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Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
John Hebeler

John Hebeler
Chief Data Scientist and Principal Engineer, Lockheed Martin

John Hebeler is the chief data scientist and principal engineer for the RMS Division of Lockheed Martin, where he just finished a five-year program to analyze large, diverse data streams to form complex policy determinations in a big data event-driven architecture. John holds three patents and is the coauthor of two technical books on networking and data semantics. He presents at technical and business conferences throughout the world. Previously, he served as an adjunct professor for both Loyola University and University of Maryland. John holds a BS in electrical engineering, an MBA, and a PhD in information systems. In his free time, he’s an avid tennis player and beer brewer.

Sessions

11:05am–11:45am Wednesday, May 2, 2018
Determining normal (and abnormal) using deep learning
Models and Methods
 Location: Concourse A
John Hebeler (Lockheed Martin)
Average rating: ***..
(3.50, 2 ratings)
Determining abnormal conditions depends on maintaining a useful definition of normal. John Hebeler offers an overview of two deep learning methods to determine normal behavior, which when combined further improve performance. Read more.