我是北京邮电大学软件工程专业的本科生，预计2027年毕业。
我的研究方向集中于 AI 药物发现、精准医疗、医疗健康多模态学习 以及 单细胞与组学数据分析。目前主要从事生成对抗网络在空间转录组学分析中的应用，以及对比学习在单细胞组学数据聚类中的研究。
教育背景
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北京邮电大学软件工程 本科2023年9月 - 2027年6月
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新加坡国立大学计算机学院 NGNE 交换项目2026年8月 - 2027年5月
荣誉与奖项
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第十五届中国大学生服务外包创新创业大赛 全国三等奖2024
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第六届全球校园人工智能算法精英大赛 北京赛区二等奖（AI+医学赛道）2024
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第十六届中国大学生服务外包创新创业大赛 北京赛区三等奖2025
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第十一届全国大学生统计建模大赛 北京赛区一等奖2025
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第十八届中国大学生计算机设计大赛 北京赛区二等奖2025
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校级三等奖学金2025
News
代表性论文 (view all )
BiFlow-GAN: Integrating Bidirectional Flows and Cross-Modal Enhancement for Multi-Modal Spatial Transcriptomics
Jingxuan Wang, Rui Guan, Kun Liang, Shiyu Wang, Xin Yang, Xiaodan Cui, Di Hu
IEEE Transactions on Multimedia (Under Review) 2026 JCR Q1
We propose BiFlow-GAN, a bidirectional-flow generative adversarial framework for robust tissue domain identification in spatial transcriptomics. We design a bidirectional cross-modal attention flow (Bi-AF) mechanism with reciprocal Image-to-Gene and Gene-to-Image pathways for dynamic feature refinement. Benchmarked on diverse spatial transcriptomics datasets, significantly outperforming state-of-the-art methods.
BiFlow-GAN: Integrating Bidirectional Flows and Cross-Modal Enhancement for Multi-Modal Spatial Transcriptomics
Jingxuan Wang, Rui Guan, Kun Liang, Shiyu Wang, Xin Yang, Xiaodan Cui, Di Hu
IEEE Transactions on Multimedia (Under Review) 2026 JCR Q1
We propose BiFlow-GAN, a bidirectional-flow generative adversarial framework for robust tissue domain identification in spatial transcriptomics. We design a bidirectional cross-modal attention flow (Bi-AF) mechanism with reciprocal Image-to-Gene and Gene-to-Image pathways for dynamic feature refinement. Benchmarked on diverse spatial transcriptomics datasets, significantly outperforming state-of-the-art methods.
DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data
Jingxuan Wang, Jie Yang, M. A. Khan, Poh Loong Yee, Jian Baili, Di Hu
IEEE Journal of Biomedical and Health Informatics 2025 JCR Q1
We propose a dual-GAN framework to enhance both input and latent representations for noisy single-cell data, and design a multi-positive contrastive clustering strategy to model continuous cell-state transitions. Evaluated on multiple real-world single-cell datasets, achieving state-of-the-art clustering performance.
DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data
Jingxuan Wang, Jie Yang, M. A. Khan, Poh Loong Yee, Jian Baili, Di Hu
IEEE Journal of Biomedical and Health Informatics 2025 JCR Q1
We propose a dual-GAN framework to enhance both input and latent representations for noisy single-cell data, and design a multi-positive contrastive clustering strategy to model continuous cell-state transitions. Evaluated on multiple real-world single-cell datasets, achieving state-of-the-art clustering performance.