Liang Pang
Research Assistant at Institute of Computing Technology, Chinese Academy of Sciences
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolLiang Pang is a research-focused software engineer with 12 years of experience in machine learning and backend development, currently a Research Assistant at the Institute of Computing Technology, Chinese Academy of Sciences. He holds a PhD in Computer Science and has contributed to notable open-source ML toolkits—adding core deep text-matching layers to the MatchZoo framework and enhancing algorithms and memory management in the Shogun toolbox. Liang combines rigorous academic research with pragmatic engineering, evidenced by unit-tested contributions and rank-evaluation metrics that extend library functionality. His background includes internships at Microsoft Research Asia and industry experience at Baidu, reflecting an ability to move between cutting-edge research and production environments. An understated strength is his focus on robust, testable implementations that improve both algorithmic capability and software reliability.
12 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Institute of Computing Technology Chinese Academy of SciencesBachelor of Science (BS), Computer Software Engineering, Bachelor of Science (BS), Computer Software Engineering at Huazhong University of Science and Technologycoursera
Github Skills (22)
algorithm10
algorithms10
c-language10
python10
machine-learning10
data-structure10
keras10
deep-learning10
natural-language-processing10
neural-network10
data-structures10
cprogramming-language10
text-matching10
unit-testing9
hypothesis-testing9
Programming languages (5)
Github contributions (5)
NTMC-Community/MatchZoo
Jun 2017 - Mar 2019
Facilitating the design, comparison and sharing of deep text matching models.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:138 commits, 46 PRs, 92 pushes in 1 year 9 months
Contributions summary:Liang implemented new layers for the MatchZoo framework, specifically for deep text matching models. These additions included the `Match` layer, which computes a matching matrix, and the `MatchTensor` layer, which extends the matching capabilities. The commits also included the addition of new tests for these layers, including modifications to the test environment and rank evaluation metrics. The user's work is directly related to the core functionality and expansion of the MatchZoo library for deep learning-based text matching.
text-matchingmatchingdeep-learningneural-networknatural-language-processing
shogun-toolbox/shogun
Mar 2014 - Apr 2014
Shōgun
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:44 commits in 1 month
Contributions summary:Liang contributed to the Shogun machine learning toolbox by implementing and fixing several components related to hypothesis testing and machine learning algorithms. Their commits included the addition of a new class, `CIndexFeatures`, and unit tests for the LibLinear classifier and Lars regression, indicating a focus on expanding the library's capabilities. Furthermore, the user was involved in addressing memory leak issues and modifying the Gaussian Likelihood class, showcasing a strong understanding of the codebase.
cmakedata-sciencegunc-plus-plusmachine-learning
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