Summary
Shuyu Qin is a Deep Learning Engineer and PhD candidate in Computer Science at Lehigh University with eight years of research and engineering experience building novel generative and representation models for scientific and networked data. Currently at AT&T, she develops Network Foundation Models for multimodal representation learning, translating advanced research into scalable models for complex network datasets. Her prior work includes a 20% improvement over state-of-the-art on 4D-STEM image domain mapping via a Cycle-Consistent-Spatial-Transforming Autoencoder and shipping pip-installable packages (Auto4DSTEM, Deep Matter) that package custom VAEs, transformers, and training utilities for reproducible science. She has built specialized architectures—custom transformers, LSTM-β-VAEs, and optimized loss functions—for denoising, domain transfer, and interpretable latent spaces across hyperspectral and microscopy data. Based in Bethlehem, PA, Shuyu blends deep theoretical understanding with practical software delivery, often turning supervised problems into unsupervised solutions to unlock new application avenues.
8 years of coding experience
6 years of employment as a software developer
Bachelor's degree Physics, Bachelor's degree Physics at Nanjing University
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Lehigh University
Master's degree Mechanical Engineering, Master's degree Mechanical Engineering at Washington University in St. Louis
Chinese, English