The Computational Science and Statistics Seminar Series is a weekly seminar in which students and faculty present their current research. This seminar gives graduate students, as well as faculty, an opportunity to find out about the uses of computational mathematics in various fields.
Schedule for Spring 2016
The seminar will be held on Mondays, 3:00 - 4:00, in SOL 008.
January 18 MLK Day
February 15 President’s Day
February 22 Qin Ma(Assistant Professor Bioinformatics, Computational Systems Biology)
Omic data mining & modeling and biological systems inference in Bioinformatics
Bioinformatics is in the big data era, and all kinds of Omics data provide huge information for us to understand complex biological system at different levels. Hence, current bioinformatic scientists, should develop and apply computational techniques, with two necessary capabilities: big data analysis and modeling; and biological systems inference in support of novel biology discovery. In this talk, I will show my experiences in genomic and transcriptomic data mining and biological systems modeling by addressing bacterial bioinformatics problems. And the knowledge and information gained through such studies have been applied to reconstruction of metabolic pathways & network and elucidation of associated regulatory systems in microbes.
February 29 Ryan Burton (Analytics Manager, Capital Services), Alfred Furth (Vice President, Chief Data Scientist, Capital Services), James Gentile (Director of Risk, Capital Services)
Analyzing Collection Effectiveness using Incremental Response Modeling
Incremental response modeling (IRM) is commonly used to optimize marketing campaigns. Traditionally marketing campaigns are optimized by targeting customers most likely to respond without considering the incremental effect the campaign had. IRM targets those most likely to respond favorably to the campaign using a randomly split control and test group. The talk is divided into three sections including applications and benefits, basic theory, and a case study analyzing collection effectiveness with IRM. The IRM case study in collection effectiveness showed collection costs can be decreased without impacting revenue. By focusing collection calls on customer segments positively influenced, collection effectiveness is maximized.
March 7 SPRING BREAK
March 14 Dong Xu (with Plant Science)
March 21 Susan Puumala
March 28 Jixiang Wu
April 4 Matt Biesecker
April 11 Harry Harlow