Gang Zhao
Staff Research Scientist at Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolGang Zhao is a Staff Research Scientist in Palo Alto with eight years of experience bridging numerical methods for PDEs, parallel computing, and hydrologic modeling. He holds a PhD in Water Resources Engineering from Texas A&M and has applied advanced simulation and reservoir modules across academia and national labs, including Oak Ridge and the Carnegie Institution. As an active SciPy contributor, he has improved sparse linear algebra implementations (notably TFQMR and sparse-matrix features), reflecting a knack for turning research-grade algorithms into robust, usable code. Positive, energetic and goal-oriented, he combines deep domain expertise in climate and water-resource impacts with practical C/Python development skills and a track record of integrating models at scale.
8 years of coding experience
11 years of employment as a software developer
Bachelor of Science (B.Sc.), Environmental Science, Bachelor of Science (B.Sc.), Environmental Science at Nankai UniversityDoctor of Philosophy (Ph.D.), Water Resources Engineering, Doctor of Philosophy (Ph.D.), Water Resources Engineering at Texas A&M University
Chinese, English
Github Skills (7)
sparse-matrix10
algorithms10
scipy10
python10
linear-algebra10
scientific-computing10
numpy10
Programming languages (3)
Github contributions (5)
scipy/scipy
Jun 2021 - May 2022
SciPy library main repository
Role in this project:
Back-end Developer
Back-end DeveloperContributions:70 reviews, 25 commits, 63 PRs in 11 months
Contributions summary:Gang primarily contributed to the `scipy/scipy` repository, specifically within the sparse linear algebra module. Their work focused on enhancing existing algorithms such as TFQMR, and making the code consistent, including normalization of type descriptions, adding a TFQMR algorithm, and fixing issues. Additionally, they introduced a feature for sparse matrices and addressed issues related to initial guess handling. Their contributions indicate a focus on improving the functionality, correctness, and usability of the sparse linear algebra tools within SciPy.
scipypythonscientific-computing
zhaog6/scipy
Mar 2022 - Jun 2022
SciPy library main repository
Contributions:19 pushes, 25 branches in 3 months
pythonsvdscikit-learnscipyanomalydetection
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.