From cardiac research to
full-stack AI engineering
Started in biomedical research, now I build software and ship products.
3Degrees
4.0GPA
70+Visualizations Built
2Products Shipped
Education
MS in Computer Science
Artificial Intelligence
Georgia Institute of Technology
GPA: 4.00/4.00Expected 2026
MSE in Biomedical Engineering
Biotechnology and Systems Biology
University of Michigan
GPA: 4.00/4.002023
BSE in Biomedical Engineering
Biotechnology and Pharmaceutical Engineering
University of Michigan
GPA: 3.96/4.002022
Skills & Technologies
Languages
AI / ML
Frontend
Tools & Platforms
Backend & Infrastructure
Experience
Software Engineer
Mintallxyz, Inc. · San Francisco, CA
- ·Architect and ship an autonomous browser-testing agent on Cloudflare Workers: headless Browser Run with multimodal paths (structured UX evaluations, assertions) and Playwright MCP for visual review
- ·Build and own Mintall Studio, a canvas-based AI design application: tldraw editor, workspace UI, session persistence
- ·Architect and implement the backend AI generation system: provider-abstracted genai package, Cloudflare Workers, text/image generation
- ·Lead framework migrations: React 19, Tailwind v4, 14+ shadcn/ui components, React Query state overhaul
- ·Extract shared packages into a monorepo architecture with Clerk auth across multiple apps
Software Engineer (Contract)
Curious Cardinals · San Francisco, CA
- ·Sole engineer alongside a COO and Product Manager, building FlightPlan from an empty repo to a launched product that raised $100K within 48 hours
- ·Designed and built 10+ multi-stage LLM pipelines with structured output, durable execution via Inngest, and real-time SSE streaming
- ·Architected the data layer: Supabase/PostgreSQL with Drizzle ORM, pgvector semantic search, nightly cron-based data refresh
- ·Built the mobile-first Next.js frontend: D3 visualizations, React Query, scroll-snap navigation across all product surfaces
Life Science Research Professional
Stanford Medicine · Palo Alto, CA
- ·Built custom Python and R pipelines to automate cardiac research data collection, increasing efficiency by 150%
- ·Created 70+ visualizations from RNAseq/ATACseq, Luminex, and flow cytometry data
- ·Imaged hundreds of cardiac tissue samples across species using TUNEL, CODEX, H&E, and Masson's Trichrome staining