Learning Sciences Institute Australia

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Jin Wang is a Research Statistician at the Learning Sciences Institute Australia. He used to work as a Research Associate at the School of Computer Science, University of Western Australia, and a Data Scientist at the CRC for Water Sensitive Cities. Jin Wang has extensive experience to develop data analytics models to analyze various datasets. His current research focuses on social statistics for learning science research. His research interest includes: survey data analysis, regression analysis, multivariate statistical analysis, time series analysis, structural equation modeling and multilevel models.

Research interests

  • Educational Data Mining & Analysis
  • Social Statistics
  • Data Visualization
  • Doctoral study in Data Mining and Machine Learning, specifically, time series mining

Publications

Articles

J. Wang, R. Cardell-Oliver, W. Liu, An Incremental Algorithm for Discovering Routine Behaviours from Water Meter Data, Journal of Knowledge-Based Systems (accepted).
R. Cardell-Oliver, J. Wang, H. Gigney, Smart Meter Analytics to Pinpoint Opportunities for Reducing Household Water Use, Journal of Water Resources Planning and Management, 2016.
J. Wang et al., Patient Admission Prediction based on a Pruned Fuzzy Min-Max Neural Network with Rule Extraction, Journal of Neural Computation and Applications, vol.26(2), pp. 277-289, 2015.
J. Wang, X. Sun, S. Nahavandi, A. Kouzani, Y. Wu and M. She, Multichannel Biomedical Time Series Clustering via Hierarchical Probabilistic Latent Semantic Analysis, Computer Methods and Programs in Biomedicine, Elsevier, vol. 117(2), pp. 238-246, 2014.
X. Sun, J. Wang, M. She, and L. Kong, Sparse representation with multi-manifolds analysis for texture classification from few training images, Image and Vision Computing, vol. 32(11), pp. 835-846, 2014.
J. Wang, M. She, S. Nahavandi, and A. Kouzani, Human identification from ECG signals via sparse representation of local segments, IEEE Signal Processing Letters, vol. 20(10), pp. 937-940, 2013.
J. Wang, P. Liu, M. She, S. Nahavandi and A. Kouzani, Bag-of-words representation for biomedical time series classi_cation, Biomedical Signal Processing and Control, Elsevier, vol. 8(6), pp. 634-644, 2013.
J. Wang, P. Liu, M. She, S. Nahavandi and A. Kouzani, Biomedical time series clustering based on non-negative sparse coding and topic model, Computer Methods and Programs in Biomedicine, Elsevier, vol. 111(3), pp. 629-641, 2013.
J. Wang, X. Sun, P. Liu, M. She, and L. Kong, Sparse representation of local spatial-temporal features with dimensionality reduction for motion recognition, Neurocomputing, Elsevier, vol. 115(4), pp. 150-160, 2013.
J. Wang, X. Sun, M. She, A. Kouzani and S. Nahavandi, Unsupervised mining of long time series based on latent topic model, Neurocomputing, Elsevier, vol. 103, pp. 93-103, 2013.
J. Wang, P. Liu, M. She, A. Kouzani and S. Nahavandi, Supervised learning probabilistic latent semantic analysis for human motion analysis, Neurocomputing, Elsevier, vol. 100, pp. 134-143, 2013.
X. Sun, J. Wang, M. She, and L. Kong, Scale invariant texture classification via sparse representation, Neurocomputing, Elsevier, vol. 122, pp. 338-348, 2013.
Y. Wu, R. Chen, J. Wang, X. Sun and M. She, Intelligent clothing for automated recognition of human physical activities in free-living environment, Journal of the Textile Institute, vol. 103(8), pp. 1-11, 2011.

Conferences

J. Wang, R. Cardell-Oliver, and W. Liu, E_cient Discovery of Recurrent Routine Behaviours in Smart Meter Time Series by Growing Subsequences, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Ho Chi Minh City, Vietnam, May 2015.
J. Wang, R. Cardell-Oliver, and W. Liu, Discovering routine behaviours in smart water meter data, IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information (IEEE ISSNIP 2015), Singapore, April 2015.
J. Wang, X. Sun, R. Chen, M. She and Q. Wang, Object Categorization via Sparse Representation of Local Features, IEEE International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 2012.
J. Wang, R. Chen, X. Sun, M. She and L. Kong, Generative models for automatic recognition of human daily activities from a single triaxial accelerometer, IEEE International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012.
X.Sun, J. Wang, R. Chen, M. She and L. Kong, Multi-scale local pattern co-occurrence matrix for textural image classification, IEEE International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012.
J. Wang, P. Liu, M. She, A. Kouzani and S. Nahavandi, Human action recognition based on pyramid histogram of oriented gradients, IEEE International Conference on Systems, Man and Cybernetics (SMC), Alaska, USA, 2011.
R. Chen, M. She, J. Wang, X. Sun and L. Kong, Driver verification based on handgrip recognition on steering wheel, IEEE International Conference on Systems, Man and Cybernetics (SMC), Alaska, USA, 2011.
Wang, P. Liu, M. She, and H. Liu, Human action categorization using conditional random field, IEEE Symposium Series on Computational Intelligence, Paris, France, 2011.
P. Liu, J. Wang, H. Liu and M. She, Human action recognition based on 3D SIFT and LDA model, IEEE Symposium Series on Computational Intelligence, Paris, France, 2011.
J. Wang, R. Chen, X. Sun and M. She, Recognizing human daily activities from accelerometer signal, Procedia Engineering, Elsevier, Dali, China, 2011 (From CEIS Conference 2011).
X. Sun, J. Wang, R. Chen, M. She and L. Kong, Directional Gaussian filter-based LBP descriptor for textural image classification, Procedia Engineering, Elsevier, Dali, China, 2011 (From CEIS Conference 2011).
J. Wang, M. She, A. Kouzani and S. Nahavandi, A review of vision-based gait recognition methods for human identification, IEEE Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 2010.