Yanwen Xu

Yanwen Xu is an Assistant Professor in the Department of Mechanical Engineering at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. Her research bridges data analytics with engineering design to solve complex reliability challenges under uncertainty.

Education:

• Ph.D. in Industrial Engineering, University of Illinois at Urbana-Champaign (2023)
• M.S. in Applied Statistics, University of Illinois at Urbana-Champaign (2021)
• M.S. in Industrial Engineering, University of Illinois at Urbana-Champaign (2019)
• B.S. in Mathematics and Applied Mathematics, Huazhong University of Science & Technology (2016)

Research Interests: Dr. Xu pioneers physics-informed machine learning for engineering reliability, integrating stochastic optimization with uncertainty quantification to develop robust predictive models. Her work spans rare event simulation, sensor design optimization, and battery health prognostics, emphasizing domain-specific physics integration for enhanced accuracy in safety-critical systems.

Publication Trends: Her 2022-2023 publications reveal a cohesive focus on physics-informed ML applications in reliability engineering, featuring Gaussian process modeling and sequential sampling techniques. Key themes include sensor design optimization, battery degradation prediction, and rare event probability estimation across mechanical and electrical systems.

Scientific Awards: Recognized with prestigious honors including the First Place Thomas L. Fagan, Jr., Student Paper Award at RAMS (2022) and UIUC's Sharp Outstanding Graduate Student Award (2022), her contributions are further validated by:

• Graduate College Conference Presentation Award, UIUC (2021)
• 2020 QCRE Best Paper Award from IISE Annual Conference (2020)

Dr. Xu leads the RaDA (Reliability and Data Analytics) Lab in ECSW 3.460, mentoring graduate researchers and actively recruiting Graduate Research Assistants for Fall 2024. Her lab drives innovation at the intersection of data science and engineering reliability, with current projects focused on physics-informed predictive modeling for safety-critical applications.