Mingda Qiao

Mingda Qiao studies learning theory and theoretical computer science in general. His research focuses on the theory of prediction, learning, and decision-making in sequential settings, as well as collaborative federated learning. He regularly reviews for top conferences and journals in theoretical computer science and machine learning.

Mingda was a FODSI postdoc at UC Berkeley and MIT. He received his PhD in Computer Science from Stanford University, and his BEng in Computer Science from Tsinghua University.