Yinhao Zhu
Staff Engineer at Qualcomm
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolYinhao Zhu is a Staff Engineer in San Diego with a decade of experience specializing in 3D computer vision, differentiable rendering, and foundation models for spatial and temporal scene understanding. At Qualcomm he drives research-to-product work in 3D reconstruction, Gaussian Splatting, 4D world models, and autonomous driving, building systems that bridge NeRF-like representations and real-time perception. His academic background (PhD in Electrical Engineering) and graduate research in physics-informed ML, surrogate modeling, and uncertainty quantification give him a strong foundation in principled modeling and probabilistic reasoning uncommon in many applied CV teams. He has also applied sequence-to-sequence deep learning in genomics during industry internships, showing a knack for cross-domain adaptation of methods. Colleagues rely on him to translate cutting-edge research into scalable implementations for XR, compression, and perception pipelines.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Science - BS, Automatic Control, Bachelor of Science - BS, Automatic Control at Tongji UniversityDoctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at University of Notre DameControl Science and Engineering, Control Science and Engineering at Harbin Institute of Technology
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (47)
uncertainty8
computational-biology8
decoder8
surrogate8
robot8
bayesian-deep-learning8
physics8
encoder-decoder8
biomechanics8
simulator8
kinematics8
encoder7
fourier-transform7
robotics7
reinforcement-learning-environments7
Programming languages (3)
Github contributions (5)
cics-nd/pde-surrogate
May 2019 - Oct 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Contributions:3 commits, 1 PR, 2 pushes in 4 months
physicsdeep-learninguncertainty-quantificationdimensionallabeled-data
cics-nd/cnn-surrogate
Apr 2018 - May 2018
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Contributions:4 commits, 2 pushes, 1 branch in 23 days
convolutionaldecoderuncertainty-quantificationdensity-estimationencoder
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