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My group's research focuses on the fundamental theory of control, learning, and optimization and its applications to a variety of topics (neuroscience, autonomous driving, smart grid, cloud computing, cell biology, economics, epidemics etc).
Imagine a hyper-connected world in which stable, robust, and efficient autonomous devices seamlessly integrate through the Internet of Things (IoT) to aid people. To achieve stability and robustness, these devices should be equipped with real-time learning and control algorithms that autonomously optimize policies in response to internal and external changes. Such algorithms should be efficient and implementable on-board, even in low-cost hardware. However, their limited hardware and large scale may cause noisy signals and require distributed control and/or asynchronous computation. In my group, we create theoretical foundations and computational tools to enhance the stability, robustness, and efficiency of autonomous systems.