About

Sara Shashaani is an Associate Professor at NC State University in the Edward P. Fitts Department of Industrial and Systems Engineering. Her research focuses on areas related to industrial engineering, with an emphasis on systems optimization, human factors, and ergonomics. She contributes to the academic community through her role in advancing knowledge in these fields and supporting the department's educational and research missions.

Research topics

• Computer Science
• Mathematical optimization
• Programming language
• Mathematics
• Software engineering
• Algorithm
• World Wide Web
• Aerospace engineering
• Meteorology
• Statistics
• Physics
• Engineering

Selected publications

• Stratified adaptive sampling for derivative-free stochastic trust-region optimization

  ArXiv.org · 2026-03-31

  articleOpen access

  There is emerging evidence that trust-region (TR) algorithms are very effective at solving derivative-free nonconvex stochastic optimization problems in which the objective function is a Monte Carlo (MC) estimate. A recent strand of methodologies adaptively adjusts the sample size of the MC estimates by keeping the estimation error below a measure of stationarity induced from the TR radius. In this work we explore stratified adaptive sampling strategies to equip the TR framework with accurate estimates of the objective function, thus optimizing the required number of MC samples to reach a given ε-accuracy of the solution. We prove a reduced sample complexity, confirm a superior efficiency via numerical tests and applications, and explore inexpensive implementations in high dimension.

  PublisherOA PDF

• Root Finding and Metamodeling for Rapid and Robust Computer Model Calibration

  ArXiv.org · 2026-03-24

  articleOpen accessSenior author

  We concern computer model calibration problem where the goal is to find the parameters that minimize the discrepancy between the multivariate real-world and computer model outputs. We propose to solve an approximation using signed residuals that enables a root finding approach and an accelerated search. We characterize the distance of the solutions to the approximation from the solutions of the original problem for the strongly-convex objective functions, showing that it depends on variability of the signed residuals across output dimensions, as wells as their variance and covariance. We develop a metamodel-based root finding framework under kriging and stochastic kriging that is augmented with a sequential search space reduction. We derive three new acquisition functions for finding roots of the approximate problem along with their derivatives usable by first-order solvers. Compared to kriging, stochastic kriging accounts for observational noise, promoting more robust solutions. We also analyze the case where a root may not exist. Our analysis of the asymptotic behavior in this context show that, since existence of roots in the approximation problem may not be known a priori, using new acquisition functions will not compromise the outcome. Numerical experiments on data-driven and physics-based examples demonstrate significant computational gains over standard calibration approaches.

  PublisherOA PDF

• Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization

  arXiv (Cornell University) · 2026-04-16

  preprintOpen access

  We propose a stochastic nonconvex optimization algorithm that achieves almost sure $\tilde{\mathcal{O}}(ε^{-1.5})$ iteration complexity for problems with smooth objective functions and gradients only observable with noise. The mean-zero stochastic noise is decision-dependent and has unbounded support with subexponential tail, allowing our framework to cover a broad class of problems. The improved almost sure iteration complexity is achieved with a new variant of the adaptive sampling trust-region optimization (ASTRO) augmented with an adaptively regularized local model, which we term Reg-ASTRO. Adaptive sampling ensures that the estimation precision is aligned with a measure of stationarity, so that iterates closer to stationarity trigger higher accuracy requirement for sampling. A key analytical challenge arises because the trust-region radius and regularization are coupled and not determined prior to gradient estimation at each iteration. We further establish an almost sure $\tilde{\mathcal{O}}(ε^{-4.5})$ sample complexity for Reg-ASTRO, which improves to $\tilde{\mathcal{O}}(ε^{-3.5})$ under stronger regularity conditions and use of common random numbers, substantially outperforming first-order methods in theory and numerical experiments.

  PublisherDOI

• Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization

  ArXiv.org · 2026-04-16

  articleOpen access

  We propose a stochastic nonconvex optimization algorithm that achieves almost sure $\tilde{\mathcal{O}}(ε^{-1.5})$ iteration complexity for problems with smooth objective functions and gradients only observable with noise. The mean-zero stochastic noise is decision-dependent and has unbounded support with subexponential tail, allowing our framework to cover a broad class of problems. The improved almost sure iteration complexity is achieved with a new variant of the adaptive sampling trust-region optimization (ASTRO) augmented with an adaptively regularized local model, which we term Reg-ASTRO. Adaptive sampling ensures that the estimation precision is aligned with a measure of stationarity, so that iterates closer to stationarity trigger higher accuracy requirement for sampling. A key analytical challenge arises because the trust-region radius and regularization are coupled and not determined prior to gradient estimation at each iteration. We further establish an almost sure $\tilde{\mathcal{O}}(ε^{-4.5})$ sample complexity for Reg-ASTRO, which improves to $\tilde{\mathcal{O}}(ε^{-3.5})$ under stronger regularity conditions and use of common random numbers, substantially outperforming first-order methods in theory and numerical experiments.

  PublisherOA PDF

• Root Finding and Metamodeling for Rapid and Robust Computer Model Calibration

  arXiv (Cornell University) · 2026-03-24

  preprintOpen accessSenior author

  We concern computer model calibration problem where the goal is to find the parameters that minimize the discrepancy between the multivariate real-world and computer model outputs. We propose to solve an approximation using signed residuals that enables a root finding approach and an accelerated search. We characterize the distance of the solutions to the approximation from the solutions of the original problem for the strongly-convex objective functions, showing that it depends on variability of the signed residuals across output dimensions, as wells as their variance and covariance. We develop a metamodel-based root finding framework under kriging and stochastic kriging that is augmented with a sequential search space reduction. We derive three new acquisition functions for finding roots of the approximate problem along with their derivatives usable by first-order solvers. Compared to kriging, stochastic kriging accounts for observational noise, promoting more robust solutions. We also analyze the case where a root may not exist. Our analysis of the asymptotic behavior in this context show that, since existence of roots in the approximation problem may not be known a priori, using new acquisition functions will not compromise the outcome. Numerical experiments on data-driven and physics-based examples demonstrate significant computational gains over standard calibration approaches.

  PublisherDOI

• Stratified adaptive sampling for derivative-free stochastic trust-region optimization

  arXiv (Cornell University) · 2026-03-31

  preprintOpen access

  There is emerging evidence that trust-region (TR) algorithms are very effective at solving derivative-free nonconvex stochastic optimization problems in which the objective function is a Monte Carlo (MC) estimate. A recent strand of methodologies adaptively adjusts the sample size of the MC estimates by keeping the estimation error below a measure of stationarity induced from the TR radius. In this work we explore stratified adaptive sampling strategies to equip the TR framework with accurate estimates of the objective function, thus optimizing the required number of MC samples to reach a given ε-accuracy of the solution. We prove a reduced sample complexity, confirm a superior efficiency via numerical tests and applications, and explore inexpensive implementations in high dimension.

  PublisherDOI

• Complexity of Zeroth- and First-Order Stochastic Trust-Region Algorithms

  SIAM Journal on Optimization · 2025-09-17 · 2 citations

  article
  PublisherDOI

• Worst-Case Approximations for Robust Analysis in Multiserver Queues and Queuing Networks

  2025-12-07

  article
  PublisherDOI

• Dynamic Calibration of Digital Twin via Stochastic Simulation: A Wind Energy Case Study

  2025-12-07

  article
  PublisherDOI

• Dynamic Calibration Framework for Digital Twins Using Active Learning and Conformal Prediction

  2025-12-07

  articleSenior author
  PublisherDOI

Recent grants

• Collaborative Research: Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization

  NSF · $282k · 2023–2026

Frequent coauthors

• Kimia Vahdat

  North Carolina State University

  10 shared

• Raghu Pasupathy

  9 shared

• Pranav Jain

  Indraprastha Institute of Information Technology Delhi

  8 shared

• Eunshin Byon

  University of Michigan–Ann Arbor

  5 shared

• Yunsoo Ha

  5 shared

• David J. Eckman

  Texas A&M University

  4 shared

• Seth D. Guikema

  University of Michigan–Ann Arbor

  4 shared

• Julie Swann

  North Carolina State University

  3 shared

Education

• PhD, Industrial Engineering

  Purdue University

  2016

• Master of Science, Systems Engineering and Operations Research

  Virginia Tech

  2014

• Master of Science, Industrial Engineering

  Purdue University

  2011

• Bachelor of Science, Applied Computing

  Southern Cross University

  2008

• Bachelor of Science, Industrial Engineering

  Iran University of Science and Technology

  2008

Awards & honors

• 2024 | ISE Bowman Faculty Scholar Award
• 2023 | IISE Modeling and Simulation Division Teaching Award
• 2022 | Outstanding Contribution in Reviewing, Journal of Sim…
• 2022 | Distinguished Service as the Proceedings Editor, Wint…
• 2022 | Research Innovation Seed Funding Award, North Carolin…