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RCMSIA Overview


The research center for management science and information analytics(RCMSIA) in Shanghai University of Finance and Economics(SHUFE), which was founded in the school of information management and engineering(SIME) in September 2013, is an academic institution holding a central position in the network of interdisciplinary research in SHUFE crossing the fields of operations research, statistics, economics, finance, information science, data analytics, and etc.

With the belief that tackling current society’s most significant problems requires strong analytical skills in data processing and decision making, RCMSIA teach and do research on many fields, such as optimization theory and applications, machine learning, operations management, healthcare management and information science. Our faculty has published widely in top journals such as Operations Research, Mathematics of Operations Research, Mathematical Programming, MIS quarterly, IJCAI, FOCS, SODA and etc.

RCMSIA has hosted many guest lectures, international workshops and short courses given by renowned professors such as Prof. Yinyu Ye and Prof. Tse Leung Lai from Stanford University, Prof. Aharon Ben-Tal from Israel, and etc. We have developed intensive collaborations with professors from many famous universities including Stanford University, Chicago University, University of Michigan, New York University, University of Minnesota, and etc. Our undergraduate students have been offered to continue their doctoral study in Stanford, Princeton, Wharton, CMU, Michigan, Chicago, and etc.

Leaves Optimization Solver Overview


The LEAVES international parallel computing optimization lab(LEAVES), which was founded in October 2015, is a joint research lab directed by both the research center for management science and information analytics(RCMSIA) in Shanghai University of Finance and Economics(SHUFE) and Stanford Financial and Risk Management Institute(FARM).

The goal of the lab is to explore the possible parallel optimization algorithms with GPU/CUDA architecture for general convex/nonconvex programming problems and further investigate its applications in machine learning/deep learning. By doing that we try to address computational challenges arising from different fields with large-scale volumes at the big data era. Currently we focus on developing the first optimization solver in China for general mathematical programs.

The lab is advised by Prof. Yinyu Ye from Stanford University and is co-advised by Prof. Guanghui Lan from Georgia Institute of Technology and Prof. Dongdong Ge from SHUFE.