Dr. Awni Hannun
Staff Machine Learning Researcher
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Awni Hannun. Every entry undergoes editorial source verification.
Deep Speech: Scaling up End-to-End Speech Recognition
First author of the landmark Baidu Deep Speech paper, proving that replacing complex phonetic acoustic pipelines with a single recurrent neural network trained end-to-end with CTC achieves state-of-the-art accuracy in noisy real-world speech.
End-to-end recurrent models without attention mechanisms struggle to maintain alignment over multi-minute audio streams.
MLX: An Efficient Machine Learning Framework for Apple Silicon
Conceived, engineered, and open-sourced MLX, a NumPy-like array framework with composable function transformations (grad, vmap) that exploits Apple Silicon's unified memory architecture for zero-copy CPU/GPU execution.
Exclusively optimized for Apple Silicon hardware; non-macOS platforms are not natively accelerated.