Topic: Applied Statistics in the Era of Artificial Intelligence: A Review and Vision

Speaker: Simin Zheng and Caleb King

Date: Thursday April 2, 2026 at 9 am PST /Noon EST / 6pm Central European Time

Abstract

The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. 

Speaker Bio

Simin Zheng is currently a PhD candidate in Statistics at Virginia Tech, focusing on research in the reliability of AI systems, AI-driven statistical analysis, and large language model performance evaluation. She will join JMP this summer as a full-time Research Statistician Developer in the DOE and Reliability group, working on reliability-related research and development.

Dr. Caleb King is a senior developer for the DOE & Reliability group at JMP Statistical Discovery LLC. He received his PhD in statistics from Virginia Tech. His research interests included design of experiments and reliability. Prior to joining JMP, he worked as a senior statistician at Sandia National Laboratories. Dr. King currently also serves as Past Chair for ISEA.