Christina M. Mastrangelo

 

Phone: (206) 543-5439
Fax: (206) 685-3072
Email: mastr@u.washington.edu

 

 

BACKGROUND

Dr. Christina Mastrangelo is an Associate Professor of Industrial & Systems Engineering at the University of Washington. She holds BS, MS and Ph.D. degrees in Industrial Engineering from Arizona State University. Prior to joining UW in 2002, 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 IIE.

SCHOLARLY PURSUITS

Dr. Mastrangelo's primary research field is systems engineering, quality engineering and empirical stochastic modeling. Her research interests include the areas of operational modeling for semiconductor manufacturing, system-level modeling for infections disease control, multivariate quality control, statistical monitoring methods for continuous and batch processing and multi-response modeling.

System Modeling for Infectious Disease Control

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.

Operational Modeling for Semiconductor Manufacturing

This research, sponsored by NSF, seeks understanding of the effects of lower-level processes on system-level outputs of product performance characteristics, such as yield, conformance to specifications, defectivity and quality. Understanding this and the effects of competing process models is important to improve productivity, identify and validate quality control parameters, and, ultimately, increase yield.

Multivariate Process Monitoring

This research, previously sponsored by NSF, studies empirical methods for process monitoring and control in manufacturing and service industries. The manufacturing environment in which statistical methods are used is changing rapidly. The reality of modern production and service processes is trending toward shorter production runs, more data, higher quality requirements, and greater computing capability. This work in multivariate modeling methods for dynamic systems is oriented toward this environment which requires more sophisticated modeling and the integration of more domain knowledge.

UNDERGRADUATE TEACHING

GRADUATE TEACHING