MY RESUME

June - September 2020

CNA SUMMER INTERN 
DATA SCIENCE PROGRAM

​

I supported Navy decision-makers with interactive tools and data science. For example, I constructed an interactive dashboard to visualize Navy supply chains, analyze the drivers of logistics response time, and inform evidence-based decisions. I built an optimization tool to determine ideal combat group designs and assess tradeoffs between cost and operational capability. I quantified scheduling risks and provided mitigation suggestions for platforms at-risk of deployment interruption due to COVID-19 effects on shipyards. I extracted trends of maintenance patterns by developing time-dependent metrics from the integration of large datasets. 

​​

I was privileged enough to work under the supervision of Dr. Adi Zolotov and Tim Kao and to have opportunities to brief my work to Navy stakeholders and the CNA C-Suite team.

July - November 2019

DATA ANALYTICS INTERN
RETINAL CARE

I integrated data sources and machine learning techniques to improve the predictive ability of a novel eye care screening device. I improved the predictive ability and prevented unnecessary care by developing a personalized prediction model using individual patient attributes and screening measurements. I transformed raw data from multiple clinical trials into process improvement recommendations aimed at operational efficiency of a small business. I continuously adjusted research to align with company priorities by collaborating with the company president, chief research officer, and physician.

Aug 2018 - June 2019

RESEARCH ASSISTANT
NC STATE & SOUTHEAST MEDICAL CENTER

I characterized processes in the emergency department (ED) and provided recommendations to improve the patient and staff experience. I integrated multiple sources of big data across varying times and departments. I identified key variables which best defined variance in the ED operations using data mining and advanced statistical analysis. I optimized staffing in a linear programming model generated from the endogenous patient volume and nurse capacity while considering staff preferences. I measured the effectiveness of staffing designs on reducing patient time in the ED and balancing workload by building a 3-D simulation grounded in data. I ensured implementable, relevant, and timely results by managing university research team and collaborating with hospital directors, providers, and analysts.

Aug 2017 - Present

RESEARCHER, DISSERTATION PROJECT
NC STATE

I implemented machine learning techniques to identify patients at high risk of microvascular complications due to diabetes mellitus. I designed an algorithm which tests combinations of machine learning techniques to find the model which most accurately identifies high risk patients. I cleaned large amounts of data from a national clinical trial to address missingness, imbalanced variables, and to adhere to underlying model assumptions. I quantified robustness and performance in an unbiased nested cross validation technique. I served as Lead investigator on IRB protocol.