Bei Jiang
Urban Designer at WRT/Solomon
Albany, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBei Jiang is an urban designer based in Albany, California with 13 years of professional experience blending design practice and technical fluency. Currently at WRT/Solomon, Bei focuses on shaping resilient, people-centered urban environments while bringing an analytical mindset informed by rigorous training at UC Berkeley. Beyond traditional urban design work, Bei has contributed to open-source machine learning tooling—improving gradient handling and logging in the BIDMach library—demonstrating a rare crossover of spatial design and computational optimization. This mix of disciplines enables Bei to approach projects with both creative vision and data-driven rigor, optimizing performance and user experience in built environments. Colleagues describe Bei as methodical, curious, and effective at translating complex technical insights into practical design improvements.
13 years of coding experience
University of California, Berkeley
Github Skills (5)
machine-learning10
optimization10
scala10
performance-optimization9
logging9
Programming languages (2)
Github contributions (5)
BIDData/BIDMach
Feb 2016 - May 2018
CPU and GPU-accelerated Machine Learning Library
Role in this project:
ML Engineer
ML EngineerContributions:101 commits, 1 PR, 84 pushes in 2 years 3 months
Contributions summary:Bei contributed significantly to the `Grad.scala` file within the `bidmach/bidmach` repository. Their changes involved implementing features such as clipping by value and adding support for `max_grad_norm`, suggesting a focus on improving gradient handling and optimization strategies within the machine learning library. The addition of a logging component and fixes related to it indicate a focus on enhancing the monitoring and debugging capabilities of the library's model training and evaluation processes. These contributions collectively suggest an effort to enhance the stability, performance, and usability of the machine learning functionalities.
cudacpudeep-learninggpuaccelerated
byeah/byeah.github.com
Nov 2012 - Feb 2021
Contributions:59 commits, 29 pushes in 8 years 4 months
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