Data-driven spatial models in machine learning have enabled efficient control of production systems. However, most machine learning models are devoted to modeling the mean response, so they are inappropriate to analyze abnormal extreme events that are often the main interests. Since extreme events from tail distribution give rise to prohibitive expenditures in system management, extreme spatial models should be utilized to analyze extreme risks. Recent engineering applications of extreme modeling are limited to simple cases such as univariate modeling, and it is insufficient for complex systems. Moreover, existing extreme spatial models in other domains cannot be directly applied to controllable systems. In this paper, we propose an extreme spatial model that enables the modeling of multi-output response control systems. Robust parameter estimation is proposed for marginal extreme distributions, and efficient composite likelihood estimation is devised to cope with high dimensional problems. The proposed model is applied to the modeling of maximum residual stress in composite aircraft production.

Dr. Xing Wang is an Associate Professor/Researcher at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Her research focuses on statistics, actuarial science, risk management, and insurance, driven by data science and advanced statistical methodologies.  Her research has received numerous academic honors both domestically and internationally, including the Chen Jingrun Future Star Award and selection into the 2025 American Academy of Actuaries Raising Actuary. She is a recipient of multiple prestigious awards, including the Society of Actuaries (SOA) James Hickman Scholar Award, the 2025 Casualty Actuarial Society (CAS) Individual Research Competition Award and the American Statistical Association Early Career Award for Women in Statistics and Data Science. 
Dr. Wang's research has been published in leading journals in risk management and data science, including the Journal of the American Statistical Association (JASA), Insurance: Mathematics and Economics (IME), European Actuarial Journal, and IEEE Transactions. Her work has been twice nominated for the Best Theoretical Paper Award Finalist by the INFORMS.  Dr. Wang has been invited to present at numerous international conferences, including actuarial research conferences, insurance–mathematics–economics forums, and statistics and probability conferences, as well as at leading universities such as the University of Wisconsin–Madison, the University of Waterloo, the University of Minnesota, and the University of Illinois. She has also served as a reviewer for major funding and academic awards, including the SME Education Foundation and the INFORMS Data Mining Best Paper Award. 
Dr. Wang has taught a wide range of core courses, including Risk Modeling, Probability and Simulation in Risk Management, Financial Mathematics, Investments and Financial Markets, and Short-Term Actuarial Models.  Her students have received numerous awards and scholarships, including the State Farm Scholarship, Pinnacle Scholarship, Milliman Scholarship, Katie School Scholarship, and Simpson Scholarship.