Statistical learning, sometimes called machine learning, is becoming ever more important as a component of data science, and department members have had active research in this area for more than a decade.  Statistical learning methods include classification and regression (supervised learning) and clustering (unsupervised learning).  Current research topics of faculty members and their graduate students include construction of phylogenetic trees in evolution, ensembles of models and sparse clustering.  Applications include the search for novel pharmaceutical drugs and detection of biogenic patterns.