Qirong Mao-江苏大学计算机科学与通信工程学院

	Name	Qirong Mao
	Title and Position	Dean of CS school, Professor, Ph. D. Supervisor
	Research Interests	Multimedia Processing, Big data on Multimedia, Human-Computer Interaction
	Office Phone	+86-0511-88780371
	E-mail	mao_qr@ujs.edu.cn

Curriculum Vitae:

Dr. Qi-rong Mao received her MS and PhD degree from Jiangsu University, Zhenjiang, P. R. China in 2002 and 2009, both in computer application technology.
From 1999 to 2002, Dr Qi-rong Mao has worked on the Computer Supported Cooperation Work supervised by Prof. Yong-zhao Zhan in Jiangsu University. From 2005 to 2009, Dr Qi-rong Mao has worked on the multimedia analysis technology with Prof. Yong-zhao Zhan in Jiangsu University. In 2013, she worked on affective computing as a visiting scholar in Wayne State University (with Prof. Ming Dong).

Research Interests:

Her research interests include:
Multimedia Analysis
Big data on Multimedia
Human-Computer Interaction
Affective Computing
Intelligent Transportation

Award

1. Outstanding doctoral dissertation of Jiangsu University, Research on the Feature Extraction and the Recognition Method of Speech Emotion, 2009.
2. Outstanding Teachers of Jiangsu University, 2010.
3. Outstanding Course of Jiangsu Province, Operating System, 2004, Ranked the fourth；

Teaching and research achievements

Projects

1. Research on multi-modal collaborative decision emotion analysis methods based on vision speech and affective context. National Natural Science Foundation of China, Project Leader.
2. The key technology research on motion analysis in the wild by using deep learning. Project Leader.
3. Drivers' negative emotion detection and warning method based on vision and speech. Class General Financial Grant from the China Postdoctoral Science Foundation. Project Leader.
4. Research on speaker independent speech emotion recognition method with model-based adaptation and collaborative decision. National Natural Science Foundation of China—Youth Foundation . Project Leader.
5. Research on the feature fusion recognition method of natural speech emotion based on the sparse expression. Jiangsu Province Natural Science Foundation of China. Project Leader.
6. Research on speaker independent audio emotion recognition method with speaker adaptation. Research Foundation for Talented Scholars of Jiangsu University. Project Leader.
7. Research on speaker independent Audio Emotion Recognition Method based on non-individualization features fusion. University Science Research Foundation of Jiangsu Province. Project Leader.

Publications

[1] Qirong Mao, Ming Dong, Zhengwei Huang and Yongzhao Zhan. Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks. IEEE Transactions on Multimedia, 2014, 16(8): 2203–2213. (SCI index)
[2] Zhengwei Huang，Ming Dong，Qirong Mao and Yongzhao Zhan. Speech Emotion Recognition using CNN. International Conference of ACM Multimedia, Orlando, Florida, Nov. 3-7, 2014.(SCI index)
[3] Qirong Mao, Xinyu Pan, Yongzhao Zhan and Xiangjun Shen. Using Kinect for Real-Time Emotion Recognition via Facial Expressions. Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 2015, DOI: 10.1631/FITEE.1400209. (Online Publication)(SCI index)
[4] Qirong Mao, Xiaolei Zhao, Zhengwei Huang and Yongzhao Zhan. Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features. Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 2013, 14(7):573-582. (SCI index)
[5] Qirong Mao, Xiaojia Wang, Yongzhao Zhan. Speech Emotion Recognition Method Based on Improved Decision Tree and Layered Feature Selection. International Journal of Humanoid Robotics,2010,7(2):245-261. (SCI、EI index)
[6] Qirong Mao, Yongzhao Zhan. A Novel Hierarchical Speech Emotion Recognition Method Based on Improved DDAGSVM. Computer Science and Information Systems, 2010, 7(1):211-221. (SCI index)
[7] Qirong Mao, Suli Hu and Haihe Wang and Yongzhao Zhan, A Semi-Supervised Recognition Method Based on Probability Distance Manifold Learning and Graph-Model. Journal of computational and theoretical nanoscience.2014, 4(2), 303-309. (SCI index)；

Teaching Courses

1) Machine Learning, for master students
2) Advanced Machine Learning, for Ph. D. students
3) Operating Systems, for undergraduate students
4) Human Computer Interaction, for undergraduate students