Jingkun Gao is a fifth-year Ph.D. student in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU). He is interested in making our buildings more energy efficient through data science. He received an M.S. in Machine Learning from CMU and a B.S. degree in Materials Chemistry from USTC in China. He is currently working on reducing the manual effort required to implement and deploy automated fault detection and diagnosis (FDD) algorithms for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Approximately 12% of the energy consumed by commercial buildings in the United States is attributable to faulty operation of their HVAC systems, and there are hundreds of FDD algorithms developed to detect and diagnose these faults. However, deploying these algorithms currently requires hundreds of hours of manual labor for a single building. Jingkun is developing machine learning algorithms that reduce this number to nearly zero.