A mathematician by trade, Molei Tao, typically uses mathematics to design algorithms and solve physical science problems like how planets move. Recently, he became attracted to machine learning, an area that according to him, contains numerous interesting problems that are mathematically exciting and can benefit from modern mathematical tools. This year, Tao published his first machine learning conference paper, and this work was awarded the best paper award at the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS). In flat spaces, the approach of adding momentum for accelerating the training of machine learning models has already been tremendously successful, and this new progress expands the applicability of the popular and powerful idea. Tao felt fortunate to win this recognition.