Yue Zhao is pursuing a Ph.D. in Machine Learning and Public Policy at the H. John Heinz III College of Information Systems and Public Policy.
Yue Zhao's research focuses on data mining topics related to scalability and reliability–he proposes and designs large-scale learning algorithms, systems, and applications.
He designs and contributes to many widely used machine learning tools and systems, including Python Outlier Detection library (PyOD), combo (for model combination), and SUOD (for large scale unsupervised outlier detection), meta-blocks (for meta learning).
His advisor is Prof. Leman Akoglu, and he has been working with researchers from both industry and academia (U Toronto, UIUC, Tsinghua U, IQVIA, Adobe, PwC, Arima, etc.).