Projects

Multimodal meeting copilot that watches shared screens, listens to audio, powers contextual chat, runs fact-check workflows, and generates final summaries. Built at the Multimodal Frontier Hackathon in San Francisco and awarded Best Use of Railtracks.

Retrieval-Augmented Generation system built for EECS coursework and research applications.

arXiv preprint — retrieval-augmented PCF estimation from the Carbon Catalogue plus tunable post-hoc re-ranking over BPR, NeuMF, and LightGCN on Amazon Reviews; λ sweeps trace engagement–carbon Pareto frontiers by category.

Solo project — deep research agent: layered search and extraction, live knowledge graph while the run is active, Learning trail for strategy shifts, and markdown reports. Competed in HackwithBay 2.0 (Deep Agents / RSAC); placed among the top 20 teams at the finale at AWS Builders Loft, San Francisco. Stack includes Next.js, FastAPI, Claude, Tavily, Bedrock, Aerospike, and Airbyte.

Open-source benchmark harness for comparing LLM inference performance across providers and configurations. Latest vLLM finding (single-worker local setup, Qwen2-0.5B-Instruct): higher concurrency mostly increased queueing latency rather than throughput; doubling max_tokens roughly doubled latency while tokens/sec stayed similar, suggesting generation rate as the bottleneck.

Got tired of every PR review tool requiring account sign-ups and complex setup, so I built my own. Drop-in GitHub Action that runs LLM-powered code review with structured, inline comments — severity, category, confidence, and suggested fixes. Supports single-pass and multi-pass modes (correctness, security, performance). Also works as a standalone CLI. Published on the GitHub Marketplace.

Built an automated LLM-based testing agent to fuzz and validate AI-generated web applications.

Multi-stage LLM agent pipeline to parse freelance job ads, assess feasibility/margins, generate bids, execution plans, and autonomously produce deliverables.

MuZero reinforcement learning agent for Snake. Full training pipeline including MCTS, neural network policy/value estimation, and evaluation framework.

ML models to predict maritime vessel trajectories using historical AIS data. Feature engineering pipeline and evaluation across multiple modeling approaches.