Paul is the lead Data Scientist at GFI Software, a pioneer in powerful, award-winning IT solutions. He leads a team in analyzing incoming data flows across various GFI software products, using their insights to extract business value.
Prior to joining GFI, Paul worked for a government intelligence & defense contractor in Washington, D.C. on their groundbreaking work with a technology called Latent Semantic Indexing (LSI). LSI is an unstructured, unsupervised, text analytics machine learning technique that can be used to find conceptual meaning and relationships between documents, terms, entities, in massive document sets. He spent several years pushing the limits of LSI and applying it to problems and scales not before seen in academia or industry.
Paul has a B.S. in Mathematics & Physics from MIT, and a Master's in Computer Science with a focus in machine learning from George Mason University.
In this part-time course, students learn to build robust predictive models, test their validity, and clearly communicate resulting insights.