Bio

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.

Area(s) of Expertise

Shashaani's research interests include stochastic optimization and Monte Carlo methodology, theory and algorithms, their integration with data science and operations research, and their applications in long-term important problems in society.

Publications

• Complexity of Zeroth- and First-Order Stochastic Trust-Region Algorithms , SIAM Journal on Optimization (2025)
• Dynamic Calibration Framework for Digital Twins Using Active Learning and Conformal Prediction , (2025)
• Dynamic Calibration of Digital Twin via Stochastic Simulation: A Wind Energy Case Study , (2025)
• Worst-Case Approximations for Robust Analysis in Multiserver Queues and Queuing Networks , (2025)
• Building Trees for Probabilistic Prediction via Scoring Rules , Technometrics (2024)
• Calibrating Digital Twins via Bayesian Optimization with a Root Finding Strategy , 2024 WINTER SIMULATION CONFERENCE, WSC (2024)
• Code and Data Repository for Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms , INFORMS journal on computing (2024)
• Comparative Analysis of Distance Metrics for Distributionally Robust Optimization in Queuing Systems: Wasserstein vs. Kingman , 2024 WINTER SIMULATION CONFERENCE, WSC (2024)
• Data Farming the Parameters of Simulation-Optimization Solvers , ACM Transactions on Modeling and Computer Simulation (2024)
• Iteration complexity and finite-time efficiency of adaptive sampling trust-region methods for stochastic derivative-free optimization , IISE Transactions (2024)

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