Prof. Dongrui Wu

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
 

Deputy Director, Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, China

Email: drwu@hust.edu.cn

Homepage：https://sites.google.com/site/drwuhust/home

Blog：http://blog.sciencenet.cn/u/drwuHUST (Chinese)

Research Interests：Brain-Computer Interfaces, Machine Learning, Smart Healthcare, Computational Intelligence, Affective Computing

Google Scholar Profile

Education

PhD, Electrical Engineering, University of Southern California, Los Angeles, CA May 2009

M.Eng, Electrical Engineering, National University of Singapore, Singapore June 2006

B.E, Automation, University of Science and Technology of China, Hefei, Anhui, China July 2003

Major Honors & Awards

  1. First Prize, China Brain-Computer Interface Competition    2021
  2. Best Paper Award, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2021
  3. USERN Prize in Formal Sciences, USERN   2020
  4. First Prize, China Brain-Computer Interface Competition    2020
  5. First Prize, China Brain-Computer Interface Competition    2019
  6. Best Associate Editor, IEEE Transactions on Human-Machine Systems    2018
  7. Early Career Award, IEEE Systems, Man and Cybernetics (SMC) Society    2017
  8. 13th Annual National Academies Keck Futures Initiative (NAKFI) conference     2015
  9. German-American Frontiers of Engineering, National Academy of Engineering    2015
  10. Early Career Award, North American Fuzzy Information Processing Society (NAFIPS)  2014
  11. Outstanding Paper Award, IEEE Transactions on Fuzzy Systems    2014
  12. Heidelberg Laureate Forum (Abel/Fields/Turing), Heidelberg, Germany     2013
  13. Outstanding PhD Dissertation Award, IEEE Computational Intelligence Society     2012 
  14. Best Student Paper Award, IEEE International Conference on Fuzzy Systems     2005

Other Honors & Awards

  1. 3rd Prize, Chinese Automation Congress Outstanding Paper Award   2019

  2. 1st Prize, IEEE WCCI Competition: Open Source Intelligence Discovery for Cybersecurity Threat 2018

  3. 3rd Prize, Shenzhen Int’l Competition for Medical and Health Big Data Innovative Application    2018

  4. Hanxiang Early Career Award Honorable Mention, Chinese Psychological Society    2018

  5. Best Student Paper Award Finalist, 24th International Conference on Neural Information Processing (ICONIP)    2017

  6. Best Paper Award Finalist, IEEE Brain Initiative     2016

  7. Most Influential Paper Award Finalist, IEEE Transactions on Affective Computing      2015

  8. CIO 100 Award (Fleet Optimizer for GE Capital), CIO Magazine    2014

  9. Senior Member, Institute of Electrical and Electronics Engineers (IEEE)  2014

  10. Above and Beyond Awards (10 times), GE Global Research, Niskayuna, NY 2010-2014

  11. CIO 100 Award (TrueSense for GE Water), CIO Magazine    2012

  12. Viterbi Best Thesis Award Nominee, University of Southern California     2009

  13. Walter Karplus Summer Research Grant, IEEE Computational Intelligence Society     2007

  14. Travel Grant, IEEE International Conference on Fuzzy Systems, London, UK     2007

Selected Publications

Brain-Computer Interfaces:

  1. 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.

  2. W. Zhang and D. Wu*, “Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5), pp. 1117-1127, 2020. (Python)

  3. 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), pp. 1091-1108, 2020. (Matlab; IEEE Brain)

  4. H. He and D. Wu*, “Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach,” IEEE Trans. on Biomedical Engineering, 67(2), pp. 399-410, 2020. (Matlab; IEEE Brain)

  5. 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), pp. 2263-2273, 2019. (IEEE TNSRE Cover Article; Python) 

  6. 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), pp. 814-825, 2019. (Python)

  7. 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), pp. 771-781, 2018.

  8. 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), pp. 2157-2168, 2017. 

  9. 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), pp. 1522-1535, 2017.

  10. D. Wu*, “Online and Offline Domain Adaptation for Reducing BCI Calibration Effort,” IEEE Trans. on Human-Machine Systems, vol. 47, no. 4, pp. 550-563, 2017. (ESI Highly Cited Paper)

  11. 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), pp. 1125-1137, 2016.

 

Affective Computing:

  1. D. Wu and J. Huang, “Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,” IEEE Trans. on Affective Computing, 2020, in press.

  2. 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), pp. 109-118, 2010. (Top Accessed Article; IEEE Trans. on Affective Computing Most Influential Paper Award Finalist)

 

Machine Learning:

  1. 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, 2020.

  2. 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.

  3. 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, 2020, accepted. (Matlab; Python)

  4. 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), pp. 1003-1015, 2020. (Matlab)

  5. D. Wu* and J.M. Mendel, “Patch Learning,” IEEE Trans. on Fuzzy Systems, 2020, accepted. (Matlab)

  6. 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, 2020, accepted.

  7. D. Wu* and X. Tan, “Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization,” IEEE Trans. on Fuzzy Systems, 28(6), pp. 1050-1061, 2020. (Matlab)

  8. D. Wu*, C-T Lin and J. Huang*, “Active Learning for Regression Using Greedy Sampling,” Information Sciences, vol. 474, pp. 90-105, 2019.

  9. D. Wu, “Pool-based sequential active learning for regression,” IEEE Trans. on Neural Networks and Learning Systems, 30(5), pp. 1348-1359, 2019.

 

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, 2020.

  2. D. Wu* and J.M. Mendel, “Recommendations on Designing Practical Interval Type-2 Fuzzy Systems“, Engineering Applications of Artificial Intelligence, 95, pp. 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), pp. 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), pp. 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), pp. 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), pp. 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, pp. 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), pp. 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), pp. 503-516, 2006.

  10. D. Wu and W. W. Tan, “Type-2 FLS Modeling Capability Analysis,” IEEE Int’l Conf. on Fuzzy Systems, pp. 242–247, 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), pp. 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), pp. 1237-1251, 2019. (IEEE CIS Publication Spotlight)

  4. D. Wu*, “A Reconstruction Decoder for Computing with Words,” Information Sciences, 255, pp. 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), pp. 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), pp. 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), pp. 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), pp. 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), pp. 1145-1161, 2007.

  10. D. Wu and J. M. Mendel, “Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 177, pp. 5378-5393, 2007.

Theses:

  1. D. Wu, “Intelligent Systems for Decision Support,” PhD Dissertation, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, March 2009. (2009 Viterbi Best Dissertation Nomination; 2012 IEEE Computational Intelligence Society Outstanding Dissertation Award)

Patents:

  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.

 

Talks:

2. Affective Computing, 12/6/2011.

Services

Memberships:

  • Senior Member, IEEE, 2014-.
  • Executive Committee Member, Association for the Advancement of Affective Computing (AAAC), 2011-2017.
  • Board of Directors, North America Fuzzy Information Processing Society (NAFIPS), 2016-2019.
  • Distinguished Lecturer, North America Fuzzy Information Processing Society (NAFIPS), 2016-.

Editorship:

Committee Service:

  • Reviewer, Heidelberg Laureate Forum, 2018-2020.
  • Chair, IEEE Computational Intelligence Society Wuhan Chapter, 2020-.
  • Vice Chair, IEEE Systems, Man and Cybernetics Society Wuhan Chapter, 2020-.
  • Member, Brain-Machine Interface Systems Technical Committee, IEEE Systems, Man and Cybernetics Society, 2014-.
  • Chair, Affective and Physiological Computing Task Force, Emergent Technologies Technical Committee, IEEE Computational Intelligence Society, 2012-.
  • Vice Chair, Computing with Words Task Force, Fuzzy Systems Technical Committee, IEEE Computational Intelligence Society, 2012-.
  • Technical Committee Member, 1) Fuzzy Systems, 2) Emergent Technologies, 3) Intelligent Systems Applications, IEEE Computational Intelligence Society, 2012-.
  • Member, Education Task Force, Emergent Technologies Technical Committee, IEEE Computational Intelligence Society, 2012.
  • Vice Chair, 1) Computing with Words Task Force, 2) Social Media Subcommittee, IEEE Computational Intelligence Society, 2011.
  • Member, 1) Computing with Words Task Force, 2) GOLD (Graduates Of the Last Decade) Sub-Committee, IEEE Computational Intelligence Society, 2010.

Tutorials:

  • Transfer Learning for Brain-Computer Interfaces, 27th Int’l Conf. on Neural Information Processing (ICONIP), Bangkok, Thailand, November 2020. 
  • Signal processing and machine learning for brain-computer interface, 24th Int’l Conf. on Neural Information Processing (ICONIP), Guangzhou, Guangdong, November 2017.

Keynotes/Webinars:

  • Machine learning in brain-computer interfaces, Int’l Conf. on Artificial Intelligence in China, Changbaishan, July 2020.
  • Patch Learning, Int’l Conf. on Neural Computing for Advanced Applications, Shenzhen, China, July 2020.
  • 脑机接口中的机器学习, 中国自动化学会，5/12/2020.
  • Driver drowsiness estimation from EEG signals, Keynote speech, Workshop on Machines with Emotions: Affect Modeling, Evaluation, and Challenges in Intelligent Cars, IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Macau, China, November 2019. 
  • Adversarial attacks in brain-computer interfaces, International Workshop on Understanding and Harnessing AdVersarial Examples, IEEE International Conference on Data Mining, Beijing, November 2019.
  • Adversarial attacks in brain-computer interfaces, 智能康复及人机工程会议，2019年11月
  • Optimize TSK Fuzzy Systems for Big Data Regression Problems， 主题演讲，上海人工智能大会，2019年8月
  • Fuzzy Logic and Signal Processing/Machine Learning for Brain-Computer Interface (BCI), Keynote Speech, Int’l Conf. on Soft Computing & Machine Learning, Wuhan, China, April 2019. 
  • Active Learning for regression, Plenary Talk, 2nd Int’l Conf. on Data Science and Business Analytics, Changsha, China, September 2018
  • Intelligent Human-Machine Interaction, 吉林大学未来科学论坛，2018年9月
  • Machine Learning for Fatigue Estimation and Affective Computing, 精神健康技术中英青年学者双边研讨会，济南，2018年9月.
  • Industrial design in the era of artificial intelligence, 主题演讲，无锡国际工业设计博览会，2017年9月.
  • Affective Computing, GE Global Research, 12/6/2011.

Conference/Symposium/Special Session Organization:

Proposal Evaluation:

  • Italian Ministry for Education, University and Research, Italy, 2018.
  • National Center of State Science and Technology Evaluation, Kazakhstan, 2017.
  • National Science Center, Poland, 2014.
  • National Center of State Science and Technology Evaluation, Kazakhstan, 2012-2013.
  • TEAM Program from European Structural Funds, Foundation for Polish Science, 2009.