Welcome!
About Me
I’m currently a Computer Science and Applied Statistics student at The University of Virginia, working on full‑stack web apps and Machine Learning projects.
In my spare time, I enjoy personal fitness and helping others learn about exercise.
Experience
Amazon Web Services (AWS)
Software Development Engineer Intern
May 2026 – August 2026 · Herndon, VA
Built a price intelligence system and an event-driven pricing pipeline for internal metering teams.
- Designed and built a value insights system surfacing $15M in actionable price intelligence daily, replacing manual spreadsheets with an on-demand dashboard and cutting manual record pricing by 99% for internal metering teams
- Architected a distributed pricing application in Java hosted on AWS Fargate, leveraging an event-driven pipeline to process 7,000 billing records per minute with 99.94% file success rate
- Engineered data ingestion to handle 5M writes per hour to InfluxDB, optimizing batch writes and time-series indexing to serve sub-second time-range queries to React.js (TypeScript) dashboards
Mythics
Software Engineer Intern
May 2025 – August 2025 · Virginia Beach, VA
Led a team of 5–10 building a full-stack client prospecting tool with React.js, Express.js and Oracle ADW.
- Led a cross-functional team of 5–10 through development of a full-stack client prospecting tool surfacing 20+ high-quality business leads and $1M in generated business pipelines per use
- Built interactive data visualization components in React.js with modular state management, rendering client metrics across filterable lead scoring views, reducing lead evaluation time by 40% across 3 internal teams
- Architected data ingestion and enrichment with REST API endpoints leveraging Express.js and Oracle ADW to fetch 50,000+ records in sub 600ms latency
UVA School of Data Science
Machine Learning Researcher
April 2025 – Present · Charlottesville, VA
Training a CLIP-based model that matches economic time-series charts with natural-language descriptions.
- Trained a CLIP-based multimodal retrieval model using PyTorch, aligning time-series chart embeddings with natural language descriptions across 3,000+ Federal Reserve economic blog posts
- Built scalable preprocessing pipelines in Pandas and NumPy, transforming raw economic time-series records into structured contrastive learning pairs, reducing data preparation time by 95%
- Optimized model training through systematic data quality filtering and time-series alignment, removing 30+ insufficient data pairs and improving validation recall convergence by 45% across encoder configuration experiments
Skills
Languages
- Java
- C
- Python
- JavaScript/TypeScript
- R
- SQL
- HTML
- CSS
- MATLAB
Frameworks & Tools
- React.js
- Next.js
- Node.js
- TailwindCSS
- NumPy
- PyTorch
- Pandas
- REST APIs
- OOP
- Mockito
- Git
- Agile
Certifications
- 2025 Oracle Cloud Infrastructure (OCI) Foundations
- 2025 Oracle AI Foundations Associate
- CITI Researcher
Featured Projects
Recent highlights from my work
PromptLite
React.js, TailwindCSS, Vite, Chrome API
Built a Chrome Extension using React.js, TailwindCSS, and Vite, reducing user ChatGPT token waste by 30% while visualizing users’ sustainable AI usage patterns
Learn More →Recipe Macro Tracker
Expo, React.js, Flask, Google Vision OCR, Amazon AWS
Engineered a full-stack macronutrient tracking application using React.js, Flask, and Expo, supporting cross-platform deployment for web and mobile clients
Learn More →