• Built Node.js and Golang backend features in microservice architecture to improve user-to-offer matching algorithms and enable self-service business operations

• Increased revenue per visit by 20%+ and improved user progression rates through React.js frontend UX enhancements

• Implemented infrastructure as code using Terraform, created CI/CD pipelines, configured Kubernetes deployments, and implemented monitoring/alerts for system reliability

Foundation Medicine conducts genomic profiling tests, which gives doctors and patients detailed insights into the genetic makeup of their cancer.

They can use these insights for more targeted, rather than general, treatment options.

Here, I worked on the EMR Integrations team. We were responsible for the test ordering system on hospital software, which was enabled by Kafka.

My duties included:

• Completing tickets add features or fix bugs in our JavaScript codebase

• Add unit tests to be ran by Jenkins in CI/CD pipeline to ensure that my changes were thoroughly validated

• Program Node.js scripts to speed up developer processes, like encoding/decoding base64 all on the CLI

I worked on the Insider Risk team, tasked with completing a summer project to notify our Security Operations Center of potential bad actors within the company.

To do this, I created an AWS architecture that:

• Used a Python Lambda with an SQL query filtering employees by seemingly suspicious behavior (like mass downloading sensitive documents to a USB)

• Deployed API Gateway to call this Lambda and send response to SOC

We also had a brief intern hackathon within the company, in which I:

• Collaborated with a group to create a React website for non-engineering employees to post engineering problems they had so that company engineers can solve them

• Hosted this website on AWS Amplify with Cognito authentication

Ruiz Foods sells frozen Mexican food under the El Monterey brand in grocery stores and under the Tornados brand to convenience stores for their roller grills.

They store pallets of products and ingredients in large, frozen warehouses at -10°F and heights up to 50 feet.

Sometimes, pallets aren't placed perfectly, and they tip. A tipped pallet can end up falling over which causes product loss, time loss to fix it, and even potentially injury.

To address this problem, Ruiz Foods wanted to implement a solution involving a drone that would fly to and upload a picture of each pallet location.

The drone part was left out of our scope, to be completed in another phase. My group focused on creating an interface and ML model to deduce whether a picture of a pallet was deemed risky or not.

To complete this project, I:

• Served as Point-of-Contact with company representatives to translate business needs into app features

• Developed React web app for interface

• Deployed PostgreSQL database on AWS RDS

• Created REST API endpoints with Node.js and Express

Lyric Learner parses artist lyrics via Genius' RESTful API in Python, interprets the lyrics and generates new lyrics via a Java program written within the Maven Framework, then uses Twitter's RESTful API to send out Tweets.

For Lyric Generation, the program utilizes Markov Chains to come up with a lyric based on all of the artist's past lyrics. A Markov Chain is a concept similar to predictive text on iMessage in which suggested words are chosen based on a weighted average of the words that most commonly follow it.

I used React to create the display for a bug tracking website and used Flask in Python to create REST API endpoints for creating or editing bug or user data.