Lijun Xue
CEO & Founder at LUDENS AI
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolLijun Xue is a CEO and founder with 12 years of hands-on experience building AI systems, currently leading Ludens AI from Palo Alto after founding and scaling FITURE’s AI organization. He combines deep research roots (Master’s from Carnegie Mellon) with product-grade engineering at companies like DJI and Tesla, delivering real-time edge computer vision, distributed training, and supply-chain ML systems. Lijun has a strong open-source pedigree—contributions to Theano (now PyTensor) improving tensor ops and memory usage—which underscores his focus on core ML infrastructure as well as production reliability. Notably, he has repeatedly turned prototype research into shipped products, from mobile object tracking on DJI gimbals to an edge-based fitness motion engine.
12 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BS) Electrical and Electronics Engineering, Bachelor of Science (BS) Electrical and Electronics Engineering at South China University of TechnologyMaster's degree Software Engineering, Master's degree Software Engineering at Carnegie Mellon University
Chinese, English, Japanese
Github Skills (8)
tensorrt10
tensor10
tensorflow10
python10
operation10
theano10
numpy9
testing8
Programming languages (16)
Github contributions (5)
Theano/Theano
Aug 2013 - Apr 2015
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Role in this project:
Back-end Developer
Back-end DeveloperContributions:216 commits, 8 PRs, 16 comments in 1 year 8 months
Contributions summary:Lijun contributed to the Theano library by implementing and updating tensor variable interfaces, specifically focusing on the addition of `cumprod` and `cumsum` functionalities. They made several updates to the `var.py` file, modifying imports and function calls related to tensor operations, showing a focus on improving the core functionality of the library. The user also added tests for these new methods, demonstrating a commitment to code quality and the correct implementation of the new features.
python-librarymathmulti-dimensionalpythonevaluate
royxue/MobileAnalysis
Nov 2014 - Jan 2015
Contributions:52 commits, 6 issues in 2 months
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