Shuai Wang
Tenure-Track Associate Professor
Suzhou City, Jiangsu, China
Summary
👤
Senior🎓
Top SchoolShuai Wang is a tenure-track associate professor and researcher with 11 years of experience specializing in speaker recognition and diarization, holding a PhD from Shanghai Jiao Tong University. He transitioned from industry research at Tencent—where he led speech AI efforts for games—to roles at the Shenzhen Research Institute of Big Data and academia, now also serving as an adjunct at CUHK Shenzhen. With 40+ publications in top speech venues (INTERSPEECH, ICASSP, TASLP) and wins in challenges like VoxCeleb and DIHARD, he combines strong empirical results with production-focused engineering. His open-source contributions to the widely used wenet-e2e/wespeaker toolkit show practical expertise in PyTorch model improvements, ONNX export, and diarization pipelines, bridging research and deployable systems. Based in Suzhou, he uniquely pairs competitive-challenge success with hands-on deployment experience across industry and academia.
11 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Bachelor's degree at Northwestern Polytechnical UniversityDoctor of Philosophy - PhD, Speaker Recognition/ Diarization, Doctor of Philosophy - PhD, Speaker Recognition/ Diarization at Shanghai Jiao Tong University
Github Skills (13)
pytorch10
machine-learning10
data-export10
exports10
onnx10
resnet10
exporter10
python9
inference9
data-structure7
data-structures7
algorithm7
algorithms7
Programming languages (4)
Github contributions (5)
wenet-e2e/wespeaker
Sep 2021 - Nov 2022
Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:21 reviews, 16 commits, 35 PRs in 1 year 1 month
Contributions summary:Shuai's contributions primarily revolve around the development and modification of machine learning models within the context of speaker recognition and verification. The user implemented code formatting and added pooling options to the ResNet model, demonstrating familiarity with model architecture and PyTorch. Further contributions include the addition of ONNX export capabilities and inference code, extending the model's deployment options. Additionally, the user updated the diarization recipe and included changes to the plda codes demonstrating integration of speaker embeddings and model training pipelines.
pythonproduction-readyecapa-tdnnspeaker-verificationproduction
wsstriving/starter-academic
Sep 2020 - Jul 2024
📝 Easily create a beautiful website using Hugo, GitHub, and Netlify
Contributions:64 pushes in 3 years 10 months
hugohugo-themestatic-site-generatornetlify
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