ELT Developer / Project Manager — SmartVape / eCigTracker

The effect of electronic cigarettes (eCig) has not been largely studied. The lack of concrete measures for nicotine intake is a major area lacking in eCig vaping research. It is a result of the vast differences in eCig devices that range from a 200-watt box mod device to an 8.5-watt Juul. Compounding this issue is the ability for vapers to choose between a huge variety of electronic cigarette liquids(e-liquid) from 60 mg/ml salt nicotine to 3 mg/ml freebase nicotine. This project aimed to create a methodology that would allow researchers to get qualitative data on the device, e-liquid, and vaping habits in an attempt to receive concrete data for intervention resources. In short, vapers would take pictures of their device and e-liquids on the first day of the intervention. Then, on later days, the vapers would take pictures of their device and liquids so that we could see how much and at what wattage e-liquids were vaped.

Web scraped e-commerce websites using Python's Selenium package.
Hosted on Amazon Elastic Compute Cloud (AWS EC2)
Stored scraped JSON information in AWS DynamoDB.
Stored scraped image data in Amazon Simple Storage Service (AWS S3.)
Normalization of device and e-liquid data.
Creation of an SQL database using Amazon Relational Database Services (AWS RDS) from web-scraped data for vapers to easily look up their eCig and e-liquid devices.
Requirements planning with Primary Investigators.
UX/UI design of eCigTracker iOS application.
Developed methodology for image analysis.
Developed intervention materials and trained staff on intervention requirements.
Handled interactions and document submissions with the Institutional Review Board (IRB).