Shaobo Guan
Senier Machine Learning Engineer at Apple
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShaobo Guan is a senior machine learning engineer based in Sunnyvale with a decade of experience applying research-grade computer vision and generative modeling to production problems. He holds a PhD in computational and cognitive neuroscience and an MSc in computer science from Brown University, blending deep scientific rigor with practical ML engineering. At Apple he moved from applied research to senior engineering roles, shipping models informed by his prior work on recurrent and bidirectional visual processing. His open-source tl-GAN work—highlighted in a popular Medium post and demonstrated via controlled GAN latent-space editing—shows a knack for interpretable generative models and tooling for dataset ingestion and preprocessing. Colleagues rely on him to bridge neuroscience insights and scalable ML pipelines, and he often surfaces practical algorithmic intuitions about how recurrent processing and latent disentanglement improve robustness in messy real-world data.
10 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD, Systems/Computational Neuroscience, Doctor of Philosophy - PhD, Systems/Computational Neuroscience at Brown UniversityBachelor's degree, Biotechnology, Bachelor's degree, Biotechnology at Shanghai Jiao Tong University
Stackoverflow
1reputation
0reached
3answers
0questions
Github Skills (15)
data-preprocessing10
data-ingestion10
keras10
machine-learning10
cgan10
tensorflow10
cyclegan10
python10
dcgan10
image-processing9
computer-vision9
pandas8
graph6
tensorboard6
math6
Programming languages (4)
Github contributions (5)
SummitKwan/transparent_latent_gan
Sep 2018 - Oct 2018
Use supervised learning to illuminate the latent space of GAN for controlled generation and edit
Role in this project:
Data Scientist
Data ScientistContributions:52 commits, 11 PRs, 14 pushes in 1 month
Contributions summary:Shaobo's contributions primarily focus on data ingestion, preprocessing, and model training within the context of a GAN project. They implemented scripts to download and process various datasets, including IAM-top50, a transient attribute scene dataset, CelebA, CIFAR, and MNIST. The user also developed and tested a vanilla GAN model for image generation and explored the latent space of a pre-trained PGGAN model. Furthermore, they built a CNN model to predict face attributes and used it to analyze and manipulate the synthetic images generated by the GAN.
pytorchsupervised-learningilluminatecontrolledsupervised
SummitKwan/CS242_project_generative_neura_network
Dec 2016 - Jul 2018
Contributions:4 commits, 3 pushes, 1 branch in 1 year 6 months
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