伍冬睿

姓名：伍冬睿

职称：教授

职务：人工智能与自动化学院副院长

电子邮件：drwu@hust.edu.cn

个人主页：http://faculty.hust.edu.cn/drwu

研究领域：脑机接口、机器学习、智慧医疗、计算智能、情感计算

教育经历：

2003，中国科学技术大学，自动化，学士

2006，新加坡国立大学，电子与计算机工程，硕士

2009，美国南加州大学，电子工程，博士

奖励荣誉：

  1. 2024 世界机器人大赛–BCI脑控机器人大赛技术赛 全国特等奖（冠军）
  2. 2023 IEEE Fellow
  3. 2023 中国自动化学会自然科学一等奖 (1/5)
  4. 2023《麻省理工科技评论》中国智能计算创新人物
  5. 2023 世界机器人大赛–BCI脑控机器人大赛技术赛 全国一等奖（亚军）
  6. 2023 华瑙学者杰出青年奖
  7. 2023 脑科学与类脑智能科创新青年30人-青年科学家
  8. 2023 IEEE神经系统与康复工程汇刊最佳副编奖
  9. 2022 教育部青年科学奖
  10. 2022 瀚翔青年科学家奖
  11. 2022 世界机器人大赛–BCI脑控机器人大赛技术赛 全国特等奖（冠军）
  12. 2022 湖北青年五四奖章
  13. 2021 中国自动化学会 青年科学家奖
  14. 2021 世界机器人大赛–BCI脑控机器人大赛技术赛 全国特等奖（冠军）
  15. 2021 IEEE 神经系统与康复工程汇刊 最佳论文奖
  16. 2020 USERN Prize in Formal Sciences
  17. 2020 湖北省杰出青年基金
  18. 2020 世界机器人大赛–BCI脑控机器人大赛技术赛 全国一等奖
  19. 2019 第三届中国脑机接口比赛技术赛 全国一等奖
  20. 2018 IEEE人机系统汇刊 最佳副编奖
  21. 2017 ICONIP最佳学生论文奖入围
  22. 2017 IEEE系统、人和控制论学会 青年科学家奖
  23. 2016 IEEE脑计划 最佳论文奖入围
  24. 2015 德国-美国工程前沿论坛
  25. 2015 美国国家科学院、工程院、医学院Kech未来计划
  26. 2015 IEEE情感计算汇刊 最有影响力论文奖入围
  27. 2014 首席信息官100项目奖
  28. 2014 北美模糊信息处理学会 青年科学家奖
  29. 2014 IEEE模糊系统汇刊 最佳论文奖
  30. 2013 海德堡阿贝尔、图灵、菲尔兹获奖者论坛
  31. 2012 首席信息官100项目奖
  32. 2012 IEEE计算智能协会 最佳博士论文奖
  33. 2005 IEEE模糊系统国际会议 最佳学生论文奖

学术任职：

  1. IEEE模糊系统汇刊 (IF=11.9) 主编 (2023-)
  2. IEEE神经系统与康复工程汇刊 (IF=4.9) 副编 (2019-)
  3. IEEE 系统、人和控制论学会助理副主席, 2021-2022
  4. IEEE 系统、人和控制论学会管理委员会委员, 2022
  5. IEEE模糊系统汇刊 (IF=11.9) 副编 (2011–2018; 2020-2021)
  6. IEEE人机系统汇刊 (IF=2.968) 副编 (2014–)
  7. IEEE计算智能杂志 (IF=11.356) 副编 (2017–)
  8. IEEE神经系统和康复工程汇刊 (IF=3.802) 副编 (2019–)
  9. 2013年客座主编IEEE计算智能杂志“计算智能与情感计算”特刊
  10. 2016年客座主编IEEE模糊系统汇刊“脑机接口”特刊
  11. 2018年客座主编IEEE计算智能新兴主题汇刊 “深度迁移学习进展”特刊
  12. 2021年客座主编IEEE计算智能杂志“智慧医疗中的元学习”特刊
  13. 海德堡阿贝尔、图灵、菲尔兹获奖者论坛评委
  14. IEEE计算智能学会武汉分会主席
  15. IEEE系统、人和控制论学会武汉分会副主席
  16. IEEE计算智能学会情感计算工作组主席

科研成果：

IEEE Fellow，IEEE模糊系统汇刊(IF=11.9)主编。主要研究方向为脑机接口、机器学习等，成果应用于中船、华为等。发表PIEEE、IEEE TPAMI、《国家科学评论》等论文200余篇，谷歌学术引用1.6万余次。连续5年入选斯坦福大学全球前2%科学家榜单。获教育部青年科学奖、中国自动化学会自然科学一等奖（排名第1）、《麻省理工科技评论》中国智能计算创新人物等，及6个最佳论文奖。2021/2022/2024中国脑机接口比赛技术赛全国冠军，央视新闻直播间采访报道。

学术专著：

  1. J. M. Mendel and D. Wu, “Perceptual Computing: Aiding People in Making Subjective Judgments,” Wiley-IEEE Press, April 2010. (Matlab code) (Book Review)(Google Books)

代表性论文：

Brain-Computer Interface:
  1. D. Wu*, “Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces,” Journal of Neural Engineering, 22:031005, 2025.
  2. 伍冬睿, “精准、安全、隐私保护的脑机接口”, 中国人工智能学会通讯, 15(3):29-35, 2025.
  3. L. Meng, X. Jiang, X. Chen, W. Liu, H. Luo and D. Wu*, “Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces,” Information Fusion, 107:102316, 2024. (Python)
  4. R. Bian, H. Wu, B. Liu and D. Wu*, “Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 31:446-455, 2023. (Python)
  5. D. Wu, J. Xu, W. Fang, Y. Zhang, L. Yang, X. Xu*, H. Luo* and X. Yu*, “Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review,” National Science Open, 1:20220023, 2022.
  6. D. Wu*, X. Jiang, R. Peng, “Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Tutorial,” Neural Networks, 153:235-253, 2022. (Matlab)
  7. X. Zhang, D. Wu*, L. Ding*, H. Luo, C-T Lin, T-P Jung and R. Chavarriaga, “Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers,” National Science Review, 8(4), 2021. (Python; TechXplore; TechXplore2)
  8. W. Zhang and D. Wu*, “Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1117-1127, 2020. (Python; 世界机器人大会)
  9. H. He and D. Wu*, “Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach,” IEEE Trans. on Biomedical Engineering, 67(2):399-410, 2020. (ESI Highly Cited Paper; Matlab; IEEE Brain)
  10. X. Zhang and D. Wu*, “On the Vulnerability of CNN Classifiers in EEG-Based BCIs,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(5):814-825, 2019. (Python)
Affective Computing:
  1. D. Wu, B-L Lu, B. Hu* and Z. Zeng*, “Affective Brain-Computer Interfaces (aBCIs): A Tutorial,” Proceedings of the IEEE, 111(10):1314-1332, 2023.
  2. Y. Xu, X. Jiang and D. Wu*, “Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition,” IEEE Trans. on Affective Computing, 2024, in press. (Python)
  3. Y. Xu, Y. Cui, X. Jiang, Y. Yin, J. Ding, L. Li and D. Wu*, “Inconsistency-Based Multi-Task Cooperative Learning for Dimensional Emotion Recognition,” IEEE Trans. on Affective Computing, 13(4):2017-2027, 2022. (Python)
  4. D. Wu* and J. Huang, “Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,” IEEE Trans. on Affective Computing, 13(1):16-27, 2022.
  5. S. Li, Y. Xu, H. Wu, D. Wu*, Y. Yin, J. Cao and J. Ding, “Facial Expression Recognition in-the-wild with Deep Pre-trained Models,” European Conference on Computer Vision (ECCV) ABAW Workshop, Tel Aviv, Israel, October 2022.  (Python)
  6. 权学良, 曾志刚, 蒋建华, 张亚倩, 吕宝粮, 伍冬睿*, 基于生理信号的情感计算研究综述. 自动化学报, 47(8):1769−1784, 2021.
  7. D. Wu, C. Courtney, B. Lance, S. Narayanan, M. Dawson, K. Oie, and T.D. Parsons, “Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task,” IEEE Trans. on Affective Computing, 1(2):109-118, 2010. (Top Accessed Article; IEEE Trans. on Affective Computing Most Influential Paper Award Finalist)
Machine Learning:
  1. L. Deng, Y. Wang, H. Wang, X. Ma, X. Du, X. Zheng and D. Wu*, “Time-Aware Attention-Based Transformer (TAAT) for Cloud Computing System Failure Prediction,” ACM KDD, Barcelona, Spain, August 2024.
  2. C. Zhao, D. Wu*, J. Huang, Y. Yuan, H-T Zhang, R. Peng and Z. Shi, “BoostTree and BoostForest for Ensemble Learning,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 45(7):8110-8126, 2023. (Python)
  3. W. Zhang, L. Deng, L. Zhang and D. Wu*, “A Survey on Negative Transfer,” IEEE/CAA Journal of Automatica Sinica, 10(2):305-329, 2023. (Python)
  4. X. Zhang, H. Xiong and D. Wu, “Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs,” Int’l Joint Conf. on Artificial Intelligence (IJCAI), Montreal, Canada, August 2021.
  5. X. Zhang and D. Wu*, “Empirical Studies on the Properties of Linear Regions in Deep Neural Networks,” Int’l. Conf. on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020.
  6. D. Wu*, Y. Yuan, J. Huang and Y. Tan*, “Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA),” IEEE Trans. on Fuzzy Systems, 28(5):1003-1015, 2020. (Matlab)
  7. D. Wu* and J.M. Mendel, “Patch Learning,” IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020. (Matlab; IEEE CIS Publication Spotlight)
  8. D. Wu, C-T Lin, J. Huang* and Z. Zeng*, “On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression,” IEEE Trans. on Fuzzy Systems, 28(10):2570-2580, 2020.
  9. D. Wu*, C-T Lin and J. Huang*, “Active Learning for Regression Using Greedy Sampling,” Information Sciences, 474:90-105, 2019. (Matlab)
  10. D. Wu, “Pool-based sequential active learning for regression,” IEEE Trans. on Neural Networks and Learning Systems, 30(5): 1348-1359, 2019. (Matlab)
Smart Healthcare:
  1. Z. Wang, S. Li and D. Wu*, “Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment,” National Science Review, 12:nwaf086, 2025. (Python)
  2. J. An, R. Peng, Z. Du, H. Liu, F. Hu, K. Su* and D. Wu*, “Sparse Knowledge Sharing (SKS) for Privacy-Preserving Domain Incremental Seizure Detection,” Journal of Neural Engineering, 22(2):026003, 2025. (Python)
  3. R. Peng, Z. Du, C. Zhao, J. Luo, W. Liu, X. Chen* and D. Wu*, “Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:831-839, 2024. (Python)
  4. Z. Du, R. Peng, W. Liu, W. Li* and D. Wu*, “Mixture of Experts for EEG-Based Seizure Subtype Classification,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 31:4781-4789, 2023. (Python)
  5. Z. Wang, W. Zhang, S. Li, X. Chen and D. Wu*, “Unsupervised Domain Adaptation for Cross-Patient Seizure Classification,” Journal of Neural Engineering, 20(6):066002, 2023. (Python)
  6. R. Peng, C. Zhao, J. Jiang, G. Kuang, Y. Cui, Y. Xu, H. Du, J. Shao*, and D. Wu*, “TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 30:2567-2576, 2022. (Python)
  7. 彭睿旻, 江军, 匡光涛, 杜浩, 伍冬睿*, 邵剑波. 基于EEG的癫痫自动检测: 综述与展望. 自动化学报, 48(2):335-350, 2022.