Smart grids to optimize energy use
Thursday, January 12, 2023
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).
Related
Forecasting crop yields in an era of extreme weather
Fakhri Karray's new statistical model could help improve sustainability, agricultural systems, and the price of produce.
- forecasting ,
- food ,
- environment ,
- climate ,
- statistics ,
- sustainability ,
- machine learning ,
Accelerating neural network optimization: The power of second-order methods
A team from MBZUAI presented a new approach for optimizing neural networks at the recent NeurIPS conference.
- neural networks ,
- second-order ,
- optimization ,
- neurips ,
- students ,
- research ,
Machine learning 101
From optimal decision making to neural networks, we look at the basics of machine learning and how.....
- prediction ,
- algorithms ,
- ML ,
- deep learning ,
- research ,
- machine learning ,