刘爱萍 导师介绍
姓名
刘爱萍



工作单位 中国科学技术大学先进技术研究院/中国科学技术大学信息科学技术学院

学位/职称
副研究员

办公室电话


Email
aipingl@ustc.edu.cn

教育背景
2011.09-2016.05英属哥伦比亚大学电子与计算机工程系Ph.D.
2009.09-2011.08英属哥伦比亚大学电子与计算机工程系M.Sc.
2005.09-2009.06中国科学技术大学电子科学与技术系B.Sc.

研究方向
脑信息处理；医学大数据分析；脑影像分析；信号处理与机器学习；大脑刺激调节；神经退行性疾病辅助诊断

任职经历
l2018-至今 中国科学技术大学 特任研究员
l2017.08-至今 英属哥伦比亚大学太平洋帕金森病研究中心 Postdoctoral Researcher
l2017.01-2017.07合肥工业大学电子科学与应用物理学院Assistant Professor
l2016.06-2016.12英属哥伦比亚大学电子与计算机工程系Postdoctoral Researcher

获得荣誉、奖项
2015国家优秀自费留学生奖学金 (全球500名/年)
2012 UBC Four Year Fellowship (FYF)
2009中国科学技术大学优秀本科生毕业论文
2008华为奖学金
2007 中国科学技术大学优秀学生奖学金
2006 张宗植科技奖学金
2004全国生物奥林匹克竞赛江苏省一等奖

主持、参与项目
国家重点研发计划“智能机器人”重点专项，“生-机智能交互与生机电一体化机器人技术”项目子课题，2017YFB**，2017/09-2020/08，负责人
国家自然科学基金青年基金，“基于动态连接特性的功能脑区划分及其在帕金森病研究中的应用”, **，2018/01-2020/12，负责人
安徽省自然科学基金青年基金，“状态相关的大脑功能区域划分方法研究”，**QF184, 2018/07-2020/06，负责人
加拿大Mitacs Globalink国际合作研究项目，“Brain dynamics in neurodegenerative”，IT06351，2016，负责人

论文、著作
1.Aiping Liu, S.-J. Lin, T. Mi, P. Chan, X. Chen, Z. J. Wang and M. J. McKeown, “Decreased subregional specificity of the putamen in Parkinson’s Disease revealed by dynamic connectivity-derived parcellation (to appear)”, NeuroImage: Clinical, to appear, 2018.
2.Aiping Liu, X. Chen, X. Dan, M. J. McKeown and Z. J. Wang, “A Combined Static and Dynamic Model for Resting State fMRI Brain Connectivity Networks: Application to Parkinson’s Disease”, IEEE Journal of Selected Topics in Signal Processing, vol. 10, no.7, pp. 1172-1181, 2016.
3.Aiping Liu, X. Chen, M. J. McKeown and Z. J. Wang, “A Sticky Weighted Regression Model for Time-Varying Resting State Brain Connectivity Estimation”, IEEE Transactions on Biomedical Engineering, vol. 62, no.3, pp. 501–510, 2015.
4.Aiping Liu, X. H. Chen, Z. J. Wang, Q. Xu, S. Appel-Cresswell and M. J. McKeown, “A Genetically Informed, Group fMRI Connectivity Modeling Approach: Application to Schizophrenia”, IEEE Transactions on Biomedical Engineering, vol.61, no.3, pp.946-956, 2014.
5.Aiping Liu, Z. J. Wang and Y. Hu, “Network modeling and analysis of lumbar muscle surface EMG signals during flexion–extension in individuals with and without low back pain”, volume 21, pp. 913-921, Journal of Electromyography and Kinesiology, 2011.
6.Aiping Liu, J. Li, Z. J. Wang, and M. J. McKeown, “A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 967380, 14 pages, 2012.
7.S. Lee, Aiping Liu*, Z. J. Wang and M. J. McKeown, “Abnormal phase coupling in Parkinson’s disease and normalization effects of subthreshold vestibular stimulation”, Frontiers in Human Neuroscience, vol. 13, pp. 118, 2019.
8.J. Cai, Aiping Liu*, T. Mi, S. Garg, W. Trappe, M. J. McKeown and Z. Jane Wang, “Dynamic Graph Theoretic Analysis of Functional Connectivity in Parkinson’s Disease: The Importance of Fiedler value”, IEEE Journal of Biomedical and Health Informatics, to appear, 2018.
9.X. Chen, Aiping Liu*, H. Poizner, M. J. Mckeown and Z. J. Wang, “An EEMD-IVA Framework for Concurrent Multidimensional EEG and Unidimensional Kinematic Data Analysis”, IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 2187-2198, 2014.
10.X. Chen, X. Xu, Aiping Liu*, M. J. McKeown and Z. J. Wang, “The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings”, IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, 2018.
11.X. Chen,Aiping Liu*, J. Chiang, Z. J. Wang, M. J. McKeown, R. K. Ward, “Removing Muscle Artifacts from EEG Data: Multichannel or Single-Channel Techniques?” IEEE Sensors Journal, vol. 16, no. 7, pp. 1986-1997, 2016. (通讯作者，JCR Q1)
12.Y. Zhang, Aiping Liu?, S. N. Tan, M. J. McKeown, Z. J. Wang, “Connectivity-based parcellation of functional SubROIs in putamen using a sparse spatially regularized regression model”, Biomedical Signal Processing and Control, Volume 27, pp. 174-183, May 2016.
13.X. Chen, Aiping Liu*, Q. Chen, Y. Liu, L. Zou, M. J. McKeown, “Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics”, Computers in Biology and Medicine, vol. 88, pp. 1-10, 2017.
14.J. Cai, S. Lee, F. Ba, S. Garg, L. J. Kim, Aiping Liu*, D. Kim, Z. J. Wang and M. J. McKeown, “Galvanic Vestibular Stimulation (GVS) Augments Deficient Pedunculopontine Nucleus (PPN) Connectivity in Mild Parkinson's Disease: fMRI Effects of Different Stimuli”, Frontiers in Neuroscience, vol. 12, pp. 101, 2018.
15.X. Wang, J. Liu,Y. Cheng, E. Chen and Aiping Liu, "Dual Hypergraph Regularized PCA for Biclustering of Tumor Gene Expression Data", IEEE Transactions on Knowledge and Data Engineering, to appear, 2018.
16.N. Virji-Babul, C. Hilderman, N. Makan, Aiping Liu, J. Smith-Forrester, C. Franks and Z. J. Wang, “Changes in functional brain networks following sports related concussion in adolescents”, Journal of neurotrauma, vol. 31, no. 23, pp. 1914-1919, 2014.
17.X. Chen, Aiping Liu, H. Peng and R. K. Ward, “A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG”, Sensors, vol. 14, no. 10, pp. 18370-18389, 2014.
18.N. Baradaran, S. N. Tan, Aiping Liu, A. Ashoori, S. J. Palmer, Z. J. Wang, M. Oishi and M. J. McKeown, “Parkinson’s disease rigidity: relation to brain connectivity and motor performance”, Frontiers in neurology, vol. 4, no. 67, 2013.
19.N. Rotem-Kohavi, C. G. Hilderman, Aiping Liu, N. Makan, Z. J. Wang and N. Virji-Babul, “Network analysis of perception-action coupling in infants”, Frontiers in human neuroscience, vol. 8, no. 209, 2014.
20.J. Cheng, F. Wei, C. Li, Y. Liu, Aiping Liu, X. Chen, “Position-independent gesture recognition using sEMG signals via canonical correlation analysis,” Computers in Biology and Medicine, to appear, 2018.
21.J. Cheng, X. Chen, Aiping Liu, H. Peng, “A Novel Phonology and Radical-coded Chinese Sign Language Recognition Framework using Accelerometer and Surface Electromyography Sensors”, Sensors, vol. 15, no. 9, pp. 23303-23324, 2015.
22.X. Chen, Aiping Liu, Z. J. Wang, H. Peng, “Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis”, Journal of Applied Mathematics, vol. 2013, Article ID 401976, 11 pages, 2013.
23.会议论文
24.X. Xu, Aiping Liu, X. Chen, “A Novel Few-Channel Strategy for Removing Muscle Artifacts from Multichannel EEG Data”, Signal and Information Processing (GlobalSIP), 2017 IEEE Global Conference on, pp. 976-980, Nov. 2017.
25.Aiping Liu, X. Chen, X. Dan, M. J. McKeown and Z. J. Wang, “Joint Time Invariant and Time Dependent Brain Connectivity Network Estimation”, 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, May 2016.
26.Y. Zhang, Aiping Liu, S. N. Tan, M. J. McKeown and Z. J. Wang, “Connectivity-Based Parcellation of Putamen using Resting State fMRI Data”, 12th IEEE International Symposium on Biomedical Imaging (ISBI), Pages 34-37, April , 2015.
27.Aiping Liu, X. Chen, Z. J. Wang and M. J. McKeown, “Time varying brain connectivity modeling using FMRI signals”, Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pp. 2089-2093, 4-9 May 2014.
28.Aiping Liu, J. Li, Z. J. Wang and M. J. McKeown, “An FDR-controlled, exploratory group modeling for assessing brain connectivity”, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.558-561, 2-5 May 2012.