Abstract：Integration of statistics and generative AI plays a pivotal role for accelerating trustworthy cross-domain scientific discovery. Recent advances in generative models have dramatically increased the availability and use of synthetic data across scientific domains. While these developments create exciting opportunities for empowering data analysis, they also raise fundamental statistical challenges regarding how synthetic data can be used in a valid, reliable, and principled manner.  In this talk, we first discuss the current landscape of high-fidelity high-dimensional synthetic data generation using generative AI models such as transformer,  diffusion, and flow matching based models. More importantly, we present a principled framework for incorporating synthetic data in downstream statistical analysis that ensures valid statistical inference  even when generative AI models are misspecified. We show that the proposed synthetic data assisted and augmented methods for continuous and discreate outcomes that integrate observed and synthetic data are robust to misspecified black-box generative models and can improve statistical inferential power when the generative AI models are informative.  We demonstrate the utility of these synthetic data assisted methods to the analysis of the UK biobank data, by performing genome-wide association studies (GWAS) of proteomic data and whole-genome sequencing (WGS) analyses of brain imaging phenotypes, and binary mental health survey data both characterized by substantial missingness (about 70-90%).

 

Bio: 林希虹，美国国家科学院院士（2023），美国国家医学院院士（2018），哈佛大学公共卫生学院生物统计学系终身教授和前系主任、数量基因组研究部主任，和统计系终身教授，曾任考普斯（COPSS）委员会主席，Biometrics和 Statistics in Bioscience等杂志主编。林院士于1989年获得清华大学应用数学学士，1994年获华盛顿大学生物统计博士。她主要从事海量基因组学和健康与流行病学数据的统计和机器学习方法的研究及应用，曾获2002年美国公共卫生学会Mortimer Spiegelman奖、2006年统计学界最高奖“考普斯会长奖”、2015年美国国家癌症研究院杰出研究员奖、2017年考普斯 FN David 奖、2022年哈佛大学统计科学Marvin Zelen领导力奖等荣誉。