伍冬睿

姓名：伍冬睿

职称：教授

职务：图像信息处理与智能控制教育部重点实验室副主任

电子邮件：drwu@hust.edu.cn

个人主页：http://faculty.hust.edu.cn/drwu

个人博客：http://blog.sciencenet.cn/u/drwuHUST

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，自动化专业，学士

2006，新加坡国立大学，电子与计算机工程专业，硕士

2009，美国南加州大学，电子工程专业，博士

奖励荣誉：

  1. 2021 中国自动化学会 青年科学家奖
  2. 2021 世界机器人大赛–BCI脑控机器人大赛技术赛 全国一等奖
  3. 2021 IEEE 神经系统与康复工程汇刊 最佳论文奖
  4. 2020 USERN Prize in Formal Sciences
  5. 2020 湖北省杰出青年基金
  6. 2020 世界机器人大赛–BCI脑控机器人大赛技术赛 全国一等奖
  7. 2019 第三届中国脑机接口比赛技术赛 全国一等奖
  8. 2018 IEEE人机系统汇刊 最佳副编奖
  9. 2017 国家青年特聘专家
  10. 2017 ICONIP最佳学生论文奖入围
  11. 2017 IEEE系统、人和控制论学会 青年科学家奖
  12. 2016 IEEE脑计划 最佳论文奖入围
  13. 2015 德国-美国工程前沿论坛
  14. 2015 美国国家科学院、工程院、医学院Kech未来计划
  15. 2015 IEEE情感计算汇刊 最有影响力论文奖入围
  16. 2014 首席信息官100项目奖
  17. 2014 北美模糊信息处理学会 青年科学家奖
  18. 2014 IEEE模糊系统汇刊 最佳论文奖
  19. 2013 海德堡阿贝尔、图灵、菲尔兹获奖者论坛
  20. 2012 首席信息官100项目奖
  21. 2012 IEEE计算智能协会 最佳博士论文奖
  22. 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=12.029) 副编 (2011–2018; 2020-)
  2. IEEE人机系统汇刊 (IF=2.968) 副编 (2014–)
  3. IEEE计算智能杂志 (IF=11.356) 副编 (2017–)
  4. IEEE神经系统和康复工程汇刊 (IF=3.802) 副编 (2019–)
  5. 2013年客座主编IEEE计算智能杂志“计算智能与情感计算”特刊
  6. 2016年客座主编IEEE模糊系统汇刊“脑机接口”特刊
  7. 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
  8. 2021年客座主编IEEE计算智能杂志“智慧医疗中的元学习”特刊
  9. 海德堡阿贝尔、图灵、菲尔兹获奖者论坛评委
  10. IEEE计算智能学会武汉分会主席
  11. IEEE系统、人和控制论学会武汉分会副主席
  12. IEEE计算智能学会情感计算工作组主席

科研成果：

出版学术专著《Perceptual Computing》一部(Wiley-IEEE出版社)，发表国际学术期刊和会议论文170余篇，其中SCI 84篇(一作33篇，IEEE汇刊56篇)，ESI高引7篇，谷歌学术总引用8100余次(H=45)。1篇IEEE模糊系统汇刊最佳论文，1篇IEEE神经系统和康复工程汇刊最佳论文，1篇IEEE世界机电一体化与自动化会议最佳论文，5篇IEEE计算智能学会亮点论文，1篇在1997-2017年间共1288篇二型模糊系统SCI论文中引用排名12，1篇在Engineering Applications of Artificial Intelligence (IF=4.201) 1988-2018年间发表的2960篇论文中引用排名13。两种模糊系统算法进入Matlab 模糊逻辑工具箱。授权国际专利5项，中国发明专利6项。被中国日报、中国科学报、中新网、长江日报、财新周刊、麻省理工科技评论、DigitalTrends、TechXplore、Geeks等多个网站和媒体采访或报道。据斯坦福大学2020年发布的全球前2%科学家排名，在全球前215114位发表过5篇以上论文的人工智能科学家中排名361，中国第61。

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. 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)
  2. Z. Liu, L. Meng, X. Zhang, W. Fang, D. Wu*, “Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs,” Journal of Neural Engineering, 8:0460a4, 2021.
  3. D. Wu*, Y. Xu and B.-L. Lu, “Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016,” IEEE Trans. on Cognitive and Developmental Systems, 2020, accepted.
  4. 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)
  5. H. He and D. Wu*, “Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1091-1108, 2020. (Matlab; IEEE Brain)
  6. 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. (Matlab; IEEE Brain)
  7. Y. Cui, Y. Xu and D. Wu*, “EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(11):2263-2273, 2019. (IEEE TNSRE Cover Article; Python)
  8. 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)
  9. D. Wu*, J-T King, C-C Chuang, C-T Lin and T-P Jung, “Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI),” IEEE Trans. on Fuzzy Systems, 26(2):771-781, 2018.
  10. D. Wu*, B. J. Lance, V. J. Lawhern, S. Gordon, T-P Jung and C-T Lin, “EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features”, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(11):2157-2168, 2017.
  11. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, “Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR),” IEEE Trans. on Fuzzy Systems, 25(6):1522-1535, 2017.
  12. D. Wu*, “Online and Offline Domain Adaptation for Reducing BCI Calibration Effort,” IEEE Trans. on Human-Machine Systems, 47(4): 550-563, 2017. (ESI Highly Cited Paper)
  13. D. Wu, V. Lawhern, D. Hairston and B. Lance, “Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 24(11):1125-1137, 2016.
Affective Computing:
  1. 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, 2020, in press.
  2. D. Wu and J. Huang, “Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,” IEEE Trans. on Affective Computing, 2021, in press.
  3. 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. 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.
  2. 刘子昂, 蒋雪, 伍冬睿*, “基于池的无监督线性回归主动学习,” 自动化学报, 2021. (Matlab)
  3. 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.
  4. Z. Shi, D. Wu*, C. Guo, C. Zhao, Y. Cui and F-Y Wang*, “FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief“, Information Sciences, 2021, accepted.
  5. Z. Liu, X. Jiang, H. Luo, W. Fang, J. Liu and D. Wu*, “Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM),” Pattern Recognition Letters, 142:11-19, 2021.
  6. Y. Cui, D. Wu* and J. Huang*, “Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization,” IEEE Trans. on Fuzzy Systems, 28(12):3065-3075, 2020. (Matlab; Python)
  7. 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)
  8. D. Wu* and J.M. Mendel, “Patch Learning,” IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020. (Matlab; IEEE CIS Publication Spotlight)
  9. 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.
  10. D. Wu* and X. Tan, “Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization,” IEEE Trans. on Fuzzy Systems, 28(6):1050-1061, 2020. (Matlab; IEEE CIS Publication Spotlight)
  11. D. Wu*, C-T Lin and J. Huang*, “Active Learning for Regression Using Greedy Sampling,” Information Sciences, 474:90-105, 2019. (Matlab)
  12. 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. 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, 2021, accepted.
  2. Z. Fu, X. He, E. Wang, J. Huo, J. Huang, and D. Wu, “Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning,” Sensors, 21(885), 2021.
  3. X. Yang, Y. Zhang, B. Lo, D. Wu, H. Liao an Y-T Zhang, “DBAN: Adversarial Network with Multi-Scale Features for Cardiac MRI Segmentation,” IEEE Journal of Biomedical and Health Informatics, 2020, accepted.
  4. X. Song, P. Qian, J. Zheng, Y. Jiang, K. Xia, B. Traughber, D. Wu and R. F. Muzic, “mDixon-Based Synthetic CT Generation via Transfer and Patch Learning,” Pattern Recognition Letters, 138: 51-59, 2020.
  5. B. Zhang, Y. Cui, M. Wang, J. Li, L. Jin* and D. Wu*, “In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics,” IEEE Access, 7(1):130460-130467, 2019.
  6. Z. Liu, B. Huang, Y. Cui, Y. Xu, B. Zhang, L. Zhu, Y. Wang, L. Jin* and D. Wu*, “Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos,” IEEE Access, 7(1):122153-122163, 2019. (Python)
  7. Y. Jiang, X. Gu, D. Wu, W. Hang, J. Xue, S. Qiu and C-T Lin, “A Novel Negative-Transfer-Resistant Fuzzy Clustering Model with a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation,” IEEE/ACM Trans. on Computational Biology and Bioinformatics, 18(1): 40-52, 2021.
  8. X. Tian, Z. Deng, K-S Choi, D. Wu, B. Qin, J. Wan, H. Shen and S. Wang, “Deep multi-view feature learning for epileptic seizure detection,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(10):1962-1972, 2019.
  9. Y. Jiang, D. Wu, Z. Deng, P. Qian, J. Wang, G. Wang, F-L Chung, K-S Choi and S. Wang, “Seizure Classification from EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(12):2270-2284, 2017.
Intelligent Control:
  1. 伍冬睿*，曾志刚，莫红，王飞跃，“区间二型模糊集和模糊系统: 综述与展望,” 自动化学报, 46(8):1539-1556, 2020.
  2. D. Wu* and J.M. Mendel, “Recommendations on Designing Practical Interval Type-2 Fuzzy Systems“, Engineering Applications of Artificial Intelligence, 95:182-193, 2019.
  3. J. Huang, M. Ri, D. Wu* and S. Ri, “Interval Type-2 Fuzzy Logic Modeling and Control of a Mobile Two-Wheeled Inverted Pendulum,” IEEE Trans. on Fuzzy Systems, 26(4):2030-2036, 2018.
  4. D. Wu*, “Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Controllers: Overview and Comparison,” IEEE Trans. on Fuzzy Systems, 21(1):80-99, 2013. (ESI Highly Cited Paper)
  5. D. Wu*, “On the Fundamental Differences between Interval Type-2 and Type-1 Fuzzy Logic Controllers,” IEEE Trans. on Fuzzy Systems, 20(5):832-848, 2012. (IEEE CIS Publication Spotlight)
  6. D. Wu and J. M. Mendel, “On the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems,” IEEE Trans. on Fuzzy Systems, 19(1):179-192, 2011. (2014 IEEE TFS Outstanding Paper Award; IEEE CIS Publication Spotlight)
  7. D. Wu and J. M. Mendel, “Enhance Karnik-Mendel Algorithms,” IEEE Trans. on Fuzzy Systems, 17:923-934, 2009. (ESI Highly Cited Paper; Ranked 12th among all 1,288 SCI papers published worldwide on type-2 fuzzy systems in 1997-2017, according to “A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems,” IEEE Computational Intelligence Magazine, 15(1), pp. 89-98, 2020; Available in Matlab Fuzzy Logic Toolbox)
  8. D. Wu and W. W. Tan, “Genetic Learning and Performance Evaluation of Type-2 Fuzzy Logic Controllers,” Engineering Applications of Artificial Intelligence, 19(8):829-841, 2006. (Ranked 13th among all 2,960 papers published in EAAI in 1988-2018, according to “Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018),” EAAI, 85, pp. 517–532, 2019)
  9. D. Wu and W. W. Tan, “A Simplified Type-2 Fuzzy Controller for Real-Time Control,” ISA Trans., 15(4):503-516, 2006.
  10. D. Wu and W. W. Tan, “Type-2 FLS Modeling Capability Analysis,” IEEE Int’l Conf. on Fuzzy Systems, Reno, USA, May 2005. (Best Student Paper Award)
Perceptual Computing & Decision Making:
  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)
  2. D. Wu* and J. M. Mendel, “Similarity Measures for Closed General Type-2 Fuzzy Sets: Overview, Comparisons, and a Geometric Approach,” IEEE Trans. on Fuzzy Systems, 27(3):515-526, 2019.
  3. D. Wu, H-T Zhang* and J. Huang*, “A Constrained Representation Theorem for Well-Shaped Interval Type-2 Fuzzy Sets, and the Corresponding Constrained Uncertainty Measures,” IEEE Trans. on Fuzzy Systems, 27(6):1237-1251, 2019. (IEEE CIS Publication Spotlight)
  4. D. Wu*, “A Reconstruction Decoder for Computing with Words,” Information Sciences, 255:1-15, 2014.
  5. D. Wu and J. M. Mendel, “Linguistic Summarization Using IF-THEN Rules and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 19(1):136-151, 2011.
  6. D. Wu and J.M. Mendel, “Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System,” IEEE Trans. on Fuzzy Systems, 18(3):441-460, 2010.
  7. D. Wu and J. M. Mendel, “Perceptual reasoning for perceptual computing: A similarity-based approach,” IEEE Trans. on Fuzzy Systems, 17(6):1397-1411, 2009.
  8. D. Wu and J. M. Mendel, “A Comparative Study of Ranking Methods, Similarity Measures and Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 179(8):1169-1192, 2009. (ESI Highly Cited Paper; Top 25 Hottest Article)
  9. D. Wu and J. M. Mendel, “Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 15(6):1145-1161, 2007.
  10. D. Wu and J. M. Mendel, “Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 177:5378-5393, 2007.

发明专利：

  1. A. Kumar, B. Ellis, Z. Wan, C. Pierce, M. Dokucu, D. Wu and S. Balram, Dynamic monitoring, diagnosis, and control of cooling tower systems, WO2015012832, 1/29/2015.
  2. S. Gustfason and D. Wu, Influencer analyzer platform for social and traditional media document authors, US20150348216, 12/3/2015.
  3. J. Reimann, C. Johnson, D. Wu, S. Evans, and R. Cheinhample, A. Pandey, System and method using generative model to supplement incomplete industrial plant information, US20160004794, 1/7/2016.
  4. A. Can, E. Bas, D. Wu, J. Yu, and L. Wahrmund, Expert guided knowledge acquisition system for analyzing seismic data, WO2017152119, 8/9/2017.
  5. X. Gui, B. Shi, H. Liu and D. Wu, Target Positioning And Tracking System, Device, And Positioning And Tracking Method, WO2017084240, 5/27/2017.
  6. 伍冬睿，石振华，一种用于恒河猴眼动决策解码的多视图学习方法和系统，201910586165.4， 2020-07-10
  7. 伍冬睿，谭显烽，一种适用于云计算系统的多任务处理方法，201811434588.6， 2020-07-10
  8. 伍冬睿，孟璐斌，一种基于EEG的脑机接口回归系统白盒目标攻击方法，201910896360.7， 2020-08-04
  9. 伍冬睿，张潇，一种针对以卷积神经网络为基础的EEG脑机接口的攻击方法，201811543220.3，2020-11-10
  10. 伍冬睿，何赫，一种用于脑机接口校准的异构标签空间迁移学习方法，201911100099.1，2021-08-04

在线讲座：

  1. 脑机接口中的机器学习, 5/12/2020.
  2. Affective Computing, 12/6/2011.

媒体报道：

  1. TechXplore关于脑机接口对抗攻击的报道
  2. 中新网关于脑机接口的报道
  3. 长江日报关于脑机接口的报道
  4. 华中大新闻网关于脑机接口的报道
  5. 华中大新闻网关于脑机接口的报道
  6. 华中大新闻网关于IEEE SMC青年科学家奖的报道