PhD student Yusi Fang was recently selected for the Early Career Award by the American Statistical Association (ASA) Section on Statistics and Epidemiology and has been selected to give a talk at the ASA's Joint Statistical Meetings in 2024 in Portland, Oregon. Along with his advisors George Tseng and Zhao Ren, he created a powerful method for disease surveillance that can effectively detect outbreaks in their early stages, even in regions with varying signal sparsity levels. Their approach is more flexible and robust than existing methods, offering improved performance in identifying both ultra-sparse and moderately sparse signals, as demonstrated with a COVID-19 surveillance example. Congratulations!