Towards Scalable Algorithms for Distributed Optimization and Learning

Abstract: Increasing amounts of data generated by modern complex systems such as the energy grid, social media platforms, sensor networks, and cloud-based services call for attention to distributed data processing, in particular, for the design of scalable algorithms that take into account storage and communication constraints and help to make coordinated decisions. In this talk, we present recently proposed distributed algorithms with optimal convergence rates for optimization problems over networks, where data is stored distributedly. We focus on scalable algorithms and show they can achieve the same rates as their centralized counterparts, with an additional cost related to the structure of the network. We provide application examples to distributed inference and learning, and computational optimal transport.

Bio: Cesar A. Uribe received the M.Sc. degrees in systems and control from Delft University of Technology, in The Netherlands, and in applied mathematics from the University of Illinois at Urbana-Champaign, in 2013 and 2016, respectively. He also received the PhD degree in electrical and computer engineering at the University of Illinois at Urbana-Champaign in 2018.  He is currently a Postdoctoral Associate in the Laboratory for Information and Decision Systems-LIDS at the Massachusetts Institute of Technology-MIT and visiting professor at the Moscow Institute of Physics and Technology. His research interests include distributed learning and optimization, decentralized control, algorithm analysis, and computational optimal transport.