I'm a passionate developer creating beautiful digital experiences with modern technologies. I specialize in full-stack development, 3D graphics, and computer vision!
Professional Experience
Software Engineering Mentee
Engaged in weekly 1:1 mentorship with Google engineers, boosting algorithmic problem-solving speed by 30%. Gained hands-on experience with code reviews, scalable system design, and debugging practices used by Google SWE teams.
Production Engineering Fellow
Meta
Jun. 2025 - Sep. 2025Enhanced CI/CD pipelines for open-source services, improving deployment reliability and reducing build times by 20%. Improved observability with Prometheus and Grafana, cutting incident detection time by 30%.
Software Engineering Intern
IPG Health (90 North)
Jun. 2025 - Aug. 2025Designed a computer vision pipeline with the YouTube Data API and Gemini to extract motivators from commercials, driving a 15% lift in campaign ROI.
AI Studio Developer
Assurant
Aug. 2024 - Dec. 2024Developed a VSCode Copilot extension with GitHub REST API integration, reducing onboarding time by 70% and improving developer productivity by 40%.
Software Engineering Intern
Emerald Energy
May. 2024 - Jul. 2024Led a 4-member team developing a LangChain RAG chatbot replacing 200+ hardware manuals, reducing installation time by 40%.
Featured Projects
RGB vs LiDAR ASL Classifier
Open projectClassifier for common ASL gestures. We explored how LiDAR depth capture can improve classification accuracy. Using Intel RealSense cameras (L515 and D435), we generated point clouds of users’ poses and classified them to the closest matching ASL gesture.
RemindRx
Open projectRemindRx is a tool that helps users scan and interpret prescription labels, then generate reminders so they take their medications correctly and on schedule. Using phone or camera images of prescription labels, the system extracts the label text, classifies/structures the dosage instructions, computes optimal dosing times over the day, and sends reminders.
Theft Watch
Open projectHackHarvard 2024 Smart Cities First Place Winner. We used OpenCV and YOLOv8 to classify shoplifters into 'safe', 'suspicious', or 'dangerous'. Upon discovering a dangerous user, we send a text message via Twilio provide a detailed report as to what was stolen and description of suspect.
