范剑青是美国普林斯顿大学终身教授，Frederick L. Moore'18 冠名金融讲座教授，运筹与金融工程系教授和前任系主任，国际数理统计学会前主席，国家特聘教授，“中央研究院”院士，《Journal of Econometrics 计量经济杂志》的主编。他荣获2000年度的COPSS总统奖，2007年荣获“晨兴华人数学家大会应用数学金奖”，2012年当选中央研究院院士， 2013年获泛华统计学会的“许宝禄奖”， 2014年荣获英国皇家统计学会的“Guy 奖”的银质奖章，2018年美国统计学会的Noether高级学者奖。此外，他还是美国科学促进会(AAAS)、美国统计学会 (ASA)、国际数理统计学会 (IMS)，计量金融学会（SOFIE）的会士，以及国际顶尖统计期刊《Annals of Statistics 统计年鉴》，《Probability Theory and Related Fields概率及其相关领域统》，及《Journal of Business and Economics 商务与经济统计杂志》等的前主编等。他的主要研究领域包括高维统计，机器学习、计量金融、时间序列、非参数建模等。
Jianqing Fan is Frederick L. Moore Professor of Finance, Professor of Statistics, Former Chairman of Department of Operations Research and Financial Engineering and Director of Committee of Statistical Studies at Princeton University, where he directs both financial econometrics and statistics labs. He was the past president of the Institute of Mathematical Statistics and International Chinese Statistical Association. He is co-editing Journal of Business and Economics Statistics, and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, Journal of Econometrics and Econometrics Journal. After receiving his Ph.D. from the University of California at Berkeley, he has been appointed as assistant, associate, and full professor at the University of North Carolina at Chapel Hill (1989-2003), professor at the University of California at Los Angeles (1997-2000), and professor at the Princeton University (2003--). His published work on statistics, economics, finance, machine learning and computational biology has been recognized by The 2000 COPSS Presidents' Award, The 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow in 2009, P.L. Hsu Prize in 2013, Royal Statistical Society Guy medal in silver in 2014, Senior Noether Scholar Award in 2018, and election to Academician of Academia Sinica and follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, American Statistical Association, and Society of Financial Econometrics. His research interest includes high-dimensional statistics, machine learning, financial econometrics, and computational biology.
This talk first gives an overview on the genesis of machine learning and AI and how statistical and computational methods have evolved with growing dimensionality and sample sizes 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 outline the opportunities and challenges of statistical machine learning in financial applications. We will showcase the applications to predicting bond risk premia using macroeconomic time series, portfolio choices, and high-frequency finance.