Qi Li is a final year Ph.D. student at the Colorado School of Mines CS department. Her research interest is building data-driven computer systems, which combine Data Science and Deep Learning/Machine Learning techniques with a deep understanding of the domain-specific information from CPS systems. Her research has been published in top-tier conferences such as IoTDI23, CNS22, IPSN21, IPSN20, BuildSys20, and IGSC20, and she has released the source code and dataset to the research community. Her paper “SolarTrader” was also selected as the Best Paper by the 2020 ACM BuildSys committee.
Cyber-Physical Systems (CPS) play an increasingly important role in the development of various fields, including smart homes, smart buildings, infrastructure, smart cities, and smart medicine. The smart grid is a crucial part of CPS, and as Distributed Solar Energy Resources (DSER) are increasingly deployed and integrated, new challenges emerge. For instance, DSER performance models are not very accurate and cannot detect damage or degradation conditions accurately. The energy sharing among DSERs is suffering low efficiency and low fairness. To address these concerns, my research focuses on developing data-driven solutions using deep learning, machine learning, and image processing techniques to improve the management and control of CPS systems.