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Two PhD. Students of SOM Obtained Outstanding Paper Awards
Datetime: 2013-01-05 Clicks: 26

On 21-22 Dec, Peking University’s 2012 2nd national Management Science and Technology doctoral forum was held in Guanghua Management School, Peking University. GUO Dong and WEI Shaobo from School of Management, USTC obtained the highest award--outstanding papers award.

 

Through cautious selection of committee members, three papers were awarded with outstanding papers award. GUO Dong’s (co-supervised by prof. LIANG Liang and prof. WU Jie) Super-efficiency Infeasibility Problem under Non-radial Measurement and WEI Shaobo’s (co-supervised by professor HUA Zhongsheng and Dr. LIU Hefu) Supply Chain Integration, IT capabilities, Firm Performance: A Profile Deviation Analysis are two of the three.

 

Certificate of Honor of GUO Dong

Certificate of Honor of WEI Shaobo

 

Peking University 2012 2nd national Management Science and Technology doctoral forum was co-hosted by Graduate School, Guanghua School of Management and College of Engineering of Peking University. The theme of the forum is “focusing on the frontier, enriching academics and encouraging innovation”. Prof. CHEN Lihua, chair of Management Science and Information System, Guanghua School of Management, Peking University, prof. DAI Leyuan, chair of Industrial Engineering and Management, College of Engineering, Peking University were selected as co-chairmen of the forum. Prof. YE Yingyu from Stanford University, prof. CHEN Fangruo from Columbia University, prof. SHI Jianjun from Georgia Institute of Technology, prof. ZHOU Shuoyan from National Taiwan University of Science and Technology and near 100 phD. students from more than 20 universities of China were invited to attend the forum. Seven presentations and five sub-forum presentations have been given during the two days, covering decision analysis and green management, supply chain and logistics management, information system and knowledge management, industrial engineering and innovation management as well as business data mining. 

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