Qiushi Academy for Advanced Studies, Zhejiang University

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Yueming Wang

Professor

Yueming Wang received the Ph.D. degree in Computer Science from Zhejiang University in 2007. From 2007 to 2010, he was a postdoctoral fellow in the Microsoft Research Asia (MSRA) and the Department of Information Engineering, the Chinese University of Hong Kong. He was an associate professor in QAAS, Zhejiang University from 2010, and is a professor since 2016. His research interests are in the area of brain-machine interfaces, data mining, and pattern recognition. More specifically, he studies the problems in connecting biological systems to computer systems to generate more powerful cyborg intelligent systems via BMIs.

He has published more than 40 papers in the prestigious journals and conferences in his research area, including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Image Processing (TIP), Journal of Neural Engineering, and IEEE Computational Intelligence Magazine (CIM). He has gotten the funding support from the National Natural Science Foundation of China (2), the National 863 Program of China (1), Zhejiang Provincial Natural Science Foundation of China (Key Program, 1), and the Ph.D. Programs Foundation of Ministry of Education of China (1). The work on rat cyborg was nominated for the Annual BCI Research Award at BCI Meeting 2016. The work on bidirectional brain-machine integration won the WUWENJUN Innovative Research Award, 2016.

He is an IEEE member, CCF member, and CAAI member. He was a program chair of IEEE Computational Intelligence Society (CIS) Summer School on Neuromorphic and Cyborg Intelligent Systems, 2015 and a program chair of the Forum of Cyborg Intelligence in China National Computer Congress, 2013. He serves as the under-secretary-general of the cyborg intelligence branch, Chinese Association for Artificial Intelligence. He acts as a reviewer for the TPAMI, IJCV, TIFS, TCSVT, and Pattern Recognition. He also acts as a program committee member for ICCV’09, ICCV’11, ICCV’13, ICCV’15 and CVPR’13, CVPR’14, CVPR’15, CVPR’16.

Selected publications:

2019

1. Yu Qi, Bin Liu, Yueming Wang*, Gang Pan, Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019.

2. Lu, Minlong; Li, Ze Nian; Wang, Yueming; Pan, Gang. Deep Attention Network for Egocentric Action Recognition, IEEE Transactions on Image Processing, 28(8):3703-3713, 2019.

3. Qi, Yu; Wang, Hanwen; Liu, Rui; Wu, Bian; Wang, Yueming; Pan, Gang, Activity-dependent neuron model for noise resistance, Neurocomputing, 357:240-247, 2019.

4. Yu Qi, Kang Lin, Yueming Wang*, Feixiao Ren, Qi Lian, Shuang Wang, Hongjie Jiang, Junming Zhu, Yiwen Wang, Zhaohui Wu, Gang Pan, Epileptic Focus Localization via Brain Network Analysis on Riemannian Manifolds, IEEE Transactions on Neural Systems & Rehabilitation Engineering, 27(10): 1942 - 1951, 2019.

5. Zheng, Yongte; Jiang, Zifan; Ping, An; Zhang, Fang; Zhu, Junming; Wang, Yueming; Zhu, Wentao; Xu, Kedi, Acute Seizure Control Efficacy of Multi-Site Closed-Loop Stimulation in a Temporal Lobe Seizure Model, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3):419-428, 2019.

6. Jiacheng Zhang, Kedi Xu, Shaomin Zhang, Yueming Wang, Nenggan Zheng, Gang Pan, Weidong Chen, Zhaohui Wu, Xiaoxiang Zheng. Brain-Machine Interface-Based Rat-Robot Behavior Control. Neural Interface: Frontiers and Applications. Springer, Singapore, 2019: 123-147.

2018

1. Yueming Wang, Kang Lin, Yu Qi, Qi Lian, Shaozhe Feng, Gang Pan, Zhaohui Wu, Estimating Brain Connectivity with Vary-length Time Lags Using Recurrent Neural Network, Transactions on Biomedical Engineering, 65(9):1953 - 1963, 2018 (Featured Article).

2. Gang Pan, Jiajun Li, Yu Qi, Hang Yu, Junming Zhu, Xiaoxiang Zheng, Yueming Wang, and Shao-Min Zhang, Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks, Frontiers in Neuroscience, 12(555), 2018.

3. Yu Qi, Jiangrong Shen, Yueming Wang, Huajin Tang, Hang Yu, Zhaohui Wu, Gang Pan, Jointly Learning Network Connections and Link Weights in Spiking Neural Networks, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), 2018.

4. Kang Lin, Yu Qi, Shaozhe Feng, Qi Lian, Gang Pan, and Yueming Wang, EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.

2017

1. Lei Jiang, Yun Wang, Bangyu Cai, Yueming Wang*, and Yiwen Wang, Spatial-Temporal Feature Analysis on Single-Trial Event-related Potential for Rapid Face Identification, Frontiers in Neuroscience, 11(106), 2017.

2. Jiangrong Shen, Kang Lin, Yueming Wang, Gang Pan, Character Recognition from Trajectory by Recurrent Spiking Neural Networks, 39th Annual International IEEE EMBS Conference, 2017.

2016

1. Z. Hu, G. Pan, Y. Wang*, Z. Wu, ’Sparse Principal Component Analysis via Rotation and Truncation’, IEEE Transactions on Neural Networks and Learning Systems, 27(4): 875-890, 4/2016.

2. Y. Wang, Y. Qi, Y. Wang*, Z. Lei, X. Zheng, G. Pan, ’Delving into α-stable distribution in noise suppression for seizure detection from scalp EEG’, J Neural Eng., 13(5):056009, 2016.

3. Kang Lin, Yueming Wang, Kedi Xu, Junming Zhu, Jianmin Zhang, Xiaoxiang Zheng, Localizing seizure onset zone by convolutional transfer entropy from iEEG, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016.

4. Tan, Min; Hu, Zhenfang; Wang, Baoyuan; Zhao, Jieyi; Wang, Yueming, “Robust object recognition via weakly supervised metric and template learning”, Neurocomputing, 181:96-107, 2016.

2015

1. Y. Wang, M. Lu, G. Pan*, L. Tian, K. Xu, X. Zheng, Z. Wu, ’A Visual Cue-guided Rat Cyborg for Automatic Navigation’, IEEE Computational Intelligence Magazine, 10(2):42-52, 4/2015.

2. Y. Zhang, G. Pan*, K. Jia, M. Lu, Y. Wang, Z. Wu, ’Accelerometer-Based Gait Recognition by Sparse Representation of Signature Points with Clusters’, IEEE TRANSACTIONS ON CYBERNETICS, 45(9): 1864-1875, 9/2015.

3. Y. Wang, L. Jiang, Y. Wang, B. Cai, Y. Wang*, W. Chen, X. Zheng, ’An Iterative Approach for EEG based Rapid Face Search: A Refined Retrieval by Brain-Computer Interfaces’, IEEE Transactions on Autonomous Mental Development, 7(3): 211-222, 6/2015.

2014

1. Y. Wang, Y. Qi, J. Zhu, J. Zhang, Y. Wang, X. Zheng, Z. Wu, ’A Cauchy-based State-space Model for Seizure Detection in an EEG Monitoring System’, IEEE Intelligent Systems, 30(1): 6-12, 2014.

2. Y. Qi, Y. Wang*, J. Zhang, J. Zhu, ’Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection’, BioMed Research International, 2014 (Online, http://www.hindawi.com/journals/bmri/2014/703816/, IF: 2.706).

3. Y. Qi, F. Ma, T. Ge, Y. Wang*, J. Zhu, J. Zhang, X. Zheng, Z. Wu, ’A Bidirectional Brain-Computer Interface for Effective Epilepsy Control’, Journal of Zhejiang University-Science C (Computers & Electronics), vol. 15, no. 10, pp. 839-847, 2014.

4. M. Tan, G. Pan, Y. Wang*, Y. Zhang, and Z. Wu, ’L1-norm Latent SVM for Compact Features in Object Detection’, Neurocomputing, vol. 139, no. 2, pp. 56-64, 2014.

5. R. Zhang, G. Pan, Y. Wang, and Z. Hu, ’High-fidelity Compression of Extracellular Recordings from Motor Cortex’, IJCNN, 2014.

6. L. Zhou, Y. Qi, Y. Wang, G. Pan, Y. Wang, X. Zheng, and Z. Wu, ’Decoding Motor Cortical Activities of Monkey: a Dataset’, IJCNN, 2014

7. Y. Qi, Y. Wang, and X. Zheng, ’Robust Feature Learning by Stacked Autoencoder with Maximum Correntropy Criterion’, ICASSP, 2014

8. S. Guo, S. Chen, Q. Zhang, Y. Wang, K. Xu, and X. Zheng, ’Optogenetic Activation of the Excitatory Neurons Expressing CaMKIIa in the Ventral Tegmental Area Upregulates the Locomoter Activity of Free Behaving Rats’, BioMed Research International, 2014 (Online).

2013

1. G. Pan, X. Zhang, Y. Wang*, Z. Hu, X. Zheng, Z. Wu, “Establishing Point Correspondence of 3D Faces via Sparse Facial Deformable Model.” IEEE Transactions on Image Processing (TIP), vol. 22, no. 11, pp. 4170-4181, 2013.

2. K. Xu, Y. Wang, Y. Wang, F. Wang, Y. Hao, S. Zhang, Q. Zhang, W. Chen, X. Zheng. ’Local-learning-based neuron selection for grasping gesture prediction in motor Brain-Machine Interfaces’. Journal of Neural Engineering, 10(2):026008, 2013.

3. Yuting Zhang, Yueming Wang*, Gang Pan, and Zhaohui Wu. “Efficient Computation of Histograms on Densely Overlapped Polygonal Regions”. Neurocomputing, 118:141-149, 2013.

4. Yueming Wang, Minlong Lu, Zhaohui Wu, Liwen Tian, Weidong Hua, Xiaoxiang Zheng, Kedi Xu, Gang Pan, Ratbot: A Rat “Understanding” What Humans See, International Workshop on Intelligence Science, in conjunction with IJCAI-2013, Beijing, China, August 4, 2013.

5. Minlong Lu, Yueming Wang*, Gang Pan. Generating Fluent Tubes in Video Synopsis. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.

2012

1. Mahdi Hashemzadeh, Gang Pan*, Yueming Wang, Min Yao, Jian Wu, ’Combining Velocity and Location-Specific Spatial Clues in Trajectories for Counting Crowded Moving Objects’, International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), Vol. 27, Issue 02, 2012.

2. Xiaobo Zhang, Yueming Wang, Gang Pan, “3D Facial Landmark Localization via a Local Surface Descriptor HoSNI”, Sino-foreign-interchange workshop on Intelligence Science&Intelligent Data Engineering, 2012.

3. Min Tan, Yueming Wang*, Gang Pan, “Feature Reduction for Efficient Object Detection via L1-norm Latent SVM”, Sino-foreign-interchange workshop on Intelligence Science&Intelligent Data Engineering, 2012.

4. Yu Qi, Yueming Wang, Xiaoxiang Zheng,” Efficient Epileptic Seizure Detection by a Combined IMF-VoE Feature”, 34th Annual International IEEE EMBS Conference, 2012.

5. Yipeng Yu, Dan He, Weidong Hua, Shijian Li, Yu Qi, Yueming Wang, Gang Pan*, ’FlyingBuddy2: A Brain-controlled Assistant for the Handicapped’, The 14th ACM International Conference on Ubiquitous Computing (Ubicomp’12), Poster/Video, Pittsburgh, PA, USA, September 5-8, 2012.

2011

1. Y. Wang, G. Pan, and J. Liu, ’A deformation model to reduce the effect of expressions in 3D face recognition’, The Visual Computer, vo. 27, no. 5, pp. 333-345, 2011.

2. Gang Pan, Lin Sun, Zhaohui Wu, Yueming Wang*, ’Monocular Camera-based Face Liveness Detection by Combining Eyeblink and Scene Context’, Journal of Telecommunication Systems, 47(3-4):215-225, Springer-Verlag, August 2011.

3. X. Zheng, K. Xu, Y. Wang, S. Zhang, T. Zhao, Y. Wang, and W. Chen, “Comparisons between Linear and Nonlinear Methods for Decoding Motor Cortical Activities of Monkey”, 33th Annual International IEEE EMBS Conference, 2011.

Before 2010

1. Y. Wang, J. Liu, and X. Tang, ’Robust 3D Face Recognition by Local Shape Difference Boosting,’ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1858-1870, 2010. (IEEE TPAMI).

2. S. Chen, L. Cao, Y. Wang, J. Liu, and X. Tang, ’Image Segmentation by MAP-ML Estimations,’ IEEE Transactions on Image Processing, vol. 19, no. 9, pp. 2254-2264, 2010. (IEEE TIP).

3. Y. Wang, G. Pan, and Z. Wu, ’survey of 3D Face Recognition,’ Journal of Computer-Aided Design & Computer Graphics, 2008, 20(7):819–829 (in Chinese).

4. Y. Wang, G. Pan, and Z. Wu, ’New Method for Facial Feature Detection based on Range Data,’ Journal of Zhejiang University: Engineering Science, 2005 (in Chinese).

5. Han, G. Pan, Y. Wang, and Z. Wu, ’3D Nose: A Novel Biometrics,’ Journal of Computer-Aided Design & Computer Graphics, 2008, 20(1):38-42 (in Chinese).

6. B. Gong, Y. Wang*, J. Liu, and X. Tang, ’Automatic Expression Recognition on a Single 3D Face by Exploring Shape Deformation,’ in Proceedings of ACM International Conference on Multimedia, 2009.

7. Y. Wang, X. Tang, J. Liu, G. Pan, and R. Xiao, ’3D Face Recognition by Local Shape Difference Boosting,’ in Proceedings of European Conference on Computer Vision (ECCV), 2008.

8. Y. Wang, G. Pan, and Z. Wu, ’3D Face Recognition in Presence of Expression: a Guidance-based Constraints Deformation Approach,’ in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2007.

9. G. Pan, S. Han, and Z. Wu, and Y. Wang, ’Super-Resolution of 3D Face,’ in Proceedings of European Conference on Computer Vision (ECCV), 2006.

10. Y. Wang, G. Pan, Y. Yang, D. Li, and Z. Wu, ’Enhancing 3D Face Recognition by Combination of Voiceprint,’ in Proceedings of International Conference on Computational Science (ICCS), Lecture Notes in Computer Science, 2006.

11. Y. Wang, G. Pan, Z. Wu, Y. Wang, ’Exploring Facial Expression Effects in 3D Face Recognition using Partial ICP,’ in Proceedings of Asian Conference on Computer Vision (ACCV), 2006.

12. S. Peng, G. Pan, S. Han, and Y. Wang, ’Hallucinating 3D Face,’ in Proceedings of Asian Conference on Computer Vision(ACCV), 2006.

13. G. Pan, Y. Wang, Y. Qi, and Z. Wu, ’Finding Symmetry Plane of 3D Face Shape,’ in Proceedings of International Conference on Pattern Recognition (ICPR), 2006.

14. G. Pan, S. Han, Z. Wu, and Y. Wang, ’3D Face Recognition using Mapped Depth Images,’ in Proceedings of IEEE Workshop on Face Recognition Grand Challenge Experiments (FRGC), in conjunction with CVPR 2005.

15. Z. Wu, Y. Wang, G. Pan, ’3D Face Recognition using Local Shape Map,’ in Proceedings of IEEE International Conference on Image Processing (ICIP), 2004.

16. Y. Wang, G. Pan, Z. Wu, and S. Han, ’Sphere-Spin-Image: A Viewpoint-invariant Surface Representation for 3D Face Recognition,’ in Proceedings of International Conference on Computational Science (ICCS), Lecture Notes in Computer Science, 2004.

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