New Boeing/Carnegie Mellon Aerospace Data Analytics Lab-Machine Learning Department - Carnegie Mellon University

Thursday, October 1, 2015

New Boeing/Carnegie Mellon Aerospace Data Analytics Lab

Carnegie Mellon University has joined with The Boeing Company to establish the Boeing/Carnegie Mellon Aerospace Data Analytics Lab that will leverage our leadership in machine learning, language technologies and data analytics. Faculty involved from the Machine Learning Department are: Alex Smola, Christos Faloutsos, Yiming Yang & Barnabas Poczos.

The goal is to find ways to use artificial intelligence and big data to capitalize on the enormous amount of data generated in the design, construction and operation of modern aircraft. Creating a maintenance schedule determined by the actual flight history and component performance for each airplane, rather than historic norms for similar aircraft, is just one of the possibilities.

Boeing will provide $7.5 million for the lab over the next three years. Jaime Carbonell, the Allen Newell University Professor of Computer Science and director of the Language Technologies Institute, will lead the new research endeavor, which will tap world-class expertise from across the School of Computer Science and the CMU campus at large. John Vu, a former Boeing chief software engineer who now is a professor in language technologies and computational biology, is playing a key role in establishing and overseeing the lab.

Aeronautics is one of today's most data-intensive industries. Today's aircraft contain sensors and embedded computers which are constantly generating data. Together with voluminous information gathered during the manufacture and regular maintenance of each of thousands of aircraft, this data provides opportunities for gleaning new insights that could lead to safer, more reliable and more efficient aircraft operations.

"The mass of data generated daily by the aerospace industry overwhelms human understanding," Carbonell said, "but recent advances in language technologies and machine learning give us every reason to expect that we can gain useful insights from that data. The new algorithms and methods should create a stronger aerospace industry and be applicable to many other important endeavors."

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