This talk first gives an overview on the genesis of machine learning and AI and how statistical and computational methods have evolved with big data and become the foundation of modern machine learning and AI. It will also outline how ideas of trading modeling biases and variances have been developed into high-dimensional statistics and machine learning, with focus on deep learning models. We will highlight three applications: portfolio choices with text data via feature screening, predictability of the momentum and duration in high-frequency finance via several statistical machine learning methods, and sparse portfolio allocation and Sharpe ratio estimation.

Jianqing Fan, is a statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at Princeton University where he chaired the department from 2012 to 2015. He is the winner of the 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013) and Guy Medal in Silver (2014). He got elected to Academician from Academia Sinica in 2012.


Prof. Fan is interested in statistical theory and methods in data science, statistical machine learning, finance, economics, computational biology, biostatistics with particular skills on high-dimensional statistics, nonparametric modeling, longitudinal and functional data analysis, nonlinear time series, wavelets, among others.