Mengzhu Wang

Mengzhu Wang

National University of Defense Technology

Hi, I am Mengzhu Wang (汪梦竹). I received my M.Sc. degree from Chongqing University in 2018 and my Ph.D. degree in 2023 from the National University of Defense Technology. Currently, I am a Tenure-Track Associate Professor (Yuanguang Scholar) in the School of Artificial Intelligence at Hebei University of Technology. Previously, I interned at JD Explore Academy and DAMO Academy of Alibaba Group. I am also serving as a Program Committee Member for top-tier AI conferences, including ICML, ICLR, NeurIPS, CVPR and so on. My research interests lie broadly in machine learning and transfer learning. Specifically, I have been focusing on the development of algorithms and theoretical understanding for transfer learning, domain adaptation, and generalization in complex medical environments. I have published over 80 papers in top conferences and journals such as CVPR, ICML, ICLR, AAAI, ACM MM, IEEE TIP, IEEE TKDE, and IEEE TNNLS. My long-term goal is to design robust and efficient transfer learning models that can adapt well to real-world scenarios, especially in challenging settings like medical image segmentation and single-cell multi omics.

News!

  • 课题组拟于2026年招收硕士研究生7名，研究方向涵盖迁移学习、计算机视觉、大模型、医学图像分割和单细胞多组学，欢迎有意申请的同学通过邮件提交材料，包括个人简历、成绩单和科研计划说明，联系方式(dreamkily@gmail.com)。
  • [July, 2025] One paper have been accepted by ECAI 2025.
  • [May, 2025] One paper have been accepted by ICML 2025.
  • [Apr, 2025] Six papers have been accepted by IJCAI 2025.
  • [Jan, 2025] One paper have been accepted by TCSVT.
  • [Dec, 2024] One paper have been accepted by AAAI 2025.
  • [Mar, 2024] One paper have been accepted by TNNLS.
  • [Jan, 2024] One paper have been accepted by TIP.

Recent Publication

  • M Wang, H Su, J Li, C Li, N Yin, L Shen, J Guo. (2025) GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation, Forty-second International Conference on Machine Learning (ICML) (CCF-A会议)
  • M Wang, W Ren, Y Zhang, Y Fan, D Shi, L Jing, N Yin. (2025) Gaussian Mixture Model for Graph Domain Adaptation. The 34th International Joint Conference on Artificial Intelligence (IJCAI) (CCF-A会议)
  • M Wang, H Su, et al. (2025) Graph Convolutional Mixture-of-Experts Learner Network for Long-Tailed Domain Generalization, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) (中科院一区)
  • M Wang. (2025) SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation(AAAI) (CCF-A会议)
  • J Li, H Su, B Wang, Y Min, M Wang, N Yin, J Guo, S Wang. (2025) ESBN: Estimation Shift of Batch Normalization for Source-free Universal Domain Adaptation. The 34th International Joint Conference on Artificial Intelligence (IJCAI) (CCF-A会议, 通讯作者)
  • H Tan, M Cao, K Hu, X He, Z Wang, H Li, L Lan, M Wang. (2025) Wave-wise Discriminative Tracking by Phase-Amplitude Separation, Augmentation and Mixture. The 34th International Joint Conference on Artificial Intelligence (IJCAI) (CCF-A会议, 通讯作者)
  • N Yin, X Teng, Z Cao, M Wang. (2025) Coupling Category Alignment for Graph Domain Adaptation.The 34th International Joint Conference on Artificial Intelligence (IJCAI) (CCF-A会议, 通讯作者)
  • M Wang, J Chen, H Wang, H Wu, Z Liu, Q Zhang. (2023). Interpolation Normalization for Contrast Domain Generalization. 2023 ACM 31th International Conference on Multimedia (ACMMM) (CCF-A会议)
  • M Wang, J Yuan, Z Wang. (2023). Mixture-of-Experts Learner for Single Long-Tailed Domain Generalization. 2023 ACM 31th International Conference on Multimedia (ACMMM) (CCF-A会议 oral)
  • M Wang, J Yuan, Q Qian, Z Wang, H Li. (2022). Semantic Data Augmentation based Distance Metric Learning for Domain Generalization. 2022 ACM 30th International Conference on Multimedia (ACMMM) (CCF-A会议 oral).
  • M Wang, W Wang, B Li, X Zhang, L Lan, H Tan, T Liang, W Yu, Z Luo. (2021). Interbn: Channel fusion for adversarial unsupervised domain adaptation, 2021 ACM 30th International Conference on Multimedia (ACMMM) (CCF-A会议 oral).
  • M Wang, X Zhang, L Lan, Z Luo. Equity in Unsupervised Domain Adaptation by Nuclear Norm Maximization, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) (中科院一区)
  • M Wang, X Zhang, L Lan, Z Luo. Smooth-Guided Implicit Data Augmentation for Domain Generalization, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (中科院一区)
  • M Wang, X Zhang, L Lan, Z Luo. Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization, IEEE Transactions on Image Processing (TIP) (CCF-A, 中科院一区)
  • M Wang, J Chen, Y Wang, Z Gong, K Wu, C.M.Leung. (2023) TFC: Transformer Fused Convolution for Adversarial Domain Adaptation, IEEE Transactions on Computational Social Systems (中科院二区)
  • N Yin*, M Wang, B Gu, X Luo, DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption, 2024, International Conference on Learning Representations (ICLR) (共同一作)
  • H Su, W Luo, D Liu, M Wang, J Tang, J Chen, C Wang, Z Chen. (2024). Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization, Association for the Advancement of Artificial Intelligence (AAAI) (CCF-A会议, 通讯作者).
  • T Liang, B Li, M Wang, H Tan, Z Luo. A Closer Look at the Joint Training of Object Detection and Re-identification in Multi-Object Tracking, IEEE Transactions on Image Processing (CCF-A期刊, 通讯作者)
  • M Wang, J Chen, Y Wang, Z Chen, (2023) Joint Adversarial Domain Adaptation With Structural Graph Alignment, IEEE Transactions on Network Science and Engineering
  • M Wang, S Wang, W Wang, T Liang, J Chen, Z Luo. (2023) Reducing Bi-level Feature Redundancy for Unsupervised Domain Adaptation. Pattern Recognition (中科院一区)
  • W Wang, M Wang, Z Wang, H Li, Z Wang. (2023) Importance filtered soft label-based deep adaptation network. Knowledge-Based Systems (中科院一区, 通讯作者)
  • W. Wang, M Wang, X Dong, L Lan, Q Zu, X Zhang, C Wang. (2023). Class-specific and Self-learning Local Manifold Structure for Domain Adaptation. Pattern Recognition (中科院一区)
  • M Wang, P Li, L Shen, Y Wang, S Wang, W Wang, X Zhang, J Chen, Z Luo. (2022). Informative Pairs Mining based Adaptive Metric Learning for Adversarial Domain Adaptation, Neural Networks (中科院一区).

Academic Service

  • Reviewer for WACV 2026, ECAI 2025
  • Reviewer for ICLR 2024, ICML 2024, NeurIPS 2023-2025, AAAI 2023-2025, IJCAI 2025
  • Reviewer for CVPR 2025, ICCV 2025, ACM MM 2023-2024, ECCV 2024, MICCAI 2025, ICASSP 2024
  • Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • Reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Reviewer for IEEE Transactions on Image Processing

Major Grants

  • 国家自然科学基金青年基金， 基于数据增强的迁移学习方法和理论研究, 主持, 2025.1-2027.12,30万
  • 天津市自然科学基金青年基金, 面向数据驱动的可见与不可见迁移学习方法研究, 主持, 2025.01-2026.10，6万
  • ZZY, 大模型xxx，课题五主持，2024.10-2026.12，400万
  • xxx科学院, 跨领域多维度xxx, 主持, 2024.10-2028.12, 100万
  • xxx科学院，认知xxx, 主持, 2025.05-2025.10, 40万
  • 北京市博士后日常工作经费资助，主持，2025.05-2026.10,10万

Work Experience

  • 河北工业大学, 人工智能与数据科学学院, 副教授/博士生导师, 2023.12-至今
  • 中国人民解放军军事科学院, 脑科学研究所, 访问学者, 2023.11-至今
  • 阿里巴巴, 达摩院, 实习算法研究员, 2022.02-2023.10
  • 京东, 探索研究院, 实习算法研究员, 2021.10-2022.02
  • 清华大学, 计算机科学与技术系, 实习生, 2017.08-2017.12