Sara Shashaani
Assistant Professor
- Phone: 919.515.6400
- Email: [email protected]
- 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
- Extrapolation-based Direct Search for Nonsmooth Stochastic Zeroth-Order Optimization
- Palmieri, A., Rinaldi, F., & Shashaani, S. (2026, July 31), ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2607.29408
- Root Finding and Metamodeling for Rapid and Robust Computer Model Calibration
- Jeon, Y., & Shashaani, S. (2026, March 24), ArXiv (Cornell University), Vol. 3. https://doi.org/10.48550/arxiv.2603.23790
- Stratified adaptive sampling for derivative-free stochastic trust-region optimization
- Amici, G., Shashaani, S., & Jain, P. (2026, March 31), ArXiv (Cornell University), Vol. 3. https://doi.org/10.48550/arxiv.2604.00178
- Complexity of Zeroth- and First-Order Stochastic Trust-Region Algorithms
- Ha, Y., Shashaani, S., & Pasupathy, R. (2025, September 17), SIAM Journal on Optimization, Vol. 35, pp. 2098–2127. https://doi.org/10.1137/24m1664484
- Dynamic Calibration Framework for Digital Twins Using Active Learning and Conformal Prediction
- Sürer, Ö., & Shashaani, S. (2025, December 7). , (Vol. 12). Vol. 12. https://doi.org/10.1109/wsc68292.2025.11338975
- Dynamic Calibration of Digital Twin via Stochastic Simulation: A Wind Energy Case Study
- Jeon, Y., Shashaani, S., Byon, E., & Jain, P. (2025, December 7). , (Vol. 12). Vol. 12. https://doi.org/10.1109/wsc68292.2025.11338923
- Worst-Case Approximations for Robust Analysis in Multiserver Queues and Queuing Networks
- Eun, H.-K., Shashaani, S., & Barton, R. R. (2025, December 7). , (Vol. 12). Vol. 12. https://doi.org/10.1109/wsc68292.2025.11339009
- Building Trees for Probabilistic Prediction via Scoring Rules
- Shashaani, S., Sürer, Ö., Plumlee, M., & Guikema, S. (2024, April 15), Technometrics, Vol. 5. https://doi.org/10.1080/00401706.2024.2343062
- Calibrating Digital Twins via Bayesian Optimization with a Root Finding Strategy
- Jeon, Y., & Shashaani, S. (2024, December 15), 2024 WINTER SIMULATION CONFERENCE, WSC, pp. 335–346. https://doi.org/10.1109/wsc63780.2024.10838781
- Code and Data Repository for Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms
- Ha, Y., Shashaani, S., & Menickelly, M. (2024, November 27), INFORMS Journal on Computing. https://doi.org/10.1287/ijoc.2024.0575.cd
