Summary
Allen Cheung is a Deep Learning Engineer based in the San Francisco Bay Area with eight years of hands-on experience building and deploying computer vision and ML systems from edge devices to server-scale inference. He holds an MS in Computer Science from UCLA and has driven performance and throughput improvements—optimizing YOLO models, TensorRT, and Triton deployments to boost mAP and real-time FPS in drone and defense pipelines. His background spans research and product work, including NLP bias analysis for text-to-image models, 2D Transformer models for molecular identification, and 2D-3D fusion for autonomous driving explainability. Comfortable across the stack, he has shipped ARM64 and Jetson edge solutions, orchestrated Docker/Triton pipelines, and contributed to dataset annotation and tooling; an uncommon strength is his combination of model-level algorithmic innovation with low-level inference optimizations for production environments.
8 years of coding experience
3 years of employment as a software developer
University of California, San Diego
University of California, Los Angeles