Y.L. Yang,Full professor

PhD supervisor

School of mathematics and statistics

Address: Department of Mathematical Sciences, Xidian University 

Email: ylyang@mail.xidian.edu.cn

Tel: 029 -81891344

      Y.L.Yang was born in Pucheng, Shaanxi, 1967. He is a full Professor of Probabilistic Graphical Models and Time Series Analysis. Executive director of the Mathematical Association of Shaanxi Province. He received his B.S. degree, M.S. degree in the Department of Mathematics from Shaanxi Normal University, in 1990 and 1993, respectively, and received the Ph.D. degree at Northwestern Polytechnical University in 2003. In 2006, he outbounded post-doctoral mobile stations in Xidian University, and then was sent to the United States University of Rochester as a national public school student in 2007.

     He has received one reward of Shaanxi Provincial Science and Technology and two Bureau departmental level research awards. His research interests include probabilistic graphical models, data classification and intelligent optimization. Moreover, he has published more than 40 papers. His papers were published as the first-named author in journals such as Information Sciences, International Journal of Approximate Reasoning, Acta Application Mathematic, Control Theory and Applications,Chinese Quarterly Journal of Mathematics, Acta Mathematica Sinica, Journal of Mathematical Research and Exposition, Control Theory and Applications, Pattern Recognition and Artificial Intelligence and Journal of China Ordanance.

1.Probabilistic Graphical Models:Mainly engaged in research and application of Bayesian network theory 

2.Time Series Forecasting: Mainly researched in the theory of the time sequence and frequency hopping sequence prediction

      With the rapidly push of global information and advance in science and technology, and the costantly accumulation of human’s knowledge,it is press for us to exploit some methods how to analyse data efficiently, accurately and intelligently, which is the core issue and foundation of machine learning, data mining and statistical learning. It has produced a number of frontier issues during the universal and real application of data analysis, and among which, intelligent data analysis and reasoning forecast is one of the important research topics. 

      Bayesian networks,also called Belief Networks,are very important branch of graphical models, which describe and deal with the uncertainty on the condition of different components of knowledge in probability theory, and have played a significant role in intelligent data analysis and information retrival classification. Its research originated from the early 1990s, and rapidiy developed in speech recognition, medical diagnostics, machine learning and other fields. As a powerful intelligent representation tool in pattern recognition and statistical reasoning, Bayesian networks have obtained deep research and extensive application. 

      The team researches on the theory of Probablistic Graphical Model, and carries out its applied research.It mainly involves graphical models of learning, causal reasoning, based on graphical models for data analysis, and time series forecasting and other relevant research directions. Welcome for students interested in to join our research team!