Nov 23: Shashaani and colleagues from Biological & Agricultural Engineering and Marine, Earth and Atmospheric Sciences win a 1-year grant from NC Department of Justice to minimize impacts of climate variability on NC swine farms through the use of simulations and decision-making under uncertainty
Nov 2023: New paper “Statistical Inference on Simulation Output: Batching as an Inferential Device” with Y. Jeon and R. Pasupathy submitted.
Oct 23: Ha presents oracle complexity with adaptive sampling at the Midwest Optimization Meeting held at the University of Michigan:
Oct 2023: Shashaani’s group have 5 presentations at the 2023 INFORMS annual meeting:
Sep 2023: Shashaani selected as the Southern Cross University’s International Alumnus of the Year
Aug 23: Vahdat successfully defended her PhD dissertation and will be a data analyst in Liberty Mutual after graduate school. Congratulations Kimia!
Aug 23: Paper “On Common-Random-Numbers and the Complexity of Adaptive Sampling Trust-Region Methods” with Ha and Pasupathy now available on optimization-online.
Jul 23: Paper “Risk Score Models for Unplanned Urinary Tract Infection Hospitalization” with Alizadeh, Vahdat, Ozaltin, and Swann submitted.
Jul 23: Shashaani featured in an ISE News article here.
Jul 23: Paper “Building Trees for Probabilistic Prediction via Scoring Rules” with Surer, Plumlee, and Guikema submitted.
Jun 23: Paper “Strata Design in Stochastic Simulations with Multivariate Inputs” with Park, Byon, and Ko 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: Paper “Data Farming Simulation Optimization Solver Parameters” with Eckman and Sanchez submitted.
Jun 23: The group presented two talks at the 1st INFORMS Conference on Quality, Statistics, and Reliability:
- “Simulation Optimization with Stratified Adaptive Sampling Wind Energy Calibration Case Study” with Jain and Byon.
- “Stratified Sampling for Stochastic Computer Models with Multivariate Inputs” with Byon, Park, and Ko.
Jun 23: Pranav Jain unconditionally passes his preliminary doctoral exam. Congratulations Pranav!
May 23: Jain to join National Renewable Energy Laboratory (NREL) as an intern in Summer 2023.
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.
May 23: sneak peak of the ISE graduation celebration.
Apr 23: Two papers by Shashaani and co-authors Eckman and Henderson published in Vol. 35, No. 2 of INFORMS Journal of Computing
Apr 23: Paper “Predicting Additive Manufacturing Defects with Robust Feature Selection for Imbalanced Data” accepted for publication in IISE Transactions.