Associate Professor Mastrangelo joined the department in 2002. She holds the following degrees:
- B.S. in Industrial Engineering, Arizona State University
- M.S. in Industrial Engineering, Arizona State University
- Ph.D. in Industrial Engineering, Arizona State University
Prior to joining UW, she was an Associate Professor of Systems and Information Engineering at the University of Virginia.
Dr. Mastrangelo has several years of industrial manufacturing experience at AlliedSignal Aerospace, Honeywell IACD and Ion Implant Services. She has published over 20 journal papers in the area of empirical stochastic modeling and statistical process monitoring. One of the papers received the Ellis R. Ott Award for significant contribution to the field of quality engineering. She is a member of ASA, ASEE, ASQ, INCOSE, INFORMS, WEPAN, and a senior member of IISE.
Dr. Mastrangelo's primary research field is systems engineering, quality engineering and predictive analytics. Her research interests include the areas of operational modeling for manufacturing, system-level modeling for infectious disease transmission and lung-cancer screening centers, multivariate quality prediction, statistical monitoring methods for manufacturing and hierarchical response modeling.
Dr. Mastrangelo's research involves the application of Industrial Engineering methodologies in healthcare. She is involved in collaborative research with Children's Hospital, Seattle which focuses on the development of an engineering based, systems-level model that will be used to identify and assess alternatives to reduce the risk of infection transmission within pediatric ICUs. She is also the director of the Center for Healthcare Organization Transformation (CHOT) @ UW. CHOT supports the research of major management, clinical, and information technology innovations in healthcare.
Dr. Mastrangelo’s industrial research, sponsored by NSF and the Navy, seeks to understand the effects of lower-level processes on system-level outputs. This is applied to obsolescence management, additive manufacturing, and food chain production. Understanding these models and the effects of competing process models is important to improve productivity, identify and validate quality control parameters, and, ultimately, increase the desired response.
- Paul, B., R. Panat, C. Mastrangelo, D. Kim, D. Johnson. “Manufacturing of Smart Goods: Current State, Future Potential and Research Recommendations,” Submitted to ASME Journal of Micro- and Nano-Manufacturing.
- Erto, P., G. Pallotta, B. Palumbo, C. Mastrangelo. “The Performance of Bayesian Control Charts for Weibull Data Monitoring.” Submitted to Quality Technology and Quantitative Management: A Special Issue in Advances in the Theory and Application of Statistical Process Control.
- Li, Z, Y. Deng, C. Mastrangelo. “Bayesian Hierarchical Model Selection for Degradation-based Reliability Prediction.” Accepted for publication in Journal of Manufacturing Systems.
- Gillan, A. and C. Mastrangelo (2012), “Monitoring Hospital-Associated Infections with Control Charts.” Frontiers in Statistical Quality Control 10, Springer-Verlag, pp . 159-170.
- Kumar, N., C. Mastrangelo and D.C. Montgomery (2011), “Hierarchical Modeling Using Generalized Linear Models,” Quality and Reliability Engineering International, 27(6), pp. 835-842.