NOTES

Photo Gallery

VIDEOS

You are now here Home > Academics > Professors >

CS:Mingsheng Shang

published: 2015-12-09 21:13:57       hits: 

name : Mingsheng Shang Sex: male phone: 13980051128
email: msshang@uestc.edu.cn office-address: Main Building A2-401, QingShui Campus
PH.D  Supervisor: Yes Master Supervisor Yes
major: Computer Science and Technology
research interst: Data Mining, Social Networks/Graph Analysis, Recommender Systems
Biography: Mingsheng Shang received his PhD from the University of Electronic Science and Technology of China (UESTC) in 2007. He is a professor at the School of Computer Science and Engineering of UESTC. He was a visiting scholar in the university of Minnesota, the University of Rochester, and the University of Fribourg. He has published more than thirty papers in refeered journal and two  monographs. Currently, his research interests include data mining, complex networks, cloud computing and their applications.
Education experience: Ph.D:   School of Computer Science & Engineering, UESTC
Thesis: On  task scheduling in the grid computing
Date: Dec. 2007 (Sep. 2003 – Dec. 2007)

M.Sc: School of Computer Science & Engineering, UESTC
Thesis: Real-time transmission technology in IP-based network
Date: Mar. 2003 (Sep. 2000 – Mar. 2003)

B.Sc: Department of Mathematics, SiChuan Normal University
Date: June 1995 (Sep. 1991 – June 1995)
Selected Publications:
[1]. Zeng W, Zeng A, Shang M-S, Zhang Y-C (2013) Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion. PLoS ONE 8(11): e79354
[2]. Zhang Q-M, Zeng A, Shang M-S (2013) Extracting the Information Backbone in Online System. PLoS ONE 8(5): e62624
[3]. Shang M-S, Lü L-Y, Zhang Y-C, Zhou T (2010) Empirical analysis of web-based user-object bipartite networks. EPL, 90(4):48006
[4]. Shang M-S,  Lü L-Y,Zeng W, Zhang Y-C, Zhou T (2009) Relevance is more significant than correlation: Information filtering on sparse data. EPL, 88(12):68008
[5]. Shang M-S (2008) Optimal algorithm for scheduling large divisible workload on heterogeneous system. Applied Mathematical Modelling 32(9): 1682–1695