Applications of Sparse Signal Recovery in Biological Testing

While group testing was originally designed for the
Selective Service to test military inductees for syphilis, it
currently appears in many forms, including the design of scientific
experiments, cryptographic schemes for corrupted data, and the
processing and analysis of streaming data.  It is, in fact, a special
case of a more general, modern problem, that of sparse signal
recovery.  This talk will focus on the design of sparse signal
recovery systems for biological applications; the main theoretical
component of which is to design an optimally efficient set of tests of
items so that the test results contain enough information to determine
a small subset of items of interest.  With the emergence of new
computational applications that monitor large volumes of streaming
data or that acquire a reduced number of measurements of large data
sets, both the design problem and its associated algorithmic problem
are crucial for efficiently extracting a small amount of useful
information from a voluminous data set, for designing efficient high
throughput biological screens, and for reducing the number of
experiments necessary for identifying items of biological interest.