中国人民大学

近期著作：

1.梁循. 互联网金融信息智能挖掘基础. 北京大学出版社. 2009.

2.梁循. 网络金融系统设计与实现案例集. 北京大学出版社. 2009.

3.梁循. 网络金融信息挖掘导论. 北京大学出版社. 2008.

4.梁循等. 电子商务理论与实践. 北京大学出版社. 2007.

5.梁循等. 互联网金融信息系统的设计与实现. 北京大学出版社. 2006.

6.梁循. 数据挖掘算法与应用. 北京大学出版社. 2006.

7.梁循等. 网络金融. 北京大学出版社. 2005.

近期论文：

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。在金融信息系统和数据挖掘算法领域学习工作20余年，积累了较丰富的学习和研究经验。在计算机、经济领域分别获得博士学位、硕士学位。完成两站博士后研究。曾在美国硅谷高科技公司工作、洪堡学者。曾主持863项目、及多项国家自然科学基金项目。开设课程《金融学》、《网络金融》、《高级网络金融》、《商务建模与商务智能》等。

出版著作教材7部，发表论文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。

社会兼职：

IEEE Senior Member、系统工程学会社会经济系统专业委员会理事、优选法统筹法及经济数学研究会理事。

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.