Yikang Shen
Chief Executive Officer at Stealth AI Startup
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolYikang Shen is an AI researcher-turned-founder with 11 years of experience building and scaling large language models and NLP systems, now serving as CEO of a stealth AI startup in Mountain View. He held research roles at IBM (helping develop next‑generation LLMs), xAI, Tencent (co‑authoring the mixture of attention heads technique), and completed a PhD at Université de Montréal/Mila focused on deep learning and language understanding. His background blends rigorous academic research and hands‑on engineering—evidenced by contributions to the Theano project’s testing and test automation work—bringing attention to model internals and maintainability. Comfortable moving between research, product, and execution, he has repeatedly shipped model efficiency and editing innovations and now leads a team to translate those advances into real‑world AI products.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science Artificial Intelligence Machine Learning Deep Learning Computational Linguistic, Doctor of Philosophy (PhD) Computer Science Artificial Intelligence Machine Learning Deep Learning Computational Linguistic at Université de MontréalMaster of Science (MSc) Computer Science, Master of Science (MSc) Computer Science at Beihang University
Chinese, 法语
Github Skills (6)
unit-testing10
python10
theano10
testing10
numpy9
machine-learning8
Programming languages (2)
Github contributions (5)
Theano/Theano
Aug 2017 - Sep 2017
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:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:7 commits, 5 PRs, 9 comments in 1 month
Contributions summary:Yikang primarily contributed to testing efforts within the Theano project. Their work involved converting docstrings to comments, indicating a focus on code maintainability and testing structure. They also implemented and debugged tests for functionalities such as shared variable handling, function behavior, and random number generation, showcasing a deep understanding of the library's internal workings and testing procedures.
mathematical-expressionsmultidimensional-arrayspymcpythontheano
yikangshen/Ordered-Neurons
Jul 2018 - Aug 2019
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"
Contributions:20 commits, 2 PRs, 10 pushes in 1 year 1 month
recurrent-neural-networkstree-structure
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