Smart grids to optimize energy use

Smart grids optimize energy use and local energy sharing to reduce load on and disruption of energy services. Typically, smart grids rely on rich user data to optimize the grid, but that puts individual users at risk for identity theft.

Borrowing from the success of smart systems in the healthcare sector MBZUAI researchers in Professor Martin Takac’s team have applied federated learning to efficiently optimize smart grids while safeguarding user data.

Takáč’s current research interests include the design and analysis of algorithms for machine learning including large-scale convex/non-convex optimization problems in a distributed and federated learning setting, applications of machine learning and high performance computing (HPC).

Prior to joining MBZUAI, Takáč was an associate professor in the Department of Industrial and Systems Engineering at Lehigh University in Pennsylvania, USA. He received several awards during this period, including the Best Ph.D. Dissertation Award by the OR Society (2014), Leslie Fox Prize (2nd Prize; 2013) by the Institute for Mathematics and its Applications, and INFORMS Computing Society Best Student Paper Award (runner up; 2012).