Sara Shashaani

Assistant Professor

Sara Shashaani joined the Department of Industrial and Systems Engineering as an assistant professor in January 2019. Prior to joining the NC State faculty, she was a postdoctoral fellow at the Department of Industrial and Operations Engineering at the University of Michigan, where she worked on designing and improving probabilistic predictive models, specifically used for hurricane-induced power outages, with challenges in highly imbalanced datasets and a large set of explanatory variables. Her dissertation research in the area of derivative-free simulation optimization awarded her a Ph.D. degree in Industrial Engineering from Purdue University in 2016. Besides her research, she is passionate about activities that target the environment, community wellness, and science communication.


Ph.D. 2016

Doctor of Philosophy in Industrial Engineering

Purdue University

MSIE 2014

Master of Science in Industrial and Systems Engineering

Virginia Tech

B.Sc. 2009

Bachelor of Applied Computing

Southern Cross University

BSIE 2008

Bachelor of Science in Industrial Engineering

Iran University of Science and Technology

Research Description

Shashaani's research interests include stochastic optimization and Monte Carlo simulation methodology, theory and algorithms, their integration with data and decision analytics, and their applications in long-term important problems in society such as sustainability and environment resiliency, energy infrastructure systems, healthcare, transportation, and human behavior modeling and economics.

Honors and Awards

  • WSC Outstanding Reviewer Award, 2019
  • Best Student Paper Award, Ph.D. Colloquium, Winter Simulation Conference, 2016
  • Ross Fellowship Award, Purdue University, 2015
  • Outstanding Teaching Assistant, ISE Virginia Tech, 2014
  • Best Poster Award, INFORMS Annual Meeting, 2012
  • Visiting Researcher Summer Scholarship, Karlsruhe Institute of Technology and Virginia Tech, 2012


ASTRO for Derivative-Based Stochastic Optimization: Algorithm Description & Numerical Experiments
ASTRO for Derivative-Based Stochastic Optimization: Algorithm Description & Numerical Experiments. (2019). 2019 Winter Simulation Conference (WSC).,
Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks
Chen, T. Y.-J., Beekman, J. A., Guikema, S. D., & Shashaani, S. (2019), JOURNAL OF INFRASTRUCTURE SYSTEMS, 25(2).
ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization
, (2018). SIAM Journal on Optimization.
Multi-Stage Prediction for Zero-Inflated Hurricane Induced Power Outages
Shashaani, S., Guikema, S. D., Zhai, C., Pino, J. V., & Quiring, S. M. (2018), IEEE Access, 6, 62432–62449.
ASTRO-DF: Adaptive sampling trust-region optimization algorithms, heuristics, and numerical experience
, (2016). 2016 Winter Simulation Conference (WSC).
A Simulation Optimization Approach to Epidemic Forecasting
, (2013). PLoS ONE.
Single-machine batch scheduling minimizing weighted flow times and delivery costs
, (2011). Applied Mathematical Modelling.

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