Keynote Speakers
Towards Scalable Algorithms for Distributed Optimization and Learning
PhD. César A. Uribe
Cesar A. Uribe is the Louis Owen Assistant Professor at the Department of Electrical and Computer Engineering at Rice University. He received the M.Sc. degrees in systems and control from the 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 Ph.D. degree in electrical and computer engineering at the University of Illinois at Urbana-Champaign in 2018. He was a Postdoctoral Associate in the Laboratory for Information and Decision Systems-LIDS at the Massachusetts Institute of Technology-MIT until 2020. He held a visiting professor position at the Moscow Institute of Physics and Technology until 2022. His research interests include distributed learning and optimization, decentralized control, algorithm analysis, and computational optimal transport.
Towards Scalable Algorithms for Distributed Optimization and Learning
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. This talk presents recently proposed distributed algorithms with near-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 network structure. We provide application examples to distributed inference and learning and computational optimal transport.
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