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Thursday, May 30 • 9:00am - 9:30am
How to Model with Millions of Variables

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In some fields, big data doesn't just mean lots of rows — it also means lots of variables. For example, in the fields of proteomics and genomics, the analysis of gene expression data or mass spectrometry readings is increasingly important for diagnosing diseases. But there can be hundreds of thousands of SNPs from thousands of patients in a single study. So how do you build predictive models in which the number of independent variables can number in the millions?

In this demonstration we show how one manufacturer used a series of 'wide data' innovations in the TIBCO Data Science platform to build a digital twin model that identified causes of yield loss in a semiconductor fab. The result represents the successful convergence of IoT, big data, and machine learning to build a predictive model on over 6 million features. 

avatar for Steven Hillion

Steven Hillion

Sr. Director of Data Science, TIBCO
Steven Hillion has been leading large engineering and analytics projects for over fifteen years. At TIBCO Software, he leads innovations in large-scale machine learning and collaborative analytics. He received his Ph.D. in mathematics. 

Thursday May 30, 2019 9:00am - 9:30am CDT
(H) P1838 Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431