Computer Science + Economics @ Johns Hopkins University
Building scalable, creative, and sustainable software solutions in healthcare, accessibility, and real-world problems.
Built a cross-platform analytics pipeline using Python, SQL, REST APIs, and PostgreSQL to process 18K+ daily events and power real-time product insights. Designed ETL workflows and CI/CD infrastructure reducing reporting latency by 90% across 20+ KPIs.
Developed multimodal ML pipelines analyzing 4.8M+ customer reviews and 22K+ businesses. Built recommendation models using CLIP embeddings and optimized a PyTorch inference pipeline for large-scale product personalization.
Designed HPC workflows using Python, Bash, and SLURM to classify 500K+ DNA sequences. Built feature-engineered models achieving 98.6% accuracy and 99% precision; research presented at the 2024 CT JSHS STEM Poster Exhibition.
AI-enabled reflective platform analyzing emotional patterns from text and video journaling. Built with React, Flask, and Gemini with real-time analytics dashboards.
AI-powered WhatsApp assistant using Flask, Twilio, and Mixtral-8x7B to track medication adherence, detect side effects, and escalate severe symptoms to clinicians.