· Developed a machine learning model using insurance claim and medical data to predict insurance churn. This allowed for proactive intervention to improve patient health by reducing insurance gaps with outreach.o Utilized Python and SQL to query data, completed exploratory data analysis (EDA) and feature engineering with imputation for missing values.o Built and tested a variety of models from the sci-kit learn library including Logistic Regression, Random Forest, XGBoost, Decision Trees, etc.o Created a pipeline to hyperparameter tune each model and validate the results.o Documented the results and methods to the team’s model catalog (Confluence).· Designed a function in python to impute missing demographic variables (race and ethnicity) utilizing the Surgeo package from CMS that is utilized department-wide. · Provided domain expertise in healthcare to contribute to project scoping, EDA and model validation.· Scoped and initiated a dimensionality reduction to convert ~70,000 ICD codes to ~3,000 as part of an analysis for predicting avoidable Emergency Department encounters.· Facilitate communication between stakeholders and data science teams, leveraging insights from end-users to inform data-driven decision-making.· Strengthened documentation of database structures and methodologies. · Developed a descriptive web page utilizing Confluence to articulate model workflows and enhance team collaboration.· Evaluated vendor data for demographic variables and provided recommendation on strategy and use.

As an instructor I taught skills to be able to perform EKGs that include heart physiology, basic arrhythmias, and understanding the fundamental concepts of an EKG. I also addressed practical topics such primary and alternate lead placements, troubleshooting to create high quality EKGs, and professional patient interaction.

I conducted cardiac stress tests, EKGs, and EEGs in various parts of the hospital, including the NICU and ER. I interpreted and summarized initial EKG results for physicians to evaluate. I also managed and troubleshot equipment to ensure proper functioning. Finally, I trained other employees on how to properly place and connect electrodes to perform EKGs and EEGs.

I was an undergraduate genetic researcher under Dr. Chow within the University of Utah’s School of Medicine since June 2017. My area of research was focused on how the inheritance of certain genetic markers will affect different physiological outcomes, specifically the pathogenesis of retinitis pigmentosa (RP). RP is a hereditary disease that causes retinal degeneration. This is really fascinating research that could help lay the groundwork for personalized treatments against RP. I originally studied these genetic markers in Drosophila Melanogaster and compared the area of the eyes of the different DGRP (Drosophila Genetics Reference Panel, which is a collection of approximately 200 inbred strains of Drosophila that were derived from the wild) lines with the genetic markers to those without. Based on the data I collected by comparing the DGRP, I was able to analyze the genetic sequences and perform an association analysis to identify the more likely candidate genes. I tended to the Drosophila stocks, imaged the frozen samples, and then measured the area of their eyes using NIH ImageJ software. I was also able to learn and use some other techniques such as TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labeling) staining (also known as a TUNEL assay) and PCR (Polymerase Chain Reaction).

I tutored a variety of math subjects to students weekly in subjects varying from elementary school mathematics to college level calculus.