Tan, Shaohua is a professor in the Department of CIS, School of EECS, and he has served as the Director of Center of Financial Intelligence Research since 2012. He obtained his M.Sc and.Ph.D from University of Leuven respectively in 1984 and 1987. His research interest is artificial intelligence applied to finance and business application

Dr. Tan has published more than 100 research papers, and most of them are published in top-tier conferences and journals, including Fuzzy Sets and Systems, Neural Computing, Journal of Economic Dynamics & Control, etc. He has presided international academic conferences and participated in a number of international cooperation research projects.

Dr. Tan has some research projects in the past. His research achievements are summarized as follows:

1) Bayesian network based financial factor relationship modeling: One major research topic in the business intelligence field is to build effective models to analyze the large-scale complex financial system and help financial researchers make decision. He proposed a new Bayesian network based framework for systematically analyzing the relationships of a large set economic and financial factors and discovering mutual causality from financial factors. The method proposed can efficiently describe the risk of financial market and analyze the market trend.

2) Intelligent financial decision under uncertain environment: This is the key factor to research the portfolio construction under uncertain financial environment. He firstly introduced the notion of interval random variable and designed a framework of interval random programming to build programing models, including interval random expected-value programming, interval random chance-constrained programming and interval random dependent-chance programming. Several robust portfolio selection models proposed had proved to result in better portfolio performance.

3) Analysis of corporate credit under the cross shareholding relationship: For the corporate credit assessment, the traditional economic or statistical models are mostly concerned only with the assessor itself, ignoring the actual relationship among companies, resulting in the distortion of evaluation results. He introduced the Banzhaf index to build the interrelated strength matrix between firms and adopted the causal model to analyze the two-way relationship between companies. This approach can get more reasonable assessment of corporate credit.