William Song
BIM Architect at Cedarglen Homes
Calgary, Alberta, Canada
Summary
👤
Senior🎓
Top SchoolWilliam Song is a BIM Architect with 8+ years applying software-driven BIM solutions using Autodesk Revit and Autodesk Platform Services to streamline building workflows from Calgary. He brings a strong research pedigree—Ph.D. in Mechanical Engineering and postdoctoral work in production management, G-code generation for ultra-fast laser micromachining, and STEP/CATIA integration—which informs a systems-minded approach to digital construction. William blends practical Revit implementation with software development skills, contributing machine-learning work on a Chinese poetry generation project that highlights his curiosity for probabilistic modeling and data pipeline improvements. Known for translating complex CAD/PLM interoperability problems into automated tools, he excels at turning interdisciplinary research into production-ready BIM processes. Colleagues rely on him to bridge engineering theory, automation, and hands-on BIM execution to deliver measurable productivity gains.
9 years of coding experience
B.Sc., Mechanical Engineering and Automation, B.Sc., Mechanical Engineering and Automation at Xi'an Jiaotong University
Stackoverflow
91reputation
14kreached
1answer
5questions
Github Skills (22)
python10
rnn-model10
n10
tensorflow10
poet9
lstm9
poe9
nlp8
data-processing8
natural-language-processing8
machine-learning7
jupyter-notebook6
macos6
dynamic-programming6
homebrew6
Programming languages (20)
Github contributions (5)
lucasjinreal/tensorflow_poems
Jun 2018 - Feb 2019
中文古诗自动作诗机器人，x炸天，基于tensorflow1.10 api，正在积极维护升级中，快star，保持更新！
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 3 PRs in 8 months
Contributions summary:William primarily contributed to the development and maintenance of a Chinese poetry generation model. Their work involved modifying the core logic of the poetry generation process, including how words are selected and integrated within the poem. The user also updated the data processing pipeline, added utility functions for cleaning and preparing the textual data, and modified the training process. These changes suggest a focus on improving the model's performance and data handling.
apirnnpythontensorflow1lstm
Freakwill/ell
Jun 2021 - Aug 2025
Contributions:1 release, 22 pushes, 1 branch in 4 years 2 months
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