Quan Wang
Senior Staff Software Engineer & Tech Lead Manager at Google DeepMind
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolQuan Wang is a Senior Staff Software Engineer and Tech Lead Manager based in New York, currently leading Gemini Audio work in Google DeepMind with seven years of industry experience at Google and prior ML roles at Amazon. He blends research-grade expertise in speaker identification, diarization, and speech/vision ML with pragmatic engineering—contributing to projects like google/uis-rnn where he improved test coverage and code quality for a well-cited diarization library. A PhD-trained researcher from Rensselaer with a B.Eng. from Tsinghua, he pairs deep technical rigor with hands-on delivery of production ML systems such as Amazon Firefly and Echo features. Beyond management, he authors technical textbooks and teaches on Udemy, signaling a strong commitment to knowledge sharing and developer enablement.
7 years of coding experience
14 years of employment as a software developer
Ph.D. Student Computer & Systems Engineering, Ph.D. Student Computer & Systems Engineering at Rensselaer Polytechnic InstituteB.Eng. Automation, B.Eng. Automation at Tsinghua University
Chinese, English
Github Skills (9)
unit-testing10
python10
evaluation9
machine-learning9
eval9
pytorch9
speaker-diarization8
clustering8
supervised-learning7
Programming languages (5)
Github contributions (5)
google/uis-rnn
Oct 2018 - Jul 2021
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
Role in this project:
ML Engineer & Test Automation Engineer
ML Engineer & Test Automation EngineerContributions:138 commits, 5 PRs, 124 pushes in 2 years 9 months
Contributions summary:Quan's contributions primarily involved adding Apache source code headers and writing unit tests for the project's Python files. They implemented tests for the `sequence_acc` function and the `sample_permuted_segments` function, ensuring the correctness of core utility functions. The user also refactored code, moving the `sequence_acc` function to a dedicated evaluation module and making formatting changes to the Python files. These changes indicate a focus on code quality, testing, and maintainability within the UIS-RNN project.
speaker-diarizationsupervised-learningsupervised-clusteringdiarizationrnn
wq2012/awesome-diarization
Jan 2019 - Dec 2022
Contributions:7 reviews, 87 commits, 31 PRs in 3 years 11 months
speaker-diarizationdiarizationdeep-learningspeakerspeech-recognition
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.