Welcome to my website! Learn more about my recent experiences below. Visit the tabs at the top of the page for more information about me and my projects or for examples of my writing.
Data Science R&D Graduate Intern (2021)
I had the exciting opportunity to dive into the intersection of cybersecurity and deep learning at Sandia National Labs! I designed and validated Graph Neural Network architecture for automated malicious event detection. The model I developed achieved performance on par with industry standards for the particular task we were addressing as measured by F1-score across multiple datasets. Additionally, I spearheaded a project to implement significant UI changes to facilitate model explainability in the Incident Response pipeline for the cybersecurity team. The changes I made were developed through iterative research, prototyping and surveyance, and they remain in deployment on a national scale. I had the chance to work with a number of incredibly talented individuals from diverse backgrounds in data science, cybersecurity, computer engineering, and human-computer interaction.
Data Scientist (2020)
I worked with a small team of data scientists to develop end-to-end data pipelines for BYU Enrollment Services. We worked to track student success within each Major degree program and statistically identify optimal Major Milestones for student success. This was a time of huge growth for me as we were given little established architecture and free reign to organize code and develop models as seemed best. This resulted in heavy research and trial-and-error that proved to be invaluable experience for me.
UNCW Research Experience for Undergraduates (2019)
I participated in the summer REU for Statistical Learning and Data Mining at the University of North Carolina at Wilmington in 2019. The NSF-funded REU gave me the opportunity to work closely with supervising professors to develop and test novel features for detecting atrial fibrillation using electrocardiogram data. I learned a lot about conducting research in unfamiliar fields, collaborating in a professional environment, and writing and presenting academic research. Read my report on the subject to learn more about our research!
Individual Projects (2018-2020)
From deep learning to natural language processing to optimal control theory, I’ve completed a variety of projects over the years that showcase my abilities. To the left, we have an example image of skin cells alongside a classification image describing which areas of the image are cancerous. One example project involved training a convolutional neural network to recognize cancer in these images. Read more on my projects page!
NCH Capital Machine Learning Research Internship (2018-2019)
I worked with a small team to validate proprietary metrics for stock prediction using machine learning techniques. We went on to combine in-house data with data from BYU’s Bloomberg terminal to identify features to be used in anomaly detection for growth-based portfolio investment strategies. If leveraged correctly, such features can save an investment firm billions in the case of large economic upheaval. It was exciting to learn about and implement my own machine learning models on real data for the first time. It opened my eyes to the difficulties of such data and the work that goes into data cleaning and munging.
Math TA (2017-2019)
I assisted in writing and developing learning material for a variety of early mathematics courses offered at Brigham Young University during my time as an undergraduate student there. I also regularly taught small groups of students and tutored individuals in these courses.