Jul 23: Paper “Risk Score Models for Unplanned Urinary Tract Infection Hospitalization” with Alizadeh, Vahdat, Ozaltin, and Swann submitted.

Jun 23: Paper “Wake Effect Parameter Calibration with Large-Scale Field Operational Data using Stochastic Optimization” published in Applied Energy! Here are some highlights:

  • Engineering wake models have parameters that crucially affect their performance.
  • Wake parameters can be calibrated as constants or functions of other wind variables.
  • Stochastic optimization (SO) provides accurate and reliable wake calibrations.
  • Both point and functional calibration of wake parameters can be done well with SO.
  • A derivative-free trust-region SO method provides robust wake calibration.
  • Efficiently implemented trust-region SO uses adaptive sampling and variance reduction.
  • Stratifying data based on wind characteristics effectively reduces variance during SO.
  • Choosing strata of wind data and dynamically sampling from each expedites calibration.
  • Robust wake calibration helps understanding power deficit patterns in wind farms.
  • Good prediction of power deficit due to wake enables optimal design of new wind farms.

Jun 23: The group presented two talks at the 1st INFORMS Conference on Quality, Statistics, and Reliability:

  1. “Simulation Optimization with Stratified Adaptive Sampling Wind Energy Calibration Case Study” with Jain and Byon.
  2. “Stratified Sampling for Stochastic Computer Models with Multivariate Inputs” with Byon, Park, and Ko.

May 23: Paper “Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization” is submitted and accessible on arxiv.

Nov 2022: Paper “Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms” co-authored by Shashaani accepted in INFORMS Journal on Computing.

Oct 2022: New paper “Monte Carlo based Machine Learning” accepted for the proceedings of the 2022 International Conference on Operations Research.

July 2022: Robust prediction error estimation for machine learning with Monte Carlo by Vahdat and Shashaani is now on arxiv.

 

July 2022: Shashaani wins the AAUW 2022 Research Publication Grants in Engineering, Medicine and Science. Founded in 1881, AAUW is one of the world’s largest sources of funding for graduate women, due to the generosity and legacy of generations of AAUW members. These prestigious awards are highly competitive and selective. The grant will financially support the research group in achieving its academic and professional goals.