David Mimno is a postdoctoral researcher in the Computer Science department at Princeton. He received his PhD from the University of Massachusetts Amherst. He previously worked for an internet auction startup, the NLP group at the University of Sheffield, and the Perseus Project, a cultural heritage digital library. He has a particular interest in historical texts and languages. David is currently chief maintainer for the MALLET Machine Learning toolkit.
In this video, David Mimno discusses some of the different choices one can make in training models and what their implications are for efficiency, scalability, and topic quality, using the MALLET topic modeling package.