Jingqing Zhang
Technical Co-founder And Head Of AI at PangaeaData.AI
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJingqing Zhang is a technical co-founder and Head of AI with 11 years of experience building and leading machine learning research teams, currently focused on clinical NLP at PangaeaData.AI. He holds a PhD in Computer Science from Imperial College London and brings prior research experience from Google Brain, where he contributed to PEGASUS, a state-of-the-art pretraining model for abstractive summarization. His engineering work spans both research and production: active contributions to the TensorLayer deep learning library show hands-on skill improving neural network layers and weight initialization for real-world model training. Jingqing combines rigorous academic training with startup grit, exploring practical NLP solutions for low-resource clinical settings while hiring and scaling his team. He’s equally comfortable iterating on core ML abstractions and translating them into domain-specific clinical applications.
11 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Imperial College LondonBachelor’s Degree, Computer Science and Technology, GPA: 91/100, Bachelor’s Degree, Computer Science and Technology, GPA: 91/100 at Tsinghua University
Github Skills (12)
neural-network10
model-building10
machine-learning10
deep-learning10
tensorflow10
python10
dropout9
initializr9
batch-normalization9
initialize9
initializer9
initialization9
Programming languages (4)
Github contributions (5)
tensorlayer/TensorLayer
Nov 2016 - Jun 2020
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:225 commits, 23 PRs, 36 pushes in 3 years 7 months
Contributions summary:Jingqing's commits focused on enhancing the tensorlayer library, particularly the neural network layers. Contributions included adding initializer support for beta and gamma in batch normalization, updating example links, modifying base layer structures, and supporting weight initialization in Dense layers. The user demonstrated experience in deep learning by working on different layer types, including the base layer, dropout, inputs, and the Dense layer, and adding functionality to improve model building and training workflows.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
JingqingZ/KG4ZeroShotText
Apr 2018 - Mar 2020
Source code of the paper 'Integrating Semantic Knowledge to Tackle Zero-shot Text Classification. NAACL-HLT 2019. '
Contributions:77 commits, 2 PRs, 14 pushes in 1 year 11 months
nlpnaaclmeta-learningzerobert
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