第十届“中国人民大学国际统计论坛”（RUC IFS 2025）将于2025年7月4-6日在中国人民大学召开。大会邀请美国、澳大利亚等国家和地区的知名学者参会，将就“统计学发展史、数理统计、数据科学与人工智能、生物统计学前沿探究、政府统计、金融统计”等问题展开深入交流与讨论。

本次介绍特邀报告人黄坚，报告主题为“Leveraging Large Models for Statistical Analysis”。

黄坚

Title

Leveraging Large Models for Statistical Analysis

Abstract

In this talk, we will explore the potential transformative role of AI, particularly large models, in advancing the fields of statistics and data science. Through examples, we will demonstrate how large models can enhance statistical analysis and tackle challenges that traditional methods often struggle to address. We will discuss applications such as leveraging large model-based data agents (LAMBDA) for coding-free data analysis, using large models for synthetic data generation to enhance statistical analysis and addressing its pitfalls, and exploring flow-based generative models for various nonparametric statistical modeling problems.

Biography

Jian Huang is a Chair Professor of Data Science and Analytics in the Departments of Data Science and AI, and Applied Mathematics at The Hong Kong Polytechnic University. He earned his Ph.D. in Statistics from the University of Washington in Seattle. His current research interests include deep learning, generative models, representation learning, large model statistics, and AI for science.  He was designated a highly cited researcher in the field of Mathematics by Clarivate from 2015 to 2019. He was also included in the list of the top 2% of the world's most cited scientists by Stanford University from 2019 to 2024. He serves on the editorial boards of JASA and JRSSB. Professor Huang is a fellow of ASA and IMS.