Years after our college capstone, an out-of-the-blue email from European AI researchers wanting to reproduce our sign language paper sent me to Google Scholar, where we've been cited 11 times.
Looking back at my senior capstone project at Missouri State — building a real-time American Sign Language smart home controller with MediaPipe, custom CNNs, and networking — and how it sparked my path in machine learning.
The neural-cost v0.2.0 GPU benchmark moves from CPU roofline analysis to Apple Silicon's MPS GPU backend — measuring PyTorch and JAX across five architectures, four batch sizes, and both eager and compiled execution modes.
An empirical investigation comparing PyTorch 2.14, JAX 0.11, and TensorFlow across five model architectures and four batch sizes, measuring compiler speedups, kernel overheads, and hardware limits on Apple Silicon.
A framework-neutral Python library that estimates a neural network's FLOPs and tensor traffic, measures its runtime, and uses a roofline model to tell you exactly where performance is being left on the table.