CS @ UC Irvine · AI & ML · Information Retrieval · Systems
Email · LinkedIn · Portfolio · GitHub
- CS undergrad at UC Irvine focused on machine learning, information retrieval, and distributed systems
- Interested in ranking/search, evaluation metrics, and building reliable, scalable infrastructure
- Actively building a portfolio geared toward research‑oriented SWE / ML roles and future graduate study
Languages
- Python · C++ · JavaScript/TypeScript · Java · SQL
Core Areas
- Machine Learning & MLOps
- Information Retrieval & search systems
- Backend & distributed systems
- Systems design, performance, and observability
Tools & Frameworks
- PyTorch, scikit‑learn, JAX (if applicable)
- Node.js, Express, React/Next.js
- PostgreSQL, Redis
- Docker, Linux, Git, GitHub Actions, Vercel, AWS (where relevant)
Developed a search engine from scratch, indexing 50,000+ documents with an inverted index and on-disk partial indexing (3-phase merge) to keep memory usage bounded while maintaining query latency under 300 ms without external libraries.
Implemented core IR techniques including TF-IDF ranking, Boolean retrieval, Porter stemming, and tokenization, and boosted relevance using PageRank, HITS, and anchor-text indexing for more precise ranking.
Built a GPU-accelerated terminal achieving 460 FPS on large text screens, reducing frame draw time to 2 ms and freeing over 80% of CPU resources for parallel workloads using GLAD, GLFW, and FreeType (UNIX/Linux).
Delivered roughly 50% faster text output on datasets exceeding 700K lines, with smoother scrolling, lower input latency, and reduced system load compared to CPU-rendered terminals.
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Research Assistant – UC Irvine
Worked on problems at the intersection of ML and systems; contributed to experimental design, implementation, and analysis. -
Tech Director – Student Organization
Led the redesign of the ticketing and workflow system; owned architecture, performance tuning, and developer experience. -
Software Engineering Intern – IIITDMJ
Contributed to full‑stack features, debugging, and code quality improvements.
- Deepening knowledge of IR (ranking, evaluation, learning‑to‑rank) and advanced ML
- Practicing system design and low‑level performance work
- Exploring topics and projects that can mature into research or publication‑worthy work
- Open to: research collaborations, infra/ML‑heavy SWE internships, and IR / search‑focused projects
- Best ways to reach me: email or LinkedIn (links above)
If you liked any of my work, a ⭐ on the corresponding repo is always appreciated!