Master's Student in Computer Science @ UC Davis
Specializing in ML Security, Data Pipeline Engineering & Production Systems
Right now I'm deep in the weeds of ML security, curating datasets for LLM-based secret detection training pipelines. I built an offer letter generation system that's actively used by the UC Davis CS department (solo project!), and I'm constantly experimenting with VectorDB's semantic search capabilities through local demos. When I'm not knee-deep in data pipelines, I'm building vote-chain web applications with ResilientDB as the backend database.
🎯 Where I am: Learning by building • Academic projects in active use • Growing through real challenges
🎨 Beyond code: Sketching, cooking, and lost in good novels fuel my creative problem-solving.
interface CurrentFocus {
primaryWork: string;
activeProjects: string[];
learningMode: string;
impact: string;
}
const rightNow: CurrentFocus = {
primaryWork: "Building ML systems that actually get used in production",
activeProjects: [
"UC Davis offer generation system (88% time savings, 200+ tests)",
"ML security pipelines with 10K+ curated datasets",
"VectorDB semantic search prototypes for real applications"
],
learningMode: "Hands-on experimentation + academic rigor + production deployment",
impact: "Systems in active use, not just demos"
};
Actively seeking: ML Engineer / Data Pipeline Engineer / Product Engineer roles
Looking for teams that build ML-powered products people actually use. I want to optimize models for real-world performance, create seamless user experiences, and have fun solving problems that matter.
Target: AI-first startups, product teams with ML at the core, or companies scaling intelligent systems.
Goal: Build ML products that are both powerful and delightful. Ready to integrate cutting-edge models into systems that users love.
Building real projects, learning through doing, growing every day