Zeyi Wang
Co-founder CTO at Astrus
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolZeyi Wang is a CTO and co-founder with 11 years of experience building production AI systems, currently leading Astrus to automate analog microchip layout using ML. He blends deep research—having studied under Prof. Martin Müller at University of Alberta and worked on AlphaGo–style RL and search—with hands-on engineering that produced fast, deployable infrastructures at startups and at HiSilicon. His background spans robotics fleet control, neural text-matching engines (notably contributions to MatchZoo), and LLVM optimization research where he developed a Genetic Monte Carlo Tree Search that yielded tangible runtime gains. Comfortable across research, product and systems, he thrives at the intersection of novel algorithms and pragmatic engineering, and is known for bringing both rigorous experimentation and a playful streak to high-stakes technical challenges.
11 years of coding experience
4 years of employment as a software developer
Master's degree, Master's degree at University of AlbertaBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at University of California, DavisShenzhen Senior Highschool
Chinese, English
Github Skills (5)
python10
text-matching10
natural-language-processing9
architecture9
architectures9
Programming languages (19)
Github contributions (5)
NTMC-Community/MatchZoo
Jan 2018 - Jun 2021
Facilitating the design, comparison and sharing of deep text matching models.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 1 review, 400 commits in 3 years 5 months
Contributions summary:Zeyi's commits primarily focused on defining, renaming, and implementing various task types within the `matchzoo/engine` module. They made changes to the `base_task.py`, `tasks.py` and `binary_classification.py` files, indicating a focus on the core functionality of the text matching models. These changes include defining base classes, task types, and updating the import structure. The modifications made show that the user has a good understanding of the library's architecture and their role in processing text matching models.
pytorchnlptext-matchingdeep-learningmatching
uduse/Sage
Jun 2016 - Jul 2020
Contributions:1 PR, 8 pushes, 2 branches in 4 years 1 month
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