Nice to meet you! I am a graduate student using deep learning to denoise and segment images of bone in the Fracture and Fatigue of Skeletal Tissues Lab at UC San Diego. I'm graduating with my PhD in December 2024 and am looking for opportunities to continue my passion for machine learning and engineering.
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I enjoy coding deep learning and machine learning solutions to grand problems. So far, I have focused on denoising and segmenting micro-CT images to reduce the radiation dose required for each scan. I love collaborating at the crossroads of several fields!
Spending time with family and friends is super important for me. As far as hobbies, I currently love running and playing the game Slay the Spire, though I also actively play tennis, climb, and ski! Going into the outdoors for hiking is the ideal way for me to spend the weekend.
About MeOver 6+ years of research experience at multiple universities and NASA, and currently seeking opportunies to contribute to an impactful technology company. I implement and develop machine learning solutions for scientific images now, but am open to application of deep learning in all spaces. So far, I've primarily worked on CT scans, which you can read about in the projects section below. I also have extensive experience performing mechanical tests, such as toughness and fatigue tests at UC San Diego, University of Utah, and NASA. I tested the structural integrity of bone and how it is compromised by disease.
Linkedin Download My ResumeRadiation dose for CT imaging of bone samples was reduced by 80% using a deep learning approach. This enabled an in situ test that revealed the collagen's important in bone's fracture resistance at the microscale.
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Self-supervised learning was used to denoise CT images of bone with no high-quality target images required. This method reduced dose imparted by a CT scan by 66%.
Full StudyData analysis of thousands of voids and the complex vascular networks in bone using Python (Pandas) revealed the complex relationships between disease and microstructure, with new findings published in several journal articles.
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