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
- 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)
- COMPARATIVE ANALYSIS OF DISTANCE METRICS FOR DISTRIBUTIONALLY ROBUST OPTIMIZATION IN QUEUING SYSTEMS: WASSERSTEIN VS. KINGMAN , 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)
- Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms , (2024)
- Data Farming the Parameters of Simulation-Optimization , 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)
- REPEATEDLY SOLVING SIMILAR SIMULATION-OPTIMIZATION PROBLEMS: INSIGHTS FROM DATA FARMING , 2024 WINTER SIMULATION CONFERENCE, WSC (2024)
- ROBUST SCREENING AND PARTITIONING FOR FEATURE SELECTION: A BINARY SIMULATION OPTIMIZATION PROBLEM , 2024 WINTER SIMULATION CONFERENCE, WSC (2024)
- Risk score models for urinary tract infection hospitalization , PLOS ONE (2024)
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.