研究方向
· AI赋能科学发现
· 多智能体协同学习
· 大模型未知识别与幻觉消解
· 大模型连续学习
授课信息
· 数据挖掘
社会兼职
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· 中国人工智能学会粒计算与知识发现专业委员会,副秘书长
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· 天津市人工智能学会,理事
个人简介
天津大学人工智能学院社会智能系主任、教授、博士生导师,中国人工智能学会粒计算与知识发现专委会副秘书长,天津市人工智能学会理事;
入选国家级青年人才、天津市高层次青年人才、百大中国博士后科学基金资助者;
主持国家自然科学基金等国家级、省部级、企事业单位合作20余项科研项目;
围绕开放环境机器学习,发表IEEE TPAMI、IJCV、ICML、NeurIPS、CVPR等CCF-A类/IEEE汇刊/SCI一区高水平论文40余篇,其中第一/通讯作者发表30余篇;
曾获得或指导学生获得中国国际大学生创新大赛金奖、CVPR开放世界挑战赛冠军、天津市优秀博士学位论文等10余项荣誉奖励;
研制技术已应用至新型材料发现、关键设备故障诊断与健康管理、海洋牧场立体监测体系、社会治理等多个重要领域,获得天津市自然科学一等奖、天津市科学技术进步一等奖、中国人工智能学会吴文俊科学技术进步一等奖等,技术入选工信部全国机器人应用场景优秀名单;
作为专项负责人建设《人工智能导论》通识课程,覆盖近1000所高校院所,近200万人次互动学习,受到中国教育报、人民网等10余家主流媒体报道。
欢迎产学研合作及希望加入团队的硕博士研究生联系,请邮件咨询wang.yu@tju.edu.cn。
----- Upcoming ! -----
面向全国“工、农、文、医”全学科专业学生与企业研发工作者的AI科学研究通识课程《AI4Science实战:技术、工具与应用案例》,将于2026年9月1日面向全国上线超星尔雅课程平台,同步上线“天研”AI赋能科学研究平台,可直接应用于科研课题及研发任务,敬请关注。
----- News ! -----
论文"Collaborative Knowledge Extraction and Integration for Graph Domain Incremental Learning"录用至CCF-A类会议SIGKDD.
论文"CKD: Contrastive knowledge distillation from a sample-wise perspective"录用至CCF-A类期刊IEEE Transactions on Image Processing (TIP).
论文"BackMix: regularizing open set recognition by removing underlying fore-background priors"录用至CCF-A类期刊IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
论文"Reducing class-wise confusion for incremental learning with disentangled manifolds"录用至CCF-A类会议IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
课题组博士研究生姚鑫杰,硕士研究生季罗娜、穆郡贤获得研究生国家奖学金.
论文"Persistence homology distillation for semi-supervised continual learning" 录用至CCF-A类会议 Annual Conference on Neural Information Processing Systems (NeurIPS).
论文"What matters in graph class incremental learning? An information preservation perspective" 录用至CCF-A类会议 Annual Conference on Neural Information Processing Systems (NeurIPS).
学术成果
论文成果
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[1]Class-specific semantic reconstruction for open set recognition
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[2]Integrated heterogeneous graph attention network for incomplete multi-modal clustering
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[4]Multi-granularity regularized re-balancing for class incremental learning
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[5]Persistence homology distillation for semi-supervised continual learning
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[6]Exploring diverse representations for open set recognition
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[7]Hierarchical semantic risk minimization for large-scale classification
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[8]Deep fuzzy tree for large-scale hierarchical visual classification
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[9]Boosting pseudo labeling with curriculum self-reflexion for attributed graph clustering
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[11]Dynamic sub-graph distillation for robust semi-supervised continual learning
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[12]Every node is different: dynamically fusing self-supervised tasks for attributed graph clustering
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[13]Collaborative decision-reinforced self-supervision for attributed graph clustering
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[14]Socialized learning: making each other better through multi-agent collaboration
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[15]Latent heterogeneous graph network for incomplete multi-view learning
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[17]Multi-granularity episodic contrastive learning for few-shot learning
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[18]Uncertainty instructed multi-granularity decision for large-scale hierarchical classification
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[20]大规模分类任务的分层学习方法综述

