• Developing Java Spring APIs that integrate with AWS Serverless architecture for processing quality control data on large sets of genetic tests.
• Creating Node.js Lambdas and Step Functions to asynchronously process real-time lab data and support the companyʼs most expansive testing suites.
• Implemented Amazonʼs ElastiCache for Redis in multiple applications, cutting API response time by up to 90% and decreasing memory usage similarly.
• Worked with Product teams to design and build features that are currently in use by thousands of providers.
• Administered, supported, and updated the agencyʼs Java CMS application. Managed Linux servers through command line, shell, and Python scripting.
• Created a Java application to replace a COBOL database. Designed a visual interface that allowed for reporting and easy access to decades worth of data. Used Spring Core, Security, and Boot. Developed with Eclipse, Maven, and JUnit testing.
• Programmed Python applications to automate searching tasks for investigators and decreased their time spent on these activities from multiple hours to a few minutes.
• Developed Java applications with Oracle backends to replace failing Access-based apps. Dramatically increased uptime, reliability, and ease of use for all users.
• Used Python to create programs to automate workflows that previously took hours of manual work.
• Created and managed queries for SumoLogic dashboard.
• Managed and conducted internal and external network scans using Qualys.
• Configured and administered hundreds of employee phones using AirWatch.
Programmed and designed an iOS application using Swift and Firebase for chronic pain patients to track their pain and communicate with doctors.
• Created a Java backend API for hosting user data.
• Released private beta to over 30 users and made changes based on feedback.
• Released full version of application to Apple App Store.
• Finalist team in Zahn Innovation Center Entrepreneurial Competition.
Used machine learning and natural language processing to determine the mood and topic of Tweets, creating a classifier that correctly predicted each 87% and 90%, respectively. The team’s research was published as part of Pace University’s Student-Faculty Research Day on May 5th, 2017.
Used natural language processing techniques to dynamically create a thesaurus of cybersecurity words based on the Tweets of experts in the field. Our team parsed over 40,000 posts to find recurrences, remove irrelevant words, and create a pertinent corpus.
Worked with a partner to produce an application for users to catalog and share the movies they’ve seen with friends. Used Swift, Firebase, and Cocoapods to create the application and backend. Published on the Apple App Store.