中國人民大學

近期著作：

1.梁循. 互聯網金融信息智能挖掘基礎. 北京大學出版社. 2009.

2.梁循. 網絡金融系統設計與實現案例集. 北京大學出版社. 2009.

3.梁循. 網絡金融信息挖掘導論. 北京大學出版社. 2008.

4.梁循等. 電子商務理論與實踐. 北京大學出版社. 2007.

5.梁循等. 互聯網金融信息系統的設計與實現. 北京大學出版社. 2006.

6.梁循. 數據挖掘算法與應用. 北京大學出版社. 2006.

7.梁循等. 網絡金融. 北京大學出版社. 2005.

近期論文：

1.Xun Liang, Zhihao Ni. Hyperellipsoidal statistical classifications in a reproducing kernel Hilbert space. IEEE Transactions on Neural Networks. 22(6):968-975. 2011.

2.Xun Liang. An effective method of pruning support vector machine classifiers. IEEE Transactions on Neural Networks. 21(1):26-38. 2010.

3.Xun Liang, Rongchang Chen, Xinyu Guo. Pruning support vector machines without altering performances. IEEE Transactions on Neural Networks. 19(10):1792-1803. 2008.

梁循，1965年生。在金融信息系統和數據挖掘算法領域學習工作20余年，積累了較豐富的學習和研究經驗。在計算機、經濟領域分別獲得博士學位、碩士學位。完成兩站博士后研究。曾在美國硅谷高科技公司工作、洪堡學者。曾主持863項目、及多項國家自然科學基金項目。開設課程《金融學》、《網絡金融》、《高級網絡金融》、《商務建模與商務智能》等。

出版著作教材7部，發表論文100余篇，其中30余篇被SCI索引，包括IEEE Transactions on Neural Networks、Pattern Recognition Letters、Electronic Commerce Research and Applications、Neurocomputing、International Journal of Neural Systems等。

社會兼職：

IEEE Senior Member、系統工程學會社會經濟系統專業委員會理事、優選法統籌法及經濟數學研究會理事。

English Resume

Xun Liang received his Ph.D. degree (with honor) in computer science and engineering at Tsinghua University in 1993. He worked as a post-doctoral fellow in the Institute of Computer Science and Technology at Peking University from 1993 to 1995, and the Department of Computer Engineering at the University of New Brunswick from 1995 to 1997. Then in 1999, he received his M.Sc. degree in economics and operations research from Stanford University. Currently, he is a professor in the School of Information at Renmin University of China.

He has industrial working experience leading over ten intelligent information products as a software architect or CTO in California from 2000 to 2007. He was a recipient of the Alexander von Humboldt fellowship.

His research interests include neural networks, support vector machines, and web financial information systems.