ENGINEERING / PUBLIC REPOSITORIES
I test ideas by building the system.
I came into engineering through machine learning, then kept moving down the stack. When a project depends on a database, GPU backend, or simulator, I want to understand what it is doing and where it can fail.
Public GitHubSELECTED BUILDS
What I have been working on.
Rust · ROCm / HIP
candle-rocm
A ROCm/HIP backend for Hugging Face Candle, built below the model layer with device discovery, safe Rust wrappers, GPU kernels, and rocBLAS matrix multiplication.
- Custom HIP runtime and FFI crates
- Used as the AMD GPU backend in picochat
Rust · language-model training
picochat
A Rust framework for language-model training and inference. It brings tokenizer training, pretraining, fine-tuning, reinforcement learning, and interactive use into one workflow.
- candle-rocm integrated as a pinned GPU backend
- Training and inference designed to stay close to the system
MuJoCo · PPO / behavior cloning
G1 manipulation challenge
A pick-and-place submission for a Unitree G1 simulation. The work splits the task into environment design, a scripted teacher, behavior cloning, and PPO fine-tuning.
- Task-agnostic training framework
- Committed ONNX routine for headless evaluation
TypeScript · Bun · Cloudflare R2
GitForge
A multi-tenant Git hosting platform that supports Git Smart HTTP and Git LFS, with auditable controls around access, protected resources, and administrative activity.
- Tenant-isolated object storage
- Role-based access control and audit logging
C++23 · concurrent systems
ScalerDB
An in-memory database built to learn the parts that libraries normally hide: schema checks, primary-key indexing, persistence, read-write locking, and latency measurement.
- std::variant value system
- GoogleTest suites for data, persistence, and concurrency
R · model validation
Diabetes prediction
The model reached 1.00 AUC because the target was derived from fasting glucose while fasting glucose remained an input. That result was a leakage warning, not a model win.
- 10,000-row public dataset
- Limitation documented in the project README
R · group research
NFL career data project
A group analysis of quarterback statistics and career win percentage using Lasso and stepwise regression. The test results explained about 37–38% of the observed variation.
- Career game logs aggregated by starter
- Omitted team context stated as a limitation