Structured Data and Learning: Data Science that Embraces Complex, Heterogeneous, Relational Data

Friday, November 6, 2015 - 1:25pm - 2:25pm
Lind 305
Robert Nendorf (Allstate)
Invited by the SIAM Student Chapter at the University of Minnesota.

Many businesses have heterogeneous data that models complex entities with rich relationships between them. This is especially true of insurance companies that must simultaneously set prices, underwrite policies, settle claims, manage agency operations, and deliver a great customer experience over time. Cutting edge technologies like graph databases and disciplines like structured relational learning now allow us to store data, develop ETL processes, and build analytics frameworks that leverage these rich relationships. We will discuss examples of graph querying and structured prediction in an applied context.

Robert Nendorf is a data scientist in the Quantitative Research and Analytics department at Allstate. He is responsible for researching and prototyping data analytics frameworks to help the business make better decisions. This has included building social network analysis tools, predictive models, and big data reporting tools for both claims and pricing partners. Before that he was a mathematician at Northwestern University where he received his PhD in algebraic topology.