Xiang Zhang
Chief GenAI Scientist at China Premium Smart Appliances Innovation Center
Qingdao, Shandong, China
Summary
👤
Senior🎓
Top SchoolXiang Zhang is a Chief GenAI Scientist and researcher with 13 years of experience building generative models across industry and startups, currently applying GenAI to industrial control in Qingdao. He holds a PhD from NYU and has driven research at Google and Clarifai on generative modeling for text, time series, anomaly detection, and LLM solutions for enterprise. As a founder and co-founder he launched ventures focused on decentralized model serving and single-persona dialogue agents, blending product instincts with deep research. His open-source work includes building character-level convolutional networks for text classification and pragmatic tooling for data conversion and training infrastructure. Known for bridging rigorous probabilistic modeling (uncertainty in text generation) with production-ready ML engineering, he excels at turning research prototypes into deployed systems. Fluent in both academia and real-world deployment, he brings an unusual combination of generative world-modeling depth and practical startup execution.
13 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at New York UniversityBachelor of Engineering (B.E.) Computer Science and Technology, Bachelor of Engineering (B.E.) Computer Science and Technology at Tianjin University
Chinese, English, French
Github Skills (9)
neural-network10
lua10
pytorch10
machine-learning10
convolutional-neural-networks10
text-classification10
data-conversion9
data-preprocessing9
configuration-management8
Programming languages (6)
Github contributions (5)
zhangxiangxiao/Crepe
Mar 2015 - Jul 2019
Character-level Convolutional Networks for Text Classification
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 32 commits, 1 PR in 4 years 4 months
Contributions summary:Xiang primarily contributed to the development and refinement of a character-level convolutional neural network for text classification. Their work included creating a tool for converting CSV data to a custom binary format, and implementing the training program, including configurations for the model, data loading, training loop, and evaluation. The user also addressed bugs in the data processing and quantization, and updated the training configurations.
nlpconvolutionalconvolutional-networkstext-classificationcharacter
thunder-nyc/Thunder
Aug 2014 - Mar 2019
Contributions:1 release, 450 commits, 80 pushes in 4 years 7 months
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