning to identify predictive markers of sudden death in epilepsy (SUDEP). I placed third in the competition, and was invited to publish my method in a peer reviewed journal. I developed a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic-clonic seizure, as well as other EEG signal detection problems exhibiting data paucity. The prepublication is available here.
Project: Keras MobileDetectNet
MobileDetectNet is an object detector which uses MobileNet feature extractor to predict bounding boxes. It was designed to be computationally efficient for deployment on embedded systems and easy to train with limited data. It was inspired by the simple yet effective design of DetectNet and enhanced with the anchor system from Faster R-CNN.