Lecturer in Machine Learning
Dr Lu’s current research focuses on machine learning, brain imaging, and tensor analysis. His research also covers related areas such as big data, biomedical engineering, computer vision, and signal/image processing. His core expertise is tensor analysis and learning. He proposed several key tensor-based machine learning algorithms and lead-authored the book "Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data" (CRC Press, 2013). He contributed to applications including face/gait recognition for human identification and surveillance, video analytics for epileptic seizure detection, EEG classification for brain-computer interfaces, and fMRI data analysis and classification for brain state decoding and brain disease diagnosis.
Tensor analysis and learning – Tensor-based machine learning for extracting useful information directly from tensor representations of multidimensional data.
Examples include tensor extensions of PCA, LDA, ICA, CCA, Lasso, and Elastic Net.
Problems include learning with sparsity (spatial & spectral), uncertainty (probabilistic model), and incompleteness (missing data).
- Brain imaging – Whole-brain fMRI analysis and classification for brain state decoding and brain disease diagnosis.
- Neuroscience – Multi-trial neural data analysis and factorisation for insights, interpretation, and classification.
- Network analysis – Analysis of brain networks, social networks, and recommender systems.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data, Chapman & Hall/CRC Press Machine Learning and Pattern Recognition Series, Taylor and Francis, ISBN: 978-1-4398572-4-3, 2013.
Xiaofeng Xie, Zhu Liang Yu, Haiping Lu, Zhenghui Gu, and Yuanqing Li, "Motor Imagery Classification based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices", IEEE Trans. on Neural Systems & Rehabilitation Engineering, accepted in July 2016, to appear.
Haiping Lu, Yaozhang Pan, Bappaditya Mandal, How-Lung Eng, Cuntai Guan and D. W. S. Chan, "Quantifying Limb Movements in Epileptic Seizures through Color-based Video Analysis", IEEE Trans. on Biomedical Engineering, Vol. 60, No. 2, Pages 461-469, Feb. 2013.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "A Survey of Multilinear Subspace Learning for Tensor Data", Pattern Recognition, Vol. 44, No. 7, pp. 1540-1551, Jul. 2011.
Haiping Lu, How-Lung Eng, Cuntai Guan, K.N. Plataniotis and A.N. Venetsanopoulos, "Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting", IEEE Trans. on Biomedical Engineering, Vol. 57, No. 12, pp. 2936-2946, Dec. 2010.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning", IEEE Trans. on Neural Networks, Vol. 20, No. 11, Page: 1820-1836, Nov. 2009.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "Boosting Discriminant Learners for Gait Recognition using MPCA Features", EURASIP Journal on Image and Video Processing, Volume 2009, Article ID 713183, 11 pages, 2009. doi:10.1155/2009/713183.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "Uncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition", IEEE Trans. on Neural Networks, Vol. 20, No. 1, Page: 103-123, Jan. 2009.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Trans. on Neural Networks, Vol. 19, No. 1, Page: 18-39, Jan. 2008.
Haiping Lu, K.N. Plataniotis and A.N. Venetsanopoulos, "A Full-Body Layered Deformable Model for Automatic Model-Based Gait Recognition", EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 261317, 13 pages, 2008. doi:10.1155/2008/261317.
Haiping Lu, Alex C. Kot and Yun Q. Shi, "Distance-Reciprocal Distortion Measure for Binary Document Images", IEEE Signal Processing Letters, Vol. 11, No. 2, Page: 228-231, Feb. 2004.
- Program Committee Member: IJCAI-16, NIPS-16, AAAI-17, AISTATS-17, NIPS-17
- Member: IEEE Computational Intelligence Society Subcommittee for Outstanding PhD Dissertation Award, 2014, 2015.
- Journal Reviewers: IEEE TPAMI, TNN/TNNLS, TSMC-B/TCyb, TFS, TIP, TSP, TBME, TNSRE, TCSVT, TIFS, TPDS, TCC, TIE, J-STSP, SPL; JMLR, CVIU, NN, PR, SP, PRL, IVC...
Dr Lu joined the University of Sheffield as a Lecturer in Machine Learning in November 2016. He has a PhD degree in Electrical and Computer Engineering from the University of Toronto, Canada, in 2008, and M.Eng. and B.Eng. degrees in Electrical and Electronic Engineering from Nanyang Technological University, Singapore, in 2004 and 2001, respectively. Before joining Sheffield, he was an Assistant Professor with the Department of Computer Science, Hong Kong Baptist University, from 2013 to 2016, a Scientist with Institute for Infocomm Research, Singapore, from 2009 to 2013, and a post-doctoral fellow with the University of Toronto from 2008 to 2009.
He is the recipient of the 2013 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award, and an awardee (among 22 out of 359) of the 2014/15 Early Career Award by the Research Grants Council (RGC) of Hong Kong.
Department of Computer Science
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