I am an incoming Ph.D. student at Columbia University, where I will join the Laboratory of AI & Biomedical Science (LABS) under the supervision of Prof. Junhao (Hao) Wen. I completed my master's studies at Xiamen University under the supervision of Prof. Rongshan Yu.
My research interests lie at AI for science, with a particular focus on foundation models, generative AI, and multimodal learning for biomedical applications. My long-term goal is to build intelligent computational models that advance our understanding of human health and disease.
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Columbia UniversityPh.D. Student2026.09 - -
Xiamen UniversityM.S. Student2023.09 - 2026.06 -
South China Normal UniversityB.S. Student2019.09 - 2023.06
Experience
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Shanghai AI LABResearch Intern2024.05 - 2026.02
Honors & Awards
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Outstanding Master's Thesis Award, Xiamen University2026
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South China Normal University Merit Student Scholarship (four times)2019-2023
News
Selected Publications (view all )
BioMTAN: A Biological Knowledge-Guided Multi-Task Attention Network for Co-Enhanced Cancer Diagnosis and Prognosis
Ying Chen*, Jiajing Xie*, Yuxiang Lin, Yuhang Song, Wenxian Yang, Rongshan Yu# (* equal contribution, # corresponding author)
IEEE Journal of Biomedical and Health Informatics (JBHI) 2026
BioMTAN integrates biological pathway knowledge with multi-task attention to jointly predict cancer molecular subtypes and survival risk from gene expression data.
BioMTAN: A Biological Knowledge-Guided Multi-Task Attention Network for Co-Enhanced Cancer Diagnosis and Prognosis
Ying Chen*, Jiajing Xie*, Yuxiang Lin, Yuhang Song, Wenxian Yang, Rongshan Yu# (* equal contribution, # corresponding author)
IEEE Journal of Biomedical and Health Informatics (JBHI) 2026
BioMTAN integrates biological pathway knowledge with multi-task attention to jointly predict cancer molecular subtypes and survival risk from gene expression data.
SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction
Ying Chen, Jiajing Xie, Yuxiang Lin, Yuhang Song, Chen Zhang, Wenxian Yang, Rongshan Yu# (# corresponding author)
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2025
SurvMamba introduces Mamba-based hierarchical intra-modal and inter-modal interaction modules to integrate whole-slide images and transcriptomic data for efficient cancer survival prediction.
SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction
Ying Chen, Jiajing Xie, Yuxiang Lin, Yuhang Song, Chen Zhang, Wenxian Yang, Rongshan Yu# (# corresponding author)
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2025
SurvMamba introduces Mamba-based hierarchical intra-modal and inter-modal interaction modules to integrate whole-slide images and transcriptomic data for efficient cancer survival prediction.
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Ying Chen*, Guoan Wang*, Yuanfeng Ji*#, Yanjun Li, Jin Ye, Tianbin Li, Ming Hu, Rongshan Yu, Yu Qiao, Junjun He# (* equal contribution, # corresponding author)
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025
SlideChat is an open-source vision-language assistant for gigapixel whole-slide pathology images, built with SlideInstruction and evaluated on SlideBench across captioning and VQA tasks.
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Ying Chen*, Guoan Wang*, Yuanfeng Ji*#, Yanjun Li, Jin Ye, Tianbin Li, Ming Hu, Rongshan Yu, Yu Qiao, Junjun He# (* equal contribution, # corresponding author)
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025
SlideChat is an open-source vision-language assistant for gigapixel whole-slide pathology images, built with SlideInstruction and evaluated on SlideBench across captioning and VQA tasks.
RAFNet: Restricted Attention Fusion Network for Sleep Apnea Detection
Ying Chen*, Huijun Yue*, Ruifeng Zou, Wenbin Lei, Wenjun Ma, Xiaomao Fan# (* equal contribution, # corresponding author)
Neural Networks 2023
RAFNet detects sleep apnea from single-lead ECG by using restricted attention to fuse target and adjacent ECG segments while suppressing redundant neighboring information.
RAFNet: Restricted Attention Fusion Network for Sleep Apnea Detection
Ying Chen*, Huijun Yue*, Ruifeng Zou, Wenbin Lei, Wenjun Ma, Xiaomao Fan# (* equal contribution, # corresponding author)
Neural Networks 2023
RAFNet detects sleep apnea from single-lead ECG by using restricted attention to fuse target and adjacent ECG segments while suppressing redundant neighboring information.