1. 基本情况：

  敬蒙蒙，男，中共党员，计算机科学与技术专业博士，信息与软件工程学院副教授。

  主要研究迁移学习，少样本学习，多模态大模型等。发表论文40余篇，包括NeurIPS，ICCV，CVPR，TIP等计算机领域的顶会顶刊，2篇论文引用量进入全球计算机领域前1%，入选ESI高被引论文。所发表论文多次提出了该领域内的原创性概念，如快速域适应，不完整跨模态检索，自适应分量嵌入等。Google学术引用超过1600次，H指数18，累计影响因子超过100，担任顶会AAAI, CIKM, ECAI的程序委员会委员，担任计算机领域多个顶级期刊和会议的审稿人，包括IEEE TPAMI, CVPR, ICCV, NeurIPS等。受邀在第二届CCF全国优秀博士生论坛作口头报告。

  近年来，主持多项国家级和省部级基金项目。

2. 个人主页：

  学院主页：https://sise.uestc.edu.cn/info/1037/11598.htm

  谷歌学术：https://scholar.google.com.hk/citations?user=gVByeFUAAAAJ&hl=zh-CN

3. 代表性论文： 

  [1] Jing Mengmeng, Zhen Xiantong, Li Jingjing, et al. Order-preserving Consistency Regularization

  for Domain Adaptation and Generalization. ICCV, 2023. CCF-A

  [2] Jing Mengmeng, Zhen Xiantong, Li Jingjing, et al. Variational model perturbation for 

  source-free domain adaptation . NeurIPS, 2023. CCF-A

  [3] Jing Mengmeng, Li Jingjing, Lu Ke, et al. Visually Source-Free Domain Adaptation via 

  Adversarial Style Matching. IEEE Transactions on Image Processing , 2024. 中科院一区Top

  [4] Jing Mengmeng, Zhao Jidong, Li Jingjing, et al. Adaptive Component Embedding for Domain

  Adaptation. IEEE Transactions on Cybernetics , 2021. 中科院一区Top

  [5] Jing Mengmeng, Meng Lichao, Li Jingjing, et al. Adversarial Mixup Ratio Confusion for

  Unsupervised Domain Adaptation. IEEE Transactions on Multimedia,2023. 中科院一区Top

  [6] Jing Mengmeng, Li Jingjing, Zhu Lei, et al. Balanced Open Set Domain Adaptation via Centroid

  Alignment. AAAI, 2021. CCF-A

  [7] Jing Mengmeng, Li Jingjing, Zhu Lei, et al. Incomplete Cross-modal Retrieval with Dual-Aligned

  Variational Autoencoders. ACM Multimedia, 2020. CCF-A

  [8] Jing Mengmeng, Li Jingjing, Lu Ke, et al. Learning explicitly transferable representations for

  domain adaptation. Neural Networks, 2020. 中科院一区Top

  [9] Zuo Lin, Jing Mengmeng, Li Jingjing,et al. Challenging tough samples in unsupervised domain

  adaptation. Pattern Recognition, 2021. 中科院一区Top

  [10] Li Jingjing, Jing Mengmeng, Lu Ke,et al. Locality Preserving Joint Transfer for Domain

  Adaptation. IEEE Transactions on Image Processing, 2019. 中科院一区Top

  [11] Liu Jieyan, Jing Mengmeng, et al. Open Set Domain Adaptation via Joint Alignment and

  Category Separation. IEEE Transactions on  Neural Networks and Learning Systems,2023. 中科院一区

  Top

  [12] Li Jingjing, Jing Mengmeng, Lu Ke, et al. Leveraging the Invariant Side of Generative

  Zero-Shot Learning. CVPR, 2019. CCF-A

  [13] Li Jingjing, Jing Mengmeng, Lu Ke, et al. From Zero-Shot Learning to Cold-Start

  Recommendation. AAAI, 2019. CCF-A

  [14] Li Jingjing, Jing Mengmeng, Lu Ke,et al. Investigating the Bilateral Connections in Generative

  Zero-Shot Learning. IEEE Transactions on Cybernetics , 2021. 中科院一区Top

  [15] Li Jingjing, Jing Mengmeng, Su Hongzu, et al. Faster Domain Adaptation Networks. IEEE

  Transactions on Knowledge and Data Engineering, 2022. 中科院一区Top

  [16] Li Jingjing, Jing Mengmeng, Zhu Lei, et al. Learning Modality-Invariant Latent Representations

  for Generalized Zero-shot Learning. ACM Multimedia, 2020. CCF-A

  [17] Li Jingjing, Jing Mengmeng, Lu Ke, et al. Alleviating Feature Confusion for Generative

  Zero-shot Learning. ACM Multimedia, 2020. CCF-A

  [18] Xie Yue, Du Zhekai, Li Jingjing, Jing Mengmeng, et al. Joint metric and feature representation

  learning for unsupervised domain adaptation. Knowledge Based Systems, 2020. 中科院一区Top

  [19] Ma Ao, You Fuming, Jing Mengmeng, et al. Multi-source domain adaptation with graph

  embedding and adaptive label prediction.Information Processing & Management, 2020. 中科院一区

  Top

  [20] Jing Mengmeng, Li Jingjing, Zhao Jidong, et al. Learning Distribution-Matched Landmarks for

  Unsupervised Domain Adaptation. DASFAA, 2018. CCF-B

  [21] Jing Mengmeng, Li Jingjing, Lu, Ke, et al. Adaptive component embedding for unsupervised

  domain adaptation. ICME, 2019. CCF-B

  [22] Ding Yongqi, Zuo Lin, Jing Mengmeng, et al. Shrinking Your TimeStep: Towards Low-Latency

  Neuromorphic Object Recognition with Spiking Neural Networks. AAAI, 2024. CCF-A

  [23] Mengmeng Jing, Jingjing Li, Ke Lu, et al. Visually source-free domain adaptation via adversarial style matching. TIP, 2024. CCF-A

  [24] Kunshan Yang, Lin Zuo, Mengmeng Jing, et al. Flexible ViG: Learning the self-saliency for flexible object recognition. TCSVT, 2025. 中科院一区Top

  [25] Yongqi Ding, Lin Zuo, Mengmeng Jing, et al. Rethinking spiking neural networks from an ensemble learning perspective. ICLR, 2025. 机器学习顶会

4. 荣誉和奖励：