Mass spectrometry, the core technology in the field of proteomics,
promises to enable scientists to identify and quantify the entire
complement of molecules that comprise a complex biological sample.  In
the biological and health sciences, mass spectrometry is commonly used
in a high-throughput fashion to identify proteins in a mixture.
Currently, the primary bottleneck in this type of experiment is
computational.  Existing algorithms for interpreting mass spectra are
slow and fail to identify a large proportion of the given spectra.
In this talk, I will describe several projects in which we apply
techniques and tools from the field of machine learning to the
analysis of mass spectrometry data.  Our goal is to enable scientists
to more easily, efficiently and accurately analyze and understand
their mass spectrometry data.