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Selected projects.
Product and engineering work across mobile, AI, and full-stack systems.
ChatSprout 2.0 – LLM-powered Scenario-based English Training Assistant
- Background: Designed an AI-assisted training system to help non-native English professionals improve communication in networking, meetings, and workplace conversations with real-time feedback.
- Solution: Built an expression-improvement assistant on top of an LLM with a RAG (retrieval-augmented generation) architecture to understand user inputs and generate context-aware suggestions.
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Core product design:
- Decomposed workplace social-communication scenarios (e.g., disagreeing, giving feedback, meeting updates) into a taxonomy and interaction flow; iterated the UX with feedback from 10+ users so people can get targeted suggestions via natural-language input.
- Designed the RAG pipeline end-to-end: user input → embedding → similar-scenario retrieval → LLM-generated rewrites and suggestions.
- Added a low-similarity fallback mechanism to reduce hallucination risk and stabilize generations.
- Improved retrieval strategy to reach ~80% Top-3 scenario match accuracy.
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Evaluation:
- Defined metrics for output quality (accuracy, tone appropriateness), latency, and token cost.
- Optimized prompts and API call strategy to keep average response time at 1.5–2.0 seconds.
- Key insight: In open-ended language practice, retrieval quality impacts generation quality more than model size.
Research Capstone: Reproducible Dataset of Obfuscated Android Malware
Python, Docker
- Built a fault-tolerant distributed data pipeline to process and enrich 12,000+ obfuscated Android malware samples.
- Designed containerized processing workflows using Docker for scalable batch execution and reproducible experimentation.
- Implemented concurrent ingestion and task orchestration to improve large-scale processing efficiency.
- Integrated LLM-based validation pipelines to analyze detection outputs and evaluate agent-based workflows.
Community Assistant
React, Node.js, PostgreSQL, Redis, Docker, AWS
- Built a containerized full-stack platform for community request and volunteer appointment management with JWT authentication and 18+ RESTful API endpoints.
- Implemented a Redis caching layer with 60s TTL and write-through invalidation to reduce redundant database queries and expose real-time cache metrics.
- Dockerized services with Docker Compose, deployed to AWS EC2, and automated testing and deployment with GitHub Actions CI/CD.