Sara Shashaani
Assistant Professor
- Phone: 919.515.6400
- Email: sshasha2@ncsu.edu
- Office: 4175 Fitts-Woolard Hall
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 simulation 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.
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Industrial Engineering | Purdue University | 2016 |
| MSIE | Master of Science in Industrial and Systems Engineering | Virginia Tech | 2014 |
| BSIE | Bachelor of Science in Industrial Engineering | Iran University of Science and Technology | 2008 |
Honors and Awards
- 2020 | Finalist in Best Service Science Paper Competition, Service Science Section Cluster of INFORMS
- 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 | Best Poster Award, INFORMS Annual Meeting
- 2012 | Visiting Researcher Summer Scholarship, Karlsruhe Institute of Technology and Virginia Tech
Discover more about Sara Shashaani
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
