Dr. Darrin Lewis Presented Machine Learning
Dr. Darrin Lewis (Hofstra CS Alumni) presented
his research in machine learning at the Hofstra CS department
seminar on September 25th, 2008. Dr. Lewis earned his Ph.D
at Columbia University under Dr. William Stafford Noble and
Dr. Tony Jebara. Prior to that, he earned a M.S.
in Computer Science at Hofstra University under
Dr. Robert Bumcrot and Dr. Jerome Epstein.
Dr. Lewis has held research positions at Bell Laboratories
and Siemens Research.
Abstract of talk: Machine learning offers
powerful tools to experts practicing in varied domains of study.
Amongst practitioners, there is a vital need to learn
from heterogeneous data sets.
This need is fueled by the increasing amount of data being
generated by different processes, that potentially inform different
aspects of a learning problem. As an example,
we consider the computational biology problem of
protein functional annotation. Numerous wet lab and
computational experiments have provided a wide variety of data
pertaining to the same set of proteins. Each
type of measurement, e.g., DNA sequence, three dimensional
structure, subcellular location, protein domain content,
interaction networks, etc., offers a different set
of discriminative features for classification. The practitioner should
have the freedom to use all of these data to
inform a classifier and should expect the learning algorithm
to exploit all the data for maximum benefit.