I am an undergraduate student at Sun Yat-Sen University, majoring in Computer Science and Technology, minoring in Mathematics and Applied Mathematics.
I also work as a Research Intern at Stony Brook University, where I am extremely fortunate to be advised by Prof. Chenyu You .
My research interests lie in multi-modal machine learning, AI for health, optimization. My broader interests include computer vision and generative models.
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Sun Yat-Sen UniversityB.Eng. in Computer Science and TechnologySep. 2023 - present -
Sun Yat-Sen UniversityMinor in Mathematics and Applied MathematicsSep. 2021 - present
Experience
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Stony Brook UniversityResearch InternJun. 2025 - present -
Sun Yat-Sen UniversityResearch InternOct. 2024 - present
Selected Publications (view all )
Together, Then Apart: Revisiting Multimodal Survival Analysis via a Min-Max Perspective
Wenjing Liu, Qin Ren, Wen Zhang, Yuewei Lin, Chenyu You
arXiv preprint 2025
Revisit multi-modal survival analysis via the dual lens of alignment and distinctiveness. Introduce Together-Then-Apart (TTA), a unified min-max optimization framework that simultaneously models shared and modality-specific representations.
Together, Then Apart: Revisiting Multimodal Survival Analysis via a Min-Max Perspective
Wenjing Liu, Qin Ren, Wen Zhang, Yuewei Lin, Chenyu You
arXiv preprint 2025
Revisit multi-modal survival analysis via the dual lens of alignment and distinctiveness. Introduce Together-Then-Apart (TTA), a unified min-max optimization framework that simultaneously models shared and modality-specific representations.
Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
Wen Zhang, Qin Ren, Wenjing Liu, Haibin Ling, Chenyu You
arXiv preprint 2025
Introduce SPROUT, a fully training- and annotation-free prompting framework for nuclear instance segmentation. SPROUT leverages histology-informed priors to construct slide-specific reference prototypes that mitigate domain gaps.
Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
Wen Zhang, Qin Ren, Wenjing Liu, Haibin Ling, Chenyu You
arXiv preprint 2025
Introduce SPROUT, a fully training- and annotation-free prompting framework for nuclear instance segmentation. SPROUT leverages histology-informed priors to construct slide-specific reference prototypes that mitigate domain gaps.