Description:
Rapid advances in cloud computing, data management, and machine learning has led to new opportunities to integrate systems, computation and decision-making in real time. Initiatives such as Industrie 4.0 and Smart Manufacturing encourage industry, government and academia to work together to advance theory and application. Papers are solicited that describe all aspects of data-driven modeling and decision making as they relate to the chemical manufacturing industries. Industrial contributions and reviews are encouraged. We are interested in theoretical advances in machine learning, applications of learning and related data analysis methods useful for data-driven modeling and decision making in systems with integrated continuous and discrete transitions.
Sponsor:
Data and Information Systems
Chair:
B. Erik Ydstie
Professor
Carnegie Mellon University
Department of Chemical Engineering
5000 Forbes Avenue
Pittsburgh, PA
15213
Email: ydstie@cmu.edu
Professor
Carnegie Mellon University
Department of Chemical Engineering
5000 Forbes Avenue
Pittsburgh, PA
15213
Email: ydstie@cmu.edu
Co-Chair:
Bethany Nicholson
Senior Member of Technical Staff
Center for Computing Research, Sandia National Laboratories
Albuquerque, NM
60616
Email: blnicho@sandia.gov
Senior Member of Technical Staff
Center for Computing Research, Sandia National Laboratories
Albuquerque, NM
60616
Email: blnicho@sandia.gov
Papers:
Abstract Submissions Closed
See more of this Group/Topical: Computing and Systems Technology Division
