0→1 applied scientist and researcher, with a background spanning edge ML hardware (FPGA-based inference devices) through modern LLM/RL systems. 26 US patents and applications.
Facilitates the Reinforcement Learning and Reasoning Models tracks for a Data Science & Machine Learning collaborative learning group, working through Sutton & Barto (SAB), the Farama stack (Gymnasium, PettingZoo), and Raschka's Reasoning Models from Scratch.
| nvidia-rag | RAG over 59,016 pages of NVIDIA documentation. Compares vector stores (Chroma, FAISS), embeddings (ada-002, all-mpnet-base-v2) and chunk sizes; RAGAS evaluation with a self-hosted judge; 8-bit and GGUF quantization for local inference. |
| hf-llm-course | HuggingFace LLM course material — tokenization, fine-tuning, causal LM pre-training, SFT and post-training. |
| rl-gym | RL fundamentals — Blackjack (Monte Carlo vs TD), CartPole, a custom GridWorld environment with a Gymnasium wrapper, and Taxi-v3 action masking. |
Interested in reasoning, evaluation, and the gap between research code and production systems.

