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.

Research Interests

Shashaani’s research interests include stochastic optimization and Monte Carlo methodology, theory and algorithms, their integration with data science and 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.

Shashaani’s personal research website

Education

DegreeProgramSchoolYear
Ph.D.Doctor of Philosophy in Industrial EngineeringPurdue University2016
MSISEORMaster of Science in Industrial and Systems Engineering and Operations ResearchVirginia Tech2014
MSIEMaster of Science in Industrial EngineeringPurdue University2011
BSIEBachelor of Science in Applied ComputingSouthern Cross University2009
BSIEBachelor of Science in Industrial EngineeringIran University of Science and Technology2008

Honors and Awards

  • 2023 | IISE Modeling and Simulation Division Teaching Award
  • 2023 | National Science Foundation CMMI-2226347 Grant for Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization
  • 2022 | AAUW Research Publication Grant in Engineering, Medicine and Science, American Educational Research Association
  • 2022 | Outstanding Contribution in Reviewing, Journal of Simulation
  • 2022 | Distinguished Service as the Proceedings Editor, Winter Simulation Conference
  • 2022 | Research Innovation Seed Funding Award, North Carolina State University
  • 2021 | Faculty Research and Professional Development Award, North Carolina State University
  • 2020 | Finalist in Best Service Science Paper Competition, Service Science Cluster of INFORMS
  • 2019 | Outstanding Reviewer Award, Winter Simulation Conference
  • 2016 | Best Student Paper Award, Ph.D. Colloquium, Winter Simulation Conference
  • 2015 | Ross Fellowship Award, Purdue University
  • 2014 | Outstanding Teaching Assistant, ISE Virginia Tech
  • 2012 | Visiting Researcher Summer Scholarship, Karlsruhe Institute of Technology & Virginia Tech

 

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Publications

Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization
Ha, Y., & Shashaani, S. (2024), IISE Transactions. https://doi.org/10.1080/24725854.2024.2335513
Adaptive Robust Genetic Algorithms with Ranking and Selection
, (2023). 2023 Winter Simulation Conference.
Diagnostic Tools for Evaluating and Comparing Simulation- Optimization Algorithms
Eckman, D. J., Henderson, S. G., & Shashaani, S. (2023, January 5), INFORMS JOURNAL ON COMPUTING, Vol. 1. https://doi.org/10.1287/ijoc.2022.1261
Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization
, (2023). Retrieved from https://arxiv.org/abs/2305.10650
Monte Carlo Based Machine Learning
Shashaani, S., & Vahdat, K. (2023), In Lecture Notes in Operations Research. https://doi.org/10.1007/978-3-031-24907-5_75
On Common-Random-Numbers and the Complexity of Adaptive Sampling Trust-Region Methods
, (2023, August 4). Retrieved from https://optimization-online.org website: https://optimization-online.org/wp-content/uploads/2023/08/astrodf-complexity-online-version.pdf
Predicting additive manufacturing defects with robust feature selection for imbalanced data
Houser, E., Shashaani, S., Harrysson, O., & Jeon, Y. (2023, May 13), IISE TRANSACTIONS, Vol. 5. https://doi.org/10.1080/24725854.2023.2207633
Risk Score Models for Unplanned Urinary Tract Infection Hospitalization
Alizadeh, N., Vahdat, K., Shashaani, S., Swann, J. L., & Ozaltin, O. (2023, August 9). , . https://doi.org/10.1101/2023.08.06.23293723
Robust Output Analysis with Monte-Carlo Methodology
, (2023).
SimOpt: A Testbed for Simulation-Optimization Experiments
Eckman, D. J., Henderson, S. G., & Shashaani, S. (2023, March 9), INFORMS JOURNAL ON COMPUTING, Vol. 3. https://doi.org/10.1287/ijoc.2023.1273

View all publications via NC State Libraries

Sara Shashaani