Rui H
Senior Research Engineer at Google DeepMind
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolRui H is a Senior Research Engineer with 11 years of experience building and shipping state-of-the-art machine learning systems for multimodal generative AI, currently working on large video and music models at Google DeepMind. He has driven production and research efforts across Google and Apple—shipping Vision Pro features like Optic ID and contributing to Gemini and Veo—while maintaining a strong research foothold from an AI Residency and CMU graduate work. His open-source contributions include implementing optimized CUDA kernels for 2D/3D sparse convolutions in the prominent google-research repository and authoring TensorFlow3D components used in 3D scene understanding research. Rui blends algorithm design, large-scale data curation, and systems-level optimization, with a track record of improving 3D detection and real-time on-device perception. He’s comfortable moving ideas from ECCV/NeurIPS papers to product deployment, and often bridges the gap between research prototypes and production constraints in multimodal modeling.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Engineering (B.Eng.) Computer Science, Bachelor of Engineering (B.Eng.) Computer Science at China University of GeosciencesMaster of Science (M.S.) Electrical and Computer Engineering, Master of Science (M.S.) Electrical and Computer Engineering at Carnegie Mellon University
Chinese, English
Stackoverflow
74reputation
5kreached
1answer
6questions
Github Skills (14)
cuda10
gpu-programming10
machine-learning10
tensorflow10
ai10
python9
neural-network6
objective-c6
computer-vision6
glut6
ios6
deep-learning6
swift6
opengl6
Programming languages (4)
Github contributions (5)
google-research/google-research
Nov 2020 - Jan 2021
Google Research
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 1 PR, 18 comments in 1 month
Contributions summary:Rui contributed CUDA code for 2D/3D sparse convolution operations. This involved implementing kernels for submanifold sparse convolutions and optimizing them with shared memory caching strategies. Furthermore, the user added utility functions related to sparse voxel networks, including pooling and index calculations, indicating a focus on developing components for 3D deep learning models. These contributions suggest expertise in implementing and optimizing sparse convolutional layers for machine learning research.
googlemachine-learningai
HRLTY/TP-GAN
Jul 2019 - Apr 2022
Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"
Contributions:2 commits, 5 pushes, 1 branch in 2 years 9 months
tensorflowcomputer-visionface-recognitiongenerative-adversarial-networksynthesis
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