Fangde Liu
Chief Executive Officer at SurgicalAI.cn
Hangzhou City, Zhejiang, China
Summary
🤩
Rockstar🎓
Top SchoolFangde Liu is a CEO and research-trained technologist with a decade of experience at the intersection of data science, robotics, computer graphics and surgical innovation. He led image-guided and autonomous surgical navigation systems at Imperial College and now manages Optimise-MS, a multinational big-data pharmacovigilance platform underpinning neurology registries across several European countries. Equally comfortable in research and product roles, he combines GPU-accelerated computing, medical image informatics and database engineering—evidenced by contributions to TensorLayer’s MongoDB interface—to bridge clinical needs and deployable software. A UK exceptional talent awardee and Royal Society of Medicine associate, he regularly consults with top industry labs (IBM Watson, Huawei, Samsung) on clinical AI translation. Based in Hangzhou, he brings a rare blend of surgical-domain insight, hands-on embedded/robotics engineering and executive leadership.
10 years of coding experience
9 years of employment as a software developer
Msc, Electrical Engineer, A, Msc, Electrical Engineer, A at Harbin Institute of TechnologyPhD, Computer Animation, PhD, Computer Animation at Bournemouth University
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
mongodb10
python10
database-design10
mongodb-database10
pymongo10
documentation9
nosql9
deeplearning-ai9
deep-learning9
tensorflow8
Programming languages (3)
Github contributions (5)
tensorlayer/TensorLayer
May 2017 - Sep 2019
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:14 commits, 10 PRs, 3 pushes in 2 years 4 months
Contributions summary:Fangde's commits primarily focus on the implementation and documentation of a database interface for the TensorLayer project. They added documentation and code examples related to MongoDB, including installation instructions and connection setup using the PyMongo client. They also worked on a database interface, demonstrating a solid understanding of database design and integration within the larger deep learning framework. The user's efforts improved the data management capabilities of the project.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
fangde/TensorLab
Jul 2017 - Jul 2018
Cloud Machine Learning Platform based on TensorLayer
Contributions:26 commits, 2 PRs, 31 pushes in 11 months
machine-learning-platformmxnetlearning-platformtensorlayercaffe2
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