Xing Niu
Senior Applied Scientist at Amazon Web Services (AWS)
New York City Metropolitan Area United States
Summary
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Rockstar🎓
Top SchoolXing Niu is a Senior Applied Scientist based in the New York City area with 11 years of experience researching multimodal generative and agentic AI. Currently at AWS, he blends deep academic training—a PhD in Computer Science—with hands-on engineering to move research ideas into scalable systems. His open-source contributions include meaningful improvements to the widely used Sockeye sequence-to-sequence NMT framework, where he optimized training schedules and added utilities for pretrained embedding initialization. Comfortable across research and production, he focuses on practical model engineering that improves training stability and parameter management. Colleagues describe him as someone who bridges theory and implementation, often spotting subtle training signals that lead to measurable gains. He brings a global academic pedigree from Southeast University, Shanghai Jiao Tong, and the University of Maryland to industry-scale ML problems.
11 years of coding experience
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Southeast UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of MarylandMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Shanghai Jiao Tong University
Stackoverflow
1reputation
0reached
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Github Skills (11)
sequence-to-sequence10
pytorch10
machine-translation10
deeplearning-ai10
deep-learning10
neural-machine-translation10
python10
machine-learning9
transformer9
data-structures8
data-structure8
Programming languages (6)
Github contributions (5)
awslabs/sockeye
Jun 2017 - Nov 2018
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:8 reviews, 7 commits, 11 PRs in 1 year 4 months
Contributions summary:Xing contributed to the Sockeye project, a sequence-to-sequence framework for neural machine translation. Their work included debugging and improving training processes by distinguishing criteria for reducing the learning rate, and adding and updating parameter extraction tools, along with supporting utilities to initialize embedding weights with pre-trained representations. These changes likely focused on enhancing the training process and supporting model development and parameter management within the machine translation framework. Additionally, there were general code improvements with bug fixes and minor modifications.
sequencetranslationsockeyeneural-machine-translationseq2seq
xingniu/multitask-ft-fsmt
May 2018 - Nov 2019
Contributions:9 commits, 8 pushes, 1 branch in 1 year 6 months
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