Keynote Speakers  

 


Prof. Jin Wang
Valdosta State University, USA


Jin Wang is a Professor of Operations Research in the Department of Mathematics at Valdosta State University, USA. He received his Ph.D. degree from the School of Industrial Engineering at Purdue University in 1994. His research interests include Operations Research, Stochastic Modeling and Optimization, Supply Chain Management, Monte Carlo Simulation, Computational Finance, Portfolio Management, and Applied Probability and Statistics. Currently, he is working on Big Data and Data Mining fields. He has more than 28 years collegiate teaching experience in the field of quantitative methods and statistics at Purdue University, Florida State University, Auburn University, and Valdosta State University. Dr. Wang has been active in professional research activities. He has authored articles for publication in referred journals and conference proceedings. He has been active in INFORMS, IIE, and the Winter Simulation Conference and invited to give presentations, organize and chair sessions at national meetings. He has participated as a principal investigator in several research projects funded by federal and industrial agencies, including the National Science Foundation, General Motors, and the National Science Foundation of P.R. China. He was invited as a panel member at the National Science Foundation Workshop. Dr. Wang also served as a consultant for financial firms. His analytical Monte Carlo method using a multivariate mixture of normal distributions to simulate market data has made a great impact in education and the finance industry. This algorithm was selected as a graduate-level research project topic for many schools, such as, Columbia University Management Department, Carnegie Mellon University Economics and Finance Department, Tilburg University in Holland, Technische Universitaet Munich in Germany, Imperial College in London. This method was also implemented in many financial companies, such as, Zurcher Kantonal Bank, IRQ, Zurich Switzerland, Klosbachstrasse, Zurcher, Switzerland, Norsk Regnesentral in Norway, Cutler Group, L.P., Altis Partners (Jersey) Limited, Windham Capital Management, LLC.

Title:Applications of Eigenvalues and Eigenvectors in Data Mining
Abstract: Applied linear algebra methods play an important role in data science. Eigenvalues and eigenvectors are widely used in efficient algorithms for data mining, with applications in dimensionality reduction, image processing, facial recognition, and Internet search engine. In this study, we mainly discuss efficient algorithms in data mining field. Principal Component Analysis (PCA) uses eigenvalues and eigenvectors to re-express data in the form of a small number of actual data points. PCA is widely applied in image compression and facial recognition. PageRank, the Google search engine algorithm, is an eigenvector of a transformation matrix corresponding to the largest eigenvalue. The best solution for the decision vector for network classification is the feature vector of the Laplacian matrix corresponding to the second smallest eigenvalue.

 

Prof. Zhenghua XU
Hebei University of Technology, China.

Prof. Zhenghua Xu received a M.Phil. in Computer Science from The University of Melbourne, Australia, in 2012, and a D.Phil in computer Science from University of Oxford, United Kingdom, in 2018. From 2017 to 2018, he worked as a research associate at the Department of Computer Science, University of Oxford. He is now a professor at the Hebei University of Technology, China, and a awardee of “100 Talents Plan” of Hebei Province. He has published more than 20 papers in top AI or database conferences, e.g., AAAI, IJCAI, ICDE, EDBT, CIKM, etc. His research focuses on topics within artificial intelligence and data mining, especially deep learning, medical artificial intelligence, health data mining, and computing vision.