Dr. Dunwei Wen is an Associate Professor in the School of Computing and Information Systems at Athabasca University, Alberta, Canada. He received the PhD in pattern recognition and intelligent systems from Central South University, and MSc from Tianjin University and BEng from Hunan University. Prior to his current position, he was a visiting scholar in the Department of Computing Science at the University of Alberta, and Professor at Central South University. His research interests include artificial intelligence, machine learning, natural language processing, computer vision, and their application in advanced and intelligent information systems.
Research interests
Dr. Dunwei Wen's research interests are Artificial Intelligence and Machine Learning, especially statistical learning and deep learning algorithms for natural language processing, signal analysis and image recognition.
He's current research program aims to develop statistical learning and deep learning methods to represent, recognize and interpret structural and semantic information in natural language text, signals, and images for implementing advanced and intelligent information systems.
Educational credentials
Ph.D., Central South University
M.Sc., Tianjin University
B.Eng., Hunan University
Whitelock-Wainwright, A., Laan, N., Wen, D., & Gasevic, D. (2020). Exploring student information problem solving behaviour using fine-grained concept map and search tool data. Computers & Education, vol. 145, 103731, February 2020.
Zang, M., Wen, D., Liu, T., Zou, H., & Liu, C. (2018). A pooled object bank descriptor for image scene classification. Expert Systems with Applications, vol. 94, 15 March 2018, pp 250-264.
Liu, T., Si, Y., Wen, D., Zang, M., & Lang, L. (2016). Dictionary learning for VQ feature extraction in ECG beats classification. Expert Systems with Applications, vol. 53, 1 July 2016, pp 129-137.
Yang, G., Wen, D., Kinshuk, Chen, N.-S., & Sutinen, E. (2015). A novel contextual topic model for multi-document summarization. Expert Systems with Applications, vol. 42, no. 3, pp 1340-1352.
Zang, M., Wen, D., Wang, K., Liu, T., & Song, W. (2015). A novel topic feature for image scene classification. Neurocomputing, vol. 148, pp 467-476.
Huang, Y-M., Liu, M-C., Chen, N-S., Kinshuk, & Wen, D. (2014). Facilitating learners' web-based information problem-solving by query expansion-based concept mapping. Australasian Journal of Educational Technology, vol. 30, no. 5, pp 517-532.
Wen, D., Cuzzola, J., Brown, L., & Kinshuk (2013). Instructor-aided asynchronous question answering system for online education and distance learning. The International Review of Research in Open and Distance Learning, vol. 13, no. 5, pp 102-125.
Yang, G., Chen, N.-S., Kinshuk, Sutinen, E., Anderson T., & Wen, D. (2013). The effectiveness of automatic text summarization in mobile learning contexts. Computers & Education, vol. 68, pp 233-243.
Liu, M. C., Huang, Y. M. , Kinshuk, & Wen D. (2013). Fostering learners' metacognitive skills of keyword reformulation in image seeking by location-based hierachical navigation. Educational Technology Research and Development, vol. 61, no. 2, pp 233-254.
Wen, D., Graf, S., Lan, C.-H., Anderson, T., Kinshuk and Dickson, K. (2007). Supporting Web-based Learning through Adaptive Assessment. FormaMente Journal, 2007, 2(1-2): 45-79.
Murshed, M., Dewan, M. A., Lin, F., & Wen, D. (2019). Engagement detection in e-learning environments using convolutional neural networks. IEEE International Conference on Cyber Science and Technology, IEEE CyberSciTech 2019, Fukuoka, Japan, August 2019, pp. 80-86. IEEE.
Dewan, M. A., Lin, F., Wen, D., Murshed, M., & Uddin, Z. (2018). A deep learning approach to detecting engagement of online learners. IEEE International Conference on Internet of People, IEEE IoP 2018, Guangzhou, China, October 2018, pp. 1895-1902, IEEE.
Huang, W-B., Wen, D., Yan, Y., Yuan, M., & Wang, K. (2016). Multi-target Osteosarcoma MRI Recognition with Texture Context Features Based on CRF. The Proceeding of 2016 International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, July 24-29, 2016, pp. 3978-3983.
Dewan, M. A., Qiao, D., Lin, F., Wen, D., Kinshuk. (2016). An Approach to Improving Single Sample Face Recognition by Using High Confident Tracking Trajectories. Proceedings of the 29th Canadian Conference on Artificial Intelligence (Canadian AI 2016), LNCS/LNAI vol. 9673, pp 115-121.
Li, Y., Wen, D., Wang, K., & Hou, A. (2013). Mixed Kernel Function SVM for Pulmonary Nodule Recognition. ICIAP (2) 2013, LNCS vol. 8157, pp 449-458.
Wen, D., Cuzzola, J., Brown, L., & Kinshuk (2012). Exploiting Semantic Roles for Asynchronous Question Answering in an Educational Setting. Proceedings of the 25th Canadian Conference on Artificial Intelligence (Canadian AI 2012), LNCS/LNAI vol. 7310, pp 374-379.
Wen, D., & Dou, Q. (2010). Exploiting Frame Information for Prepositional Phrase Semantic Role Labeling. Proceedings of the 23th Canadian Conference on Artificial Intelligence (Canadian AI 2010), LNCS/LNAI vol. 6805, pp 269-272.