Sara Shashaani
Assistant Professor
- Phone: 919.515.6400
- Email: sshasha2@ncsu.edu
- Office: 4175 Fitts-Woolard Hall
- Website: https://shashaani.wordpress.ncsu.edu
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
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Industrial Engineering | Purdue University | 2016 |
| MSISEOR | Master of Science in Industrial and Systems Engineering and Operations Research | Virginia Tech | 2014 |
| MSIE | Master of Science in Industrial Engineering | Purdue University | 2011 |
| BSIE | Bachelor of Science in Applied Computing | Southern Cross University | 2009 |
| BSIE | Bachelor of Science in Industrial Engineering | Iran University of Science and Technology | 2008 |
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
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
