题  目：统计学与人工智能(Statistics and AI)

主讲人：范剑青教授（Princeton University）

时  间：5月28号(星期二)下午3:30--4:30

地  点：首师大校本部新教二楼120

报告摘要: 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 challenges of statistical sciences at this crossroad and offer some prospects. We will offer a general robustification principle and show how to use factor adjustments to deal with dependent measurements. In particular, Factor Adjusted Robust Multiple testing (FarmTest) and Model selection (FarmSelect) will be introduced for high-dimensional statistical inference and model selection. The effectiveness of these methods will be revealed with an application to predicting bond risk premia using macroeconomic time series. Further insights on the prospects of machine learning and AI will be offered.

主讲人简介:范剑青（Jianqing Fan），世界著名统计学家。现为美国普林斯顿大学金融学讲座教授，复旦大学大数据学院教授、院长，台湾“中央研究院”院士。曾获COPSS奖（国际统计学领域最高奖项），洪堡基金会终身成就奖，晨兴华人数学家大会应用数学金奖，国际泛华统计学会“许宝禄奖”，英国皇家统计学会“Guy Medal”银质奖章，诺特资深学者奖。现为国际统计学会、国际数理统计学会、美国统计学会、美国科学促进会、国际计量金融学会的Fellow。主要研究领域为高维统计、大数据科学、机器学习、经济学与金融学等。学术成果发表在AoS、JASA、JRSS、Econometrica、JoE、JoFE等统计以及经济的国际顶级期刊上。

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