Machine learning across science and engineering

June 06, 2018

8:00 a.m. ET

Pittsburgh campus

Machine Learning in Science and Engineering

On May 10, 2017 an internal symposium titled Machine Learning in Science and Engineering was held at Carnegie Mellon University to identify ways in which these computational tools are advancing a diversity of fields. Based on the strong response at CMU, an open conference on June 6 - 8, 2018 at the CMU campus in Pittsburgh will be hosted in partnership with Georgia Tech.

This conference will survey advances in basic research that utilizes methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and ways that these methods are leading to innovations across these fields. Researchers from academia, government, and industry are invited to join us for a unique and fascinating forum on the future of research and innovation in science and engineering.

Program details 

 

Plenary Speakers

Kaye Husbands Fealing (School of Public Policy, Georgia Tech)

Max Hutchinson (Citrine)

Erica Fuchs (Engineering and Public Policy, Carnegie Mellon University)

Andrew Moore (School of Computer Science, Carnegie Mellon University)

Patrick Riley (Google Accelerated Science)

 

 

Registration ends April 15th. 

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