CS Faculty Candidate Qi Li to Give Research Presentation

Department of Computer Science identifier

Department of Computer Science tenure-track faculty candidate Qi Li will give a research presentation on Thursday, March 21, 2024, from 1-2 p.m. in Rekhi 214. The title of Li’s talk is, “Data Driven Cyber-Physical Energy Systems.”

Talk Abstract

Cyber-physical systems (CPS) play an increasingly important role in developing various fields, including smart homes, smart buildings, and smart transportation. The smart grid is a critical component of CPS. However, with the penetration of distributed energy resources (DERs), smart cities, utilities, third parties, and government agencies are having pressure on managing stochastic power generation such as predicting and reacting to the variations in electric grid.

In my presentation, I’ll introduce the systems I’ve developed to improve the management of DERs, with a particular focus on solar energy. Initially, I will introduce SolarFinder and SolarDetector designed to autonomously collect essential solar installation data in geospatial regions. This data is crucial for efficiently managing solar energy resources. Following that, I will discuss SolarTrader, a system that facilitates decentralized and equitable solar energy trading within residential Virtual Power Plants (VPPs). This system aims to mitigate the impact of the intermittent DERs. During the process of peer-to-peer energy trading, we identified anomalies in solar generation. To tackle this issue, I developed SolarDiagnostics, a system specifically designed to detect potential damage automatically and passively in solar PV arrays, thereby guaranteeing their efficient operation. I will conclude my presentation by sharing my detailing my future research plans, which are directed toward enhancing the management of CPS energy systems.

Candidate Bio

Qi Li is a final year Ph.D. candidate in the Department of Computer Science at the Colorado School of Mines, where she is advised by Dr. Dong Chen. Her research interest is building data-driven computer systems, which combine Data Science and Deep Learning/Machine Learning techniques to solve the efficacy, sustainability, and privacy-related problems in CPS energy systems. She has presented papers in top-tier conferences, including IoTDI, CNS, IPSN, BuildSys, and WiSec. Also, she received recognition for the Best Paper Award at BuildSys’20. In 2023, she was selected for CPS Rising Star. She was also selected to participate in the 2024 CRA-WP Grad Cohort for Women.