Sara Shashaani
Bio
I am an expert in Monte Carlo simulation methodology, and my interest is in analyzing complex stochastic systems to mitigate risks caused by high-impact but rare events.
SHORT DESCRIPTION OF INTERESTS:
Significant uncertainty in climate and human systems along the coastlines makes decision-making for sustainability and progress challenging and costly. I am interested in providing tools for reliable and optimal decisions that hedge against long-term risks with limited resources. More specifically, I seek solutions (decisions) robust to changes in the projected climate and human systems via distributional analysis of uncertainty.
https://shashaani.wordpress.ncsu.edu/
Publications
- Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms , INFORMS Journal on Computing (2023)
- Improved feature selection with simulation optimization , OPTIMIZATION AND ENGINEERING (2022)
- Personalized Predictions for Unplanned Urinary Tract Infection Hospitalizations with Hierarchical Clustering , Springer Proceedings in Business and Economics (2022)
- Improved Complexity Of Trust-Region Optimization For Zeroth-Order Stochastic Oracles with Adaptive Sampling , 2021 Winter Simulation Conference (WSC) (2021)
- Non-Parametric Uncertainty Bias and Variance Estimation via Nested Bootstrapping and Influence Functions , 2021 Winter Simulation Conference (WSC) (2021)
- Parameter Calibration with Stratified Adaptive Stochastic Trust-region Optimization , INFORMS Workshop on Quality, Statistics, and Reliability (2021)
- Wake Effect Calibration in Wind Power Systems with Adaptive Sampling Based Optimization , IISE Annual Conference Proceedings (2021)
- A SIMULATION OPTIMIZATION APPROACH FOR MANAGING PRODUCT TRANSITIONS IN MULTISTAGE PRODUCTION LINES , 2020 WINTER SIMULATION CONFERENCE (WSC) (2020)
- SIMULATION OPTIMIZATION BASED FEATURE SELECTION, A STUDY ON DATA-DRIVEN OPTIMIZATION WITH INPUT UNCERTAINTY , 2020 WINTER SIMULATION CONFERENCE (WSC) (2020)
- Traffic Signal Control Simulation and Optimization , Winter Simulation Conference (2020)
Grants
The incorporation of adaptive sampling with machine learning has many benefits owing to its proven efficiency. We introduce solving a calibration problem as a stochastic optimization problem where noisy realizations of the deviation between the computer model and the real system are minimized over the choice of model parameters. A variant of Derivative-Free Adaptive Sampling Trust-Region Optimization (ASTRO-DF) combined with bootstrapping and importance sampling ensures, when estimating the objective function, that small sample sizes are chosen away from a critical point and large sample sizes are chosen near a critical point. Finite-time performance and work-complexity of this algorithm are investigated with the calibration of wake effect in wind turbines. Wake represents the energy loss in downstream turbines and characterizing it is essential in designing wind farm layout and controlling turbines for maximum power generation. With large data, calibrating the wake parameters is a derivative-free optimization that can be computationally expensive. But with the proposed algorithm we are able to handle the large data and reach robust solutions by harnessing the uncertainty at two levels: the sample size and the sample choices. We do the former by generating a varying number of bootstraps and the latter by importance sampling.
Groups
- College: College of Engineering
- Themes: Coupled human and natural systems
- Themes: Energy resilience, and innovations in technology
- Expertise: Engineering and Infrastructure
- Expertise: Modeling
- Expertise: Policy and Planning
- Themes: Sustainable agriculture, forestry, and rural, natural resource-based economies