An open-source embedded framework I'm putting together to push an LLM through microcontroller firmware tasks. An agent can build and test firmware end-to-end in Docker while the human validates on real hardware. Write-once library code runs across 3 MCUs and shared peripherals (I2C displays, NFC, IMU, GPS, magnetometer, ToF range sensors). Everything runs behind a Docker-only CI/CD pipeline: host-side pytest against stubbed MicroPython modules, held to a 90% coverage gate. MIT-licensed.

A slide deck on LLMs I'm writing which is being peer-reviewed by colleagues and working researchers. Covers information theory and tokenization, works through how these models are trained (RL post-training, chain-of-thought), and ends on system integration: agent control loops and RAG.

A web platform I built from scratch as a testbed for the latest open-weight vision and language models, so I can integrate, fine-tune, and benchmark them against my own datasets and workflows as they release. The LLM side is a from-scratch RAG documentation assistant: self-hosted open-weight models served off my own GPUs, a FAISS vector index, multi-pass query rewriting, and reciprocal-rank-fusion retrieval, with generation constrained to the retrieved sources so every answer carries citations. Serving uses key-value caching and a WebSocket job dispatcher that routes queries to on-demand GPU workers which support scale-to-zero or long-live serving. The vision side covers object detection, SAM-3 instance segmentation, and CLIP embedding and clustering across distributed GPU workers, feeding annotation interfaces with SAM-assisted and model-assisted pre-labeling. Datasets, ontologies, and per-image annotations are all versioned with full revision history.

Pipelines combining VLMs, CNNs, in-context vision learning, and classical algorithms to auto-label supervised vision datasets from sparse or partial annotations (Florence2, CLIP, SAM, PEFT-tuned ViTs). Propagated labels route through human review before they land in a dataset.

For my university capstone project, I developed a multi-dimensional depth sensor by combining computer vision with a single-dimension lidar sensor. The final system incorporated a wooden jig, a pre-trained YOLO model, a 4K Logitech webcam, and an RPlidar A1 sensor.

Our research explored explanation-informed acceptance testing of deep adaptive programs. I designed explanation-rich scenarios, managed a graduate-student team, and coordinated and ran human-subject studies. The following is a video of the game I designed for our research, a simplified version of Starcraft 2. Inspired by the popular mod "Nexus Wars", this experimental domain features an adversarial multi-agent dynamic, discrete action space, continuous state space, and a partially observable environment.

An automated intra-day arbitrage trading bot: a timeseries classifier placing live NYSE orders through the Schwab API.