Events

Directly training deep learning models for applications in large-scale cyber-physical systems can be intractable due to the large number of components and decision variables. Instead, we focus on exploiting spatial […]

The inverse design paradigm has emerged as a transformative approach for the synthesis of nanophotonic structures, offering a powerful alternative to conventional intuition-driven design. By approaching photonic device design as […]

Communication networks support a wide range of applications in multi-agent systems by solving core problems such as routing, scheduling, and resource allocation. In this thesis, we focus on data-driven routing […]