Dr. Darrin Lewis Presented Machine Learning Research


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.