Yongchun Zheng
Scientist at National Astronomical Observatories, Chinese Academy of Sciences
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolYongchun Zheng is a scientist with nine years of focused experience in lunar and planetary science, contributing to China’s Chang'E missions through data processing, geologic mapping, and strategic exploration planning. Based at the National Astronomical Observatories (NAOC) in Beijing, he developed China’s first lunar simulant (CAS-1) and has led microwave data interpretation and studies of lunar surface thermal and dielectric properties. Trained as a geochemist and cosmochemist (PhD/MSc, Institute of Geochemistry, CAS), he blends laboratory petrophysics with remote-sensing analysis to translate mission data into geological insight. Beyond planetary science, he contributes ML and transfer-learning code on GitHub, indicating a practical cross-over into modern data-driven methods for remote sensing and domain adaptation. His profile reflects a rare mix of hands-on instrumentation/simulant development and advanced data-science tooling applied to lunar exploration.
9 years of coding experience
4 years of employment as a software developer
doctor, geochemistry and cosmochemistry, doctor, geochemistry and cosmochemistry at Institute of geochemistry, CASMaster, geochemistry, Master, geochemistry at Institute of Geochemistry, CASBachelor, environment science, Bachelor, environment science at Southwest Agricultural University
Github Skills (9)
dd10
pytorch10
transfer-learning10
deep-learning10
resnet10
adaptation10
python10
machine-learning9
unsupervised-learning8
Programming languages (1)
Github contributions (5)
jindongwang/transferlearning
Jul 2018 - Oct 2021
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 15 pushes, 10 comments in 3 years 4 months
Contributions summary:Yongchun contributed code related to transfer learning, domain adaptation, and related techniques. Their commits involved implementing and integrating various deep learning models, including ResNet variants, with frameworks like PyTorch. They added code for algorithms such as DAN, DeepCoral, DDC, RevGrad, DSAN, and MRAN, showcasing their focus on model implementation and experimentation in the field of transfer learning.
meta-learningdomain-adaptionrepresentation-learningself-supervised-learningfew-shot
easezyc/buildcsv
Nov 2016 - Dec 2016
Contributions:7 commits, 5 pushes, 1 branch in 1 month
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