Mingda Qiao a fifth-year PhD student in Computer Science at Stanford University, advised by Gregory Valiant. He works on the theoretical foundations of machine learning and artificial intelligence. His doctoral research focuses on the theoretical aspects of prediction, learning, and decision-making in sequential settings, as well as decision tree learning. With his collaborators, his contributions include the first non-trivial lower bound for sequential calibration, and a faster algorithm for properly learning decision trees. Prior to Stanford, Mingda received his BEng in Computer Science from Yao Class at Tsinghua University in 2018.