报告学者：肖骞

报告者单位：上海交通大学

报告时间：2026年8月10日（周一）上午10:00-11:00

报告地点：7号楼 会议室7215

报告摘要：Computer experiments have become increasingly popular in both scientific research and industrial applications. Recently, there are emerging interests in the analysis of computer experiments with binary outputs. Statistical modeling for such deterministic black-box systems can be challenging, since they require model interpolation and appropriate uncertainty quantification. The commonly used Gaussian process-based methods consider a latent-space approach to model the binary output, but cannot directly interpolate the output in the observational space. In this work, we propose a novel probabilistic predictive model based on the Hopfield process, which focuses on enabling model interpolation on binary outputs and desirable uncertainty quantification. An efficient model estimation is developed. We also enhance uncertainty quantification and model inference via an empirical Bayesian approach. Moreover, we propose a new active learning procedure to efficiently identify the decision boundary. Both theoretical investigations and numerical studies are conducted to elaborate on the merits of the proposed methods.

报告学者简介：肖骞，上海交通大学数学科学学院统计系长聘副教授、数据智能研究中心主任。2017年于美国加州大学洛杉矶分校（UCLA）获得统计学博士学位，曾在美国佐治亚大学统计系任助理教授及长聘副教授。其主要研究方向包括：最优计算机试验设计与分析和不确定性量化。其研究成果发表于《Annals of Statistics》《Journal of the American Statistical Association》《Biometrika》《Technometrics》等统计学顶级期刊。主持国家重点研发计划青年科学家项目，国家级青年人才计划，小米青年学者。