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Focus Period Program

Large-Scale and Distributed Optimization: Focus Period Program

All the seminars outside the workshop will be held in the seminar room of the Automatic Control Department -  M2112B, 2nd floor of the M-building, unless otherwise stated 

Tuesday 30 May 11:15–12:00

Learning Regularizers from Data
Yong Sheng Soh, Caltech

Thursday 1 June 10:15–11:00

Unified view on accelerated randomized methods
Alexander Gasnikov, Moscow Institute of Physics and Technology

Friday 2 June 14:15–15:00

Active Measurement for Neuroscience
Ross Boczar, UC Berkeley

Monday 5 June 13:15–14:00

Distributed Learning for Cooperative Inference
Cesar A. Uribe, University of Illinois

Monday 5 June 14:05–14:50

Communication-Efficient Decentralized and Stochastic Optimization
Soomin Lee, Georgia Tech

Wednesday 7 June 14:20–15:05

Input-and-state observability of structured network systems
Federica Garin, INRIA Rhône-Alpes

Thursday 8 June 15:30–16:15

Performance Bounds for Remote Estimation under Energy Harvesting Constraints
Ayca Ozcelikkale, Chalmers

Friday 9 June 10:15–11:00

Decentralized control and optimization
Franco Blanchini, University of Udine

Friday 9 June 11:05–11:50

Distributed nonsmooth composite optimization via the proximal augmented Lagrangian
Neil Dhingra, Univeristy of Minnesota

Tuesday 13 June 14:05–15:00

Density flows and optimal mass transport
Tryphon Georgiou, UC Irvine

Tuesday 20 June 10:15–11:00

(Nonsmooth) Control Lyapunov Functions
Philipp Braun, University of Bayreuth

Thursday 22 June 10:15–11:00

Proximal-Proximal-Gradient Method
Ernest Ryu, UCLA

Thursday 22 June 11:05–11:50

Newton-type proximal algorithms for nonconvex optimization
Andreas Themelis, KU Leuven

Tuesday 27 June 10:15–11:00

Computational Co-Designs for Controlling Very Large-Dimensional Power Systems
Aranya Chakrabortty, North Carolina State University

Thursday 29 June 10:15–11:00

Fast ADMM for Semidefinite Programs with Chordal Sparsity
Yang Zheng, University of Oxford

Thursday 29 June 11:05–11:50

A decentralized descent method for the numerical solution of the Nash equilibrum problem
Anna von Heusinger, University of Würzburg