Very late notice on this meetup, but on Thursday we have a very exciting presentation by Michael Schmidt – “The Robotic Data Scientist.”

Abstract

The past decade has seen major advances in machine learning to model and predict outcomes from data. However, the greatest challenge facing these approaches remains interpreting the results and gaining strategic understanding of the predictions. We still need ways to hypothesize and understand how a system works – the real value resides in actionability. Here I will introduce advances in the field of evolutionary computation that can accelerate this process by detecting key mathematical drivers and relationships from raw data. These results can be applicable in almost any situation — from detecting laws of physics from motion-tracking data to modeling sales drivers in a retail chain.

Bio

Michael Schmidt’s research focuses on “Machine Science” – a direction in artificial intelligence research to accelerate data-driven discovery. Over the past 6 years, he has worked on algorithms and techniques to automate knowledge discovery from data. In particular, he has published extensively on identifying mathematical relationships (such as laws of physics) in experimental data, and algorithms in evolutionary computation. Michael is the creator of the Eureqa project – a popular software program for discovering hidden mathematical relations in experimental data. His research has appeared in several news outlets from the New York Times, to NPR’s RadioLab, and Communications of the ACM. Currently, Michael runs Nutonian Inc. which specializes in scientific data mining and cloud computing for data analysis. In 2011, Michael was featured in the Forbes list of the “Most Powerful Data Scientists” by Tim O’Reilly.