Liming Huang
Senior Software Engineer at Google
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolLiming Huang is a Senior Software Engineer based in Tokyo with 10 years of experience building scalable, ML-enabled systems at Google and prior internships across Microsoft, Facebook, Baidu and MSR. He blends production backend engineering with distributed machine learning experience—his open-source contributions include adding a matrix table to Microsoft's Multiverso parameter-server framework, improving data partitioning and Get/Add semantics for large-scale training. With a master's focus on search engines and web mining from Peking University, he has hands-on experience applying TensorFlow in advertising systems and developing query understanding and chatbot features. Colleagues would describe him as endlessly curious and pragmatic, often turning research prototypes into reliable, test-covered components in production.
10 years of coding experience
1 year of employment as a software developer
Master’s Degree, Search engine and web mining, Master’s Degree, Search engine and web mining at Peking University
Chinese, English, hokkien, Japanese
Github Skills (6)
data-structures10
c-language10
cprogramming-language10
data-structure10
machine-learning9
distributed-systems9
Programming languages (3)
Github contributions (5)
microsoft/Multiverso
Mar 2016 - Aug 2016
Parameter server framework for distributed machine learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:86 commits, 6 PRs, 9 pushes in 5 months
Contributions summary:Liming's commits primarily focus on the implementation of a matrix table within the parameter server framework. They added the matrix table and implemented various methods, including Get, Add, and Partition, to handle data storage and retrieval. The changes also include adding a test function to the main program, which demonstrates the use of matrix table. Overall, the user's work enhanced the framework with a new data structure for distributed machine learning.
parameter-servermachine-learningparameterserver-frameworkdistributed-machine-learning
liming-vie/RUBER
May 2017 - Jul 2019
Implementation of RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Contributions:5 commits, 1 push, 6 comments in 2 years 2 months
dialog-systemsdialogautomatic-evaluationopen-domainunsupervised
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