UMBC CSEE Colloquium
Analytics for Cancer Survival Time
Dr. Shujia Zhou and Ran Qi
CSEE Department, University of Maryland, Baltimore County
1:00 pm Friday, 15 February 2013, ITE 227, UMBC
With advent of new medical technologies, more and more prognostic factors are discovered and used in predicting cancer survival time. Consequently, the number of cancer patient types increase significantly. However, there are limited therapies available for cancer patients. Therefore, there is an urgent need to develop accurate algorithms in grouping cancer patients so that a doctor can choose the optimal therapy for a cancer patient. In this talk we will introduce current grouping algorithms, discuss new approaches in improving their efficiencies, and present a prototype of prognostic system for cancer patients.
Dr. Shujia Zhou is a research associate professor of Computer Science and Electrical Engineering at UMBC. He received a Ph.D. from Washington University at St. Louis in 1993. He held a Director’s-funded Postdoctoral Fellowship at Los Alamos National Laboratory (LANL) and became a LANL technical staff member in 1996. At LANL, his researches in large-scale molecular dynamics simulations were published in the Journal of Science and reported in the journals of both Science and Nature. In 2000, he joined Northrop Grumman Corporation and worked on NASA Computation Technology Projects. He is a pioneer in accelerating climate and weather applications with multi-core processors such as,IBM’s Cell B.E. processor. His current research interests are big data analytics, in particular in finance and health.
Ran Qi received her M.S. degree in Computer Science from the Lamar University in 2009 and started her Ph.D program in Computer Science at the University of Maryland, Baltimore County in 2010. In 2011, she worked on train monitoring system development at Norfolk Southern for 4 months as a co-op intern. In January 2012, she worked on personalized medicine in IBM Toronto Lab for 3 weeks. Her current Ph.D research focus is data mining in health analytics and personal medicine including cancer survival analysis and developing a cancer prognostic system.