Senior Research Scientist at OPPO

I am a Senior Research Scientist at the OPPO US Research Center, where I develop AI algorithms for wearable digital health. My current work focuses on physiological signal modeling using wrist PPG and gyroscope-derived BCG, with applications to hypertension risk screening, cuffless blood pressure estimation, and real-world health monitoring. I design compact ResNet and Transformer models, develop robust validation strategies, and collaborate with cross-functional teams to translate research into smartwatch products.

I received my Ph.D. from the iVPL Lab at Northwestern University, supervised by Prof. Aggelos Katsaggelos and Prof. Daniel Kim. My doctoral research focused on AI for medical image analysis, including cardiac and abdominal organ segmentation, cross-modality domain adaptation, multimodal learning, and high-resolution disease localization. I developed deep learning methods based on Transformers, GANs, U-Net, and YOLO for robust analysis across heterogeneous imaging modalities.

More broadly, my research interests lie at the intersection of digital health, physiological sensing, medical imaging, and trustworthy machine learning. I am particularly interested in developing clinically meaningful AI systems that remain robust across subjects, devices, acquisition settings, and real-world distribution shifts.

My work has resulted in 14 first-author publications, 1,200+ citations, and multiple Top-5 rankings in international medical AI challenges, including MICCAI and ISBI.

Previously, I received an M.S. degree in Mechanical Engineering from Huazhong University of Science and Technology in 2019, supervised by Prof. Bin Li and Prof. Xinggang Wang, and a B.S. degree in Materials Processing and Control Engineering from Qiming College, HUST, in 2016.

Short resume

news

Apr 15, 2026	Our paper Gyroformer: Capturing 3D rotational BCG dynamics for cuffless hypertension risk screening has been accepted by EMBC 2026 !🎉 Stay tuned for more updates!
Sep 14, 2025	Our paper Drl-Stnet⁺⁺ Uncertainty-Aware Self-Training For Cross-Modality Segmentation With Generative Translation has been accepted by ICIP workshop 2026 !🎉 Stay tuned for more updates!
May 1, 2025	I am starting a full-time position at the OPPO US Research Center, focusing on wrist PPG analysis for reliable hypertension risk screening.
Feb 24, 2025	I am currently working as a Machine Learning Engineer intern at the OPPO US Research Center, contributing to the design of recommendation systems.
Dec 20, 2024	My work ( paper ) during the summer internship 2025 on analyzing wrist-collected PPG data for continuous hypertension risk screening has been accepted for presentation at ICASSP 2025 ! 🎉 Stay tuned for more updates!

selected publications

2025

ICASSP

Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning

Hui Lin, Jiyang Li, Ramy Hussein, and 6 more authors

ICASSP 2025, 2025

Bib

@article{lin2024longitudinal,
  title = {Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning},
  author = {Lin, Hui and Li, Jiyang and Hussein, Ramy and Sui, Xin and Li, Xiaoyu and Zhu, Guangpu and Katsaggelos, Aggelos K and Zeng, Zijing and Li, Yelei},
  journal = {ICASSP 2025},
  volume = {12},
  year = {2025},
}

2024

Usformer: A small network for left atrium segmentation of 3D LGE MRI

Hui Lin, Santiago López-Tapia, Florian Schiffers, and 8 more authors

Heliyon, 2024

Bib Slides

@article{lin2024usformer,
  title = {Usformer: A small network for left atrium segmentation of 3D LGE MRI},
  author = {Lin, Hui and L{\'o}pez-Tapia, Santiago and Schiffers, Florian and Wu, Yunan and Gunasekaran, Suvai and Hwang, Julia and Bishara, Dima and Kholmovski, Eugene and Elbaz, Mohammed and Passman, Rod S and others},
  journal = {Heliyon},
  volume = {10},
  number = {7},
  year = {2024},
  publisher = {Elsevier},
  video = {https://www.youtube.com/watch?v=4Mu5rgfUwoE},
  google_scholar_id = {QyVjEncAAAAJ}
}

2023

MICAAI Chall.

StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography

Hui Lin, Tom Liu, Aggelos Katsaggelos, and 1 more author

arXiv preprint arXiv:2310.14961, 2023

Bib

@article{lin2023stenunet,
  title = {StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography},
  author = {Lin, Hui and Liu, Tom and Katsaggelos, Aggelos and Kline, Adrienne},
  journal = {arXiv preprint arXiv:2310.14961},
  year = {2023},
}

2020

IEEE TASE

Defect image sample generation with GAN for improving defect recognition

Shuanlong Niu, Bin Li, Xinggang Wang, and 1 more author

IEEE Transactions on Automation Science and Engineering, 2020

Bib

@article{niu2020defect,
  title = {Defect image sample generation with GAN for improving defect recognition},
  author = {Niu, Shuanlong and Li, Bin and Wang, Xinggang and Lin, Hui},
  journal = {IEEE Transactions on Automation Science and Engineering},
  volume = {17},
  number = {3},
  pages = {1611--1622},
  year = {2020},
  publisher = {IEEE},
  dimensions = {true},
}

2019

J. Intell. Manuf.

Automated defect inspection of LED chip using deep convolutional neural network

Hui Lin, Bin Li, Xinggang Wang, and 2 more authors

Journal of Intelligent Manufacturing, 2019

Bib

@article{lin2019automated,
  title = {Automated defect inspection of LED chip using deep convolutional neural network},
  author = {Lin, Hui and Li, Bin and Wang, Xinggang and Shu, Yufeng and Niu, Shuanglong},
  journal = {Journal of Intelligent Manufacturing},
  volume = {30},
  pages = {2525--2534},
  year = {2019},
  publisher = {Springer},
  dimensions = {true},
}