Lam Tung is a Senior Machine Learning Engineer based in Hanoi with nine years of industry experience and a recent senior role at WorldQuant. He builds production-grade ML systems, from Vietnamese OCR and high-throughput facial recognition at Techainer to layout analysis and information extraction for enterprise document pipelines, consistently delivering state-of-the-art accuracy and real-time performance. Lam has deep hands-on expertise optimizing models for deployment—demonstrated by contributions to the pytorch/tensorrt project improving PyTorch-to-TensorRT integration and operator diagnostics. He combines research-driven methods (GANs for handwritten synthesis, instance segmentation for extraction) with pragmatic engineering—mentoring teams and shipping scalable inference pipelines that run at hundreds to thousands of FPS.
9 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Science, 3.17, Bachelor's degree, Computer Science, 3.17 at Hanoi University of Industry
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Role in this project:
ML Engineer
Contributions:1 review, 7 commits, 3 PRs in 11 months
Contributions summary:Lam contributed significantly to the `pytorch/tensorrt` repository by enhancing the integration of PyTorch models with TensorRT. They focused on improving the handling of unsupported operators by adding features to display corresponding PyTorch code. They refactored code for improved efficiency, including changing data structures used for tracking unsupported operators. Additionally, the user fixed links and updated documentation related to examples, tutorials, and repository references within the project.
Contributions:34 commits, 26 pushes, 1 branch in 1 year 7 months
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