Seminar Series - Qiang Cheng, PhD - Feature Selection and Learning on High- Dimensional and Large-Scale Data

Time: 
Monday, April 24, 2017 - 12:00pm to 1:00pm
Location: 
Thomas C. Robinson Commons, 127 CTW Building
Body: 

Seminar Series
Monday, April 24, 2017  12:00-1:00
Thomas C. Robinson Commons, 127 CTW Building

Feature Selection and Learning on High- Dimensional and Large-Scale Data

Qiang Cheng, PhD
Southern Illinois University Carbondale

Qiang Cheng image

Abstract

Diverse areas of scientific research and everyday life, such as healthcare, biomedicine and finance, are now deluged with high-dimensional data and big data. There is a need of data mining and prediction techniques for finding patterns and discovering knowledge from such data. In this talk I will present our feature selection and learning methods for handling such data effectively and efficiently. The feature selection methods integrate intrinsic discriminative information and exploit global optimization techniques on Markov random fields, giving rise to a closed-form solution of linear complexity. The learning methods are built within our minimax pattern learning framework, extending lasso-type sparse representation and possessing efficient complexity and fast convergence. I will present both supervised and unsupervised models that exploit jointly representation and learning. It is expected that these methods will have potentially a significant impact on various fields such as medicine and science

Short Bio

Dr. Qiang Cheng received the BS and MS degrees from the College of Mathematical Science at Peking University, China, and the PhD degree from the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. Currently, he is a tenured associate professor at the Department of Computer Science at Southern Illinois University Carbondale. He previously was a faculty fellow at the Air Force Research Laboratory, Wright-Patterson, OH, and a senior researcher and senior research scientist at Siemens Medical Solutions, Siemens Corporate Research, Siemens Corp., Princeton, NJ. His research interests include data science and predictive analytics, pattern recognition, machine learning, and biomedical informatics. He has published about 100 peer-reviewed papers in various premium venues including IEEE TPAMI, TNNLS, TSP, NIPS, CVPR, ICDM, AAAI, ICDE, CIKM, KDD, ACM TIST, and TKDD. He has a number of international patents issued or filed with the IBM T.J. Watson Research Laboratory, Siemens Medical, and Southern Illinois University.