Chengyin Li
PhD Student, Wayne State University
cyli [AT] wayne.edu
Bio
I am currently a fifth-year Ph.D. student under the supervision of Prof. Dongxiao Zhu in the Trustworthy AI Lab, in the Department of Computer Science at Wayne State University. I received my bachelor's degree from Nanjing University of Science and Technology, and a master's degree from University of Chinese Academy of Sciences. My research mainly focuses on Medical Image Applications, Trustworthy AI, and Visual Foundation Models (VFMs). I have successfully contributed to multiple AI research papers accepted for publication in conference venues and journals, including MICCAI, ECML, IJCAI, NeurIPS, Medical Physics, and Pediatric Research. Additionally, I have been actively engaged in research at Henry Ford Health System, primarily emphasizing enhancing the performance and robustness of medical image segmentation. I am on the job market now!
Skills
- Programming: Python (Numpy, Pandas), C++, Java, PyTorch, TensorFlow, HuggingFace, MONAI, ...
- Machine Learning & AI: Medical Imaging, Computer Vision, Visual Foundation Models, Natural Language Processing, Unsupervised/Self-supervised Learning, Explainability, Fairness, Robustness
- Tools: SQL, Matlab, R, MS Office, LATEX, Git, Google AI Platform, Amazon Web Services (AWS)
- Languages: Mandarin (native), English (business fluent)
Publications
Most recent publications on Google Scholar.
‡ indicates equal contribution.
FocalUNETR: A Focal Transformer for Boundary-aware Prostate Segmentation using CT Images
Chengyin Li, Yao Qiang, Rafi Lbn Sultan, Hassan Bagher-Ebadian, Prashant Khanduri, Indrin J. Chetty, and Dongxiao Zhu
MICCAI 2023
A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images
Chengyin Li, Hassan Bagher-Ebadian, Rafi Ibn Sultan, Mohamed Elshaikh, Benjamin Movsas, Dongxiao Zhu, and Indrin J. Chetty
Medical Physics
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang, Rafi Ibn Sultan, Indrin Chetty, and Dongxiao Zhu
Arxiv
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Chengyin Li, Zheng Dong, Fisher Nathan, and Dongxiao Zhu
ECML PKDD 2022: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Putting the “mi” in Omics: Discovering miRNA Biomarkers for Pediatric Precision Care
Chengyin Li, Rhea E. Sullivan, Dongxiao Zhu, and Steven D. Hicks
Pediatric Research
Saliency Guided Adversarial Training for Tackling Generalization Gap with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li, Sijia Liu, and Dongxiao Zhu
AdvML-Frontiers 2022: New Frontiers in Adversarial Machine Learning Workshop at ICML
COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray
Xin Li, Chengyin Li, and Dongxiao Zhu
BIBM 2020: IEEE International Conference on Bioinformatics and Biomedicine
FocalUNETR: A Focal Transformer for Boundary-aware Prostate Segmentation using CT Images
Chengyin Li, Yao Qiang, Rafi Lbn Sultan, Hassan Bagher-Ebadian, Prashant Khanduri, Indrin J. Chetty, and Dongxiao Zhu
MICCAI 2023
A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images
Chengyin Li, Hassan Bagher-Ebadian, Rafi Ibn Sultan, Mohamed Elshaikh, Benjamin Movsas, Dongxiao Zhu, and Indrin J. Chetty
Medical Physics
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang, Rafi Ibn Sultan, Indrin Chetty, and Dongxiao Zhu
Arxiv
Negative Flux Aggregation to Estimate Feature Attributions
Xin Li, Deng Pan, Chengyin Li, Yao Qiang, and Dongxiao Zhu
IJCAI 2023: International Joint Conference on Artificial Intelligence
Proximal Composite Optimization for Distributionally Robust Learning
Prashant Khanduri, Chengyin Li, Rafi Ibn Sultan, Yao Qiang, Joerg Kliewer, and Dongxiao Zhu
AdvML-Frontiers 2023: New Frontiers in Adversarial Machine Learning Workshop at ICML
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Chengyin Li, Zheng Dong, Fisher Nathan, and Dongxiao Zhu
ECML PKDD 2022: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Putting the “mi” in Omics: Discovering miRNA Biomarkers for Pediatric Precision Care
Chengyin Li, Rhea E. Sullivan, Dongxiao Zhu, and Steven D. Hicks
Pediatric Research
Counterfactual Interpolation Augmentation (CIA): A Unifed Approach to Enhance Fairness and Explainability of DNN
Yao Qiang, Chengyin Li, Marco Brocanelli, and Dongxiao Zhu
IJCAI-ECAI 2022: International Joint Conference on Artificial Intelligence
AttCAT: Explaining Transformers via Attentive Class Activation Tokens
Yao Qiang, Deng Pan, Chengyin Li, Xin Li, Rhongho Jang, and Dongxiao Zhu
NeurIPS 2022: Conference on Neural Information Processing Systems
Saliency Guided Adversarial Training for Tackling Generalization Gap with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li, Sijia Liu, and Dongxiao Zhu
AdvML-Frontiers 2022: New Frontiers in Adversarial Machine Learning Workshop at ICML
COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray
Xin Li, Chengyin Li, and Dongxiao Zhu
BIBM 2020: IEEE International Conference on Bioinformatics and Biomedicine
CV
Full Resume in PDF.
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Wayne State University 2019 - nowPh.D. Student
Department of Computer Science -
University of Chinese Academy of Sciences 2013 - 2016Master
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Nanjing University of Science and Technology 2009 - 2013Bachelor
Acknowledgement
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