◎研究方向 可信人工智能，统计学习理论，大模型
◎教育经历 华中农业大学 博士 南洋理工大学 博士后 （Research Fellow）
◎工作经历 阿联酋人工智能大学 研究助理 京东探索研究院 算法工程师
◎学术兼职 1. IEEE SMC Society: Human Machine Systems- Cognitive Computing Technical Committee 委员 2. 长期担任ICML、NeurIPS、ICLR、AAAI、IEEE TC等人工智能主流会议或期刊审稿人
◎主讲课程 模式识别基础、深度学习
◎指导研究生及博士后
◎承担项目 时序深度可加网络的算法与学习理论研究 国家自然科学基金青年基金 基于对比学习的智慧园艺研究 智能技术教育部工程研究中心 鲁棒深度可加网络的算法与理论研究 中国石油大学（华东）自主创新科研计划项目
◎获奖情况
◎荣誉称号
◎著作
◎论文 1. Yingjie Wang et al. Tilted Sparse Additive Models. In Proceedings of the International Conference on Machine Learning (ICML), 2023. (Oral, 2.37%). 2. Yingjie Wang et al. Huber Additive Models for Non-stationary Time Series Analysis. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. 3. Yingjie Wang et al. Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery. In Advances in Neural Information Processing Systems (NeurIPS), 2020. 4. Shi Fu, Sen Zhang, Yingjie Wang, et al. Towards Theoretical Understandings of Self-Consuming Generative Models, ICML, 2024. 5. Hong Chen, Yingjie Wang et al. Sparse Modal Additive Model. IEEE Transactions on Neural Networks and Learning Systems, 32(6):2373-2387, 2021. 6. Yingjie Wang et al. Sparse additive machine with pinball loss. Neurocomputing, 439: 281-293, 2021. 7. Hong Chen, Yingjie Wang, Yulong Wang, et al. Distributed Ranking with Communications: Approximation Analysis and Applications. AAAI, 2021 8. Xuebin Zhao, Hong Chen,Yingjie Wang, et al. Error Based Knockoffs Inference for Controlled Feature Selection. The Association for the Advancement of Artificial Intelligence, AAAI, 2022.
◎专利
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