Departmental Seminar (899): Nicoleta Serban, Georgia Tech
WHEN: November 7, 2019 3:00 pm-5:00 pm
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Distributed Computational Methods For Healthcare Access Modeling
The research presented in this seminar has been motivated by one of my research programs to bring rigor in measurement of and inference on healthcare access, with a recent book to be released, titled Healthcare System Access: Measurement, Inference and Intervention. I will begin with an overview of the underlying framework to assess healthcare access with a focus on health policy making. I will use this framework to motivate the access model, a classic assignment optimization but with many important computational challenges, including spatial dependence in the outcome measures, complex system constraints, large-scale decision space among other. I will present computationally efficient methods for addressing large-scale optimization problems accounting for spatial coupling in the context of uncertainty quantification.
Nicoleta Serban is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. Dr. Serban's education and research trajectory makes her unique in the pursuit of data-driven discovery endeavors. While trained as a mathematician at the most prestigious university in Romania, she pursued a doctoral degree in Statistics at Carnegie Mellon University. Her doctoral research focused on fundamental statistical methods with application to genomics and protein structure determination. After graduation, she changed fields to take a tenure-track position in an engineering school at Georgia Institute of Technology. While at Georgia Tech, she has been engaged in engineering-focused research spanning multiple fields, including enterprise transformation, degradation modeling and monitoring, and healthcare among others. Her research record is quite diverse, from mathematical statistics to modeling to data analysis to qualitative insights on causality and complexity. Dr. Serban’s current research emphasis is on health analytics using massive data sets to inform policy making and targeted interventions. To date, she has published more than 60 journal articles, and a collaborative (with Dr. William B. Rouse) book titled Understanding and Managing the Complexity of Healthcare published by MIT Press. She is the Editor for physical sciences, engineering, and the environment for the Annals of Applied Statistics. She has reviewed for multiple funding agencies and she has served in multiple workshops and meetings organized by the National Academy of Engineering and National Academy of Medicine.