Welcome to the Digital Futures focus period on Learning in Networks: Structure, Dynamics, and Control.
Networks underpin our societies and provide examples of challenging large-scale systems. These include communication and transportation networks, power grids, biological systems, and social networks resulting from the interaction of users on online platforms. To reap all the benefits of these networks and optimize their operation, we need to address challenges arising due to our a-priori lack of knowledge of their structure and dynamics, to various kinds of uncertainties (e.g. users' demand and behavior), to the combinatorial nature of the decision space. Addressing these challenges calls for multi-disciplinary efforts. The focus period will bring together researchers with expertise in machine learning, probability theory and random structures to tackle hard inference problems in random graphs and networks, and to analyze the stochastic processes arising in these networks. It will also gather experts in networked control and reinforcement learning to address control and optimization problems.
The focus period includes:
1. The Stochastic Networks conference July 1 - July 5;
2. A workshop on Learning in Networks June 29 and July 4.