Guest Editors
Usman A. Khan, Waheed U. Bajwa, Angelia Nedić, Michael G. Rabbat, and Ali H. Sayed
Publication Date

Optimization for Data-driven Learning and Control

Guest Editors:

  • Usman A. Khan, Tufts University, Medford, MA, USA
  • Waheed U. Bajwa, Rutgers University, NJ, USA
  • Angelia Nedić, Arizona State University, AZ, USA
  • Michael G. Rabbat, Facebook AI ResearchWeb
  • Ali H. Sayed, EPFL, Lausanne, Switzerland


Optimization, once considered a niche topic, has transitioned in the last decade into one of the most important workhorses of modern machine learning, artificial intelligence, and control problems. Common to many of these data-rich problems are one or more of the following characteristics that make the use of numerical optimization methods so challenging: massively large datasets, geographically distributed datasets, streaming data, communication and/or computation constraints, privacy issues, and non-convex objective functions. This special issue brings together subject experts from both industry and academia to highlight the interplay between optimization methods and these characteristics of machine learning, artificial intelligence, and control from a number of perspectives that span multiple disciplines.

Publication Date: 2020

Submission Deadline: December 15, 2019