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卢杨
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副教授
博士生导师
硕士生导师
- 教师英文名称：Yang Lu
- 出生日期：1990-04-25
- 电子邮箱：be24649b272ba1500754558913d4a2ab3459a91a62977fb0566a2b05f47889185c888486a6b8c31155e4d85e36f100fb66271c10a01603c5a9dfd6bfe82e1524555326f68bb45852f72bb926bfcab19ee789f14dc1047678b31c52fb4da25eb7ce1830e5686cbc4df5e1dd19acc6b7dcddfc07eff254bc5748cbaf89ed40d84a
- 入职时间：2019-12-10
- 所在单位：信息学院
- 学历：博士研究生毕业
- 性别：男
- 学位：哲学博士学位
- 在职信息：在职
- 毕业院校：香港浸会大学
- 学科：计算机科学与技术
- 2025-12曾获荣誉当选：ACM厦门分会新星奖
- 2023-11曾获荣誉当选：小米青年学者
访问量：
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[11]卢杨(教师).NeurIPT: Foundation Model for Neural Interfaces.San Diego, USA:Advances in Neural Information Processing Systems,2025,
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[12]卢杨(教师).Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster.San Diego, USA:Advances in Neural Information Processing Systems,2025,
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[13]卢杨(教师),PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocation.Honolulu, Hawaii:IEEE/CVF International Conference on Computer Vision,2025,
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[14]卢杨(教师),You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data.Honolulu, Hawaii:IEEE/CVF International Conference on Computer Vision,2025,
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[15]卢杨(教师),Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch.Nashville, USA:IEEE/CVF Conference on Computer Vision and Pattern Recognition,2025,
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[16]卢杨(教师).Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models.Vienna, Austria:Annual Meeting of the Association for Computational Linguistics,2025,
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[17]卢杨(教师).Categorical Data Clustering via Value Order Estimated Distance Metric Learning.Bengaluru, India:ACM SIGMOD International Conference on Management of Data,2026,
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[18]卢杨(教师).Asynchronous Federated Clustering with Unknown Number of Clusters.Pennsylvania, USA:AAAI Conference on Artificial Intelligence,2025,
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[19]卢杨(教师).MaskViM: Domain Generalized Semantic Segmentation with State Space Models.Pennsylvania, USA:AAAI Conference on Artificial Intelligence,2025,
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[20]卢杨(教师),FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data.Dublin, Ireland:ACM Multimedia,2025,
