Artificial intelligence and data science in smart grids Real-time Aggregate Flexibility A novel learning-based design of flexibility feedback in the coordination of a system operator and an aggregator in a smart grid Distributed Energy Resources Flexibility and Control Extending previous work to multi-agent setting for campus decarbonization Electric Vehicle Charging Time Series Clustering Time series analysis for ACN-Data Learning-augmengted control and decision-making Learning-augmented LQC with Untrusted Predictions Achieve both consistency and robustness for linear quadratic control Penalized Predictive Control via Information Aggregation Theoretical guarantees of regret bounds for a two-controller system Learning-augmented Nonlinear Control with Black-Box Advice Stabilize nonlinear systems with machine learned advice System Identification System Identification with Noisy Data Exploring a specific graph learning task of reconstructing a symmetric matrix that represents an underlying graph using linear measurements.
