We will focus mainly on algorithmic applications, and discuss the use of fixed-parameter tractable techniques in the analysis of highly complex data. We will address important issues with noise, and the role model organisms often play in the study of human health. Critical resources include enormous repositories of emergent data, suites of novel statistical and graph theoretical methods, deep domain knowledge and high performance computing platforms. We will describe how the potential of these resources can be harnessed to help realize the promise of new algorithmic methods in the elucidation and interpretation of previously unknown relationships. Effective load balancing and efficient combinatorial search are important concerns. Examples will be drawn from numerous types of high-throughput and/or highly heterogeneous biological and health sciences data, as well as data drawn from a variety of other research domains.

Mike Langston is a Professor of Electrical Engineering and Computer Science at the University of Tennessee. Over the years he has worked on a wide spectrum of research topics, ranging from scheduling and well-quasi-order theory to circuit design and VLSI. His current research efforts are primarily concentrated on the development, synthesis, analysis and high performance implementation of graph algorithms for the analysis of biological, social, environmental and other sorts of high dimensional, high throughput data. In addition to maintaining his research program, he regularly teaches courses on discrete optimization, graph theory, parallel computing and related topics.