the lab
Machine Learning
Local AI systems, RAG pipelines, AutoML, and applied ML research.
Download CV ↓AI / ML
- RAG pipelines
- FAISS
- SentenceTransformers
- llama-cpp-python
- AutoGluon
- Streamlit
- GGUF quantization
Languages
- Python
- Rust
Tools & Other
- Git
- Docker
- Google Sheets API
I build AI systems that run entirely locally — no cloud, no API keys, no data leaving your machine. My AI-Companion project is a modular local assistant with long-term semantic memory: RAG pipeline using FAISS and SentenceTransformers, running quantized GGUF models via llama-cpp-python.
I care deeply about privacy-preserving AI. The idea that every interaction needs to go through a third-party API feels wrong when modern hardware can run capable models on-device. I've spent significant time on GGUF quantization, memory-efficient retrieval, and making local inference actually practical.
I also built auto-ml, a Streamlit app wrapping AutoGluon to make ML model training painless — upload a dataset, pick a target, let it run. It's the kind of tool I wish existed when I started with ML.
currently exploring
Local LLM inference optimization, semantic memory systems, and making on-device AI genuinely useful for real workflows.
relevant work