Ruotian Luo
Software Engineer at Waymo
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolRuotian Luo is a software engineer and Ph.D. graduate from TTIC, currently building perception systems at Waymo with a decade of experience at the intersection of computer vision and language understanding. His research-to-production trajectory spans internships at Microsoft, Snap, and Adobe and long-form graduate work on multimodal AI, grounding his fluency in both novel models and robust engineering. He has contributed important low-level optimizations to object detection stacks—adding CUDA ROI pooling kernels and PyTorch adaptations used by practitioners in a widely referenced Faster R-CNN repo. Based in Austin, he combines academic rigor with hands-on SWE at scale, moving ideas from papers into vehicle perception pipelines. Colleagues describe him as someone who bridges deep research insight with pragmatic performance tuning, often surfacing non-obvious bottlenecks that materially improve accuracy and speed.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Toyota Technological Institute at ChicagoShanghai Experimental SchoolBachelor of Engineering (B.E.), IEEE Honor Class (Computer Science & Technology), Bachelor of Engineering (B.E.), IEEE Honor Class (Computer Science & Technology) at Shanghai Jiao Tong University
Chinese, English
Github Skills (7)
cuda10
object-detection10
computer-vision10
pytorch10
deep-learning9
mask-rcnn9
faster-rcnn9
Programming languages (13)
Github contributions (5)
ruotianluo/pytorch-faster-rcnn
Jul 2017 - May 2020
pytorch1.0 updated. Support cpu test and demo. (Use detectron2, it's a masterpiece)
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 76 commits, 11 PRs in 2 years 10 months
Contributions summary:Ruotian's contributions focused on implementing and debugging ROI pooling operations within the Faster R-CNN framework. They added CUDA kernels for forward and backward passes of the ROI pooling layer, which is crucial for object detection. These modifications were primarily related to adapting the existing code for PyTorch and optimizing the spatial pooling operations, potentially impacting the model's accuracy and performance.
pytorchcpuupdateddetectron2
ruotianluo/self-critical.pytorch
Oct 2018 - May 2020
Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.
Contributions:9 releases, 14 commits, 4 PRs in 1 year 6 months
pytorchsequencecriticaldeep-learningimage-captioning
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