Dr. Ye Luo received his Ph.D from Masschusetts Institute of Technology at year 2015. He received his B.S. degree from Massachusetts Institute of Technology at year 2010, majored in Mathematics and Economics. Before joining FBE of HKU, he worked as assistant professor at the economics department in University of Florida. Dr. Ye Luo’s main research insterests include high dimensional econometrics/statistics, machine learning and its empirical applications in economics and finance, for example, applying AI algorithms to develop smart, adaptive automated trading systems, applying big data methods/machine learning in default risk prediction, dynamic demand prediction, etc. He also has interest and expertise in natural language processing.
Dr. Ye Luo has research papers published/forthcoming at Econometrica, Journal of the Royal Statistical Society: Series B, American Economic Review, P&P, etc. Beyond Dr. Ye Luo’s academic research, he has a strong interest in connecting the research in data science to the industry. He has given/being invited to give lectures at DiDi, ShunFeng Express, Novartis, etc.
- “Errors in the Dependent Variable of Quantile Regression Models”, 2021, Econometrica, 89(2), 849-873, with Jerry Hausman, Haoyang Liu and Christopher Palmer
- “The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages”, 2018, Econometrica, 86(6), 1911-1938, with Victor Chernozhukov and Ivan Fernandez-Val
- “An Imputation-regularized Optimization Algorithm for High Dimensional Missing Data Problems and Beyond”, 2018, 80(5), 899-926, Journal of the Royal Statistics Society Series B, with Faming Liang, Bochao Jia, Jingnan Xue and Qizhai Li
- “Core Determining Class and Inequality Selection”, 2017, American Economic Review, 107(5), 274-277, with Hai Wang
- “L2-Boosting for Economic Applications”, 2017, American Economic Review, 107(5), 270-273, with Martin Spindler