Research
Our group develops optimization, control, and machine learning tools for large-scale distributed systems.
Projects
Scalable and Distributed Swarm Motion Planning via Integrated Optimization and Machine Learning,
sponsored by NASA (Oct 2019 – Sept 2021),
Reliable and Massively Scalable Grid Optimization via Low-complexity Convex Relaxation,
sponsored by ARPA-E (Dec 2018 – June 2020),
Massively Scalable Computational Methods for Power System Scheduling,
sponsored by NSF (Dec 2018 – June 2020),
Experimental and Hardware-in-the-loop Verification of Optimal and Reliable Control Methods for a Hybrid AC/DC Power Distribution Network,
sponsored by ONR (July 2018 – Oct 2021),
High-fidelity Optimization for Next-generation Shipboard Power Systems,
sponsored by ONR (Mar 2018 – Feb 2021),
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