Jacob Schreiber

Jacob Schreiber is a post-doctoral researcher at Stanford University where he develops machine learning-based tools to tackle problems in genomics. He is also the developer of a cornucopia of open source tools in Python, including pomegranate, which implements flexible probabilistic modeling, and apricot, which implements submodular optimization for data subset selection. Previously, he was a core developer for scikit-learn.


Submodular optimization for minimizing redundancy in massive data sets

Thursday October 28 10:30 PM – Thursday October 28 11:00 PM in Talks I