Oct 24: The group has five talks at the #INFORMS2024 in Seattle. Stop by if you are at the conference:

1. SC31 (Methods for Large-Scale Nonlinear and Stochastic Optimization I), Summit – 442: High Efficiency in Stochastic Trust Regions
2. ME44 (Derivative-free Simulation Optimization), Summit – 436: Efficiency Analysis of Simulation Optimization with Dynamic Stratification
3. ME44 (Derivative-free Simulation Optimization), Summit – 436: Calibrating Digital Twin via Bayesian Optimization: A root-finding strategy
4. ME44 (Derivative-free Simulation Optimization), Summit – 436: Comparative Analysis of Distance Metrics in Distributionally Robust Optimization for Queuing Systems: Wasserstein vs. Kingman
5. TE01 (Explainable AI for prognostics), Summit – 320: Strata design for variance reduction in stochastic simulation

Oct 24: Two papers accepted for publication:

• “Strata Design for Variance Reduction in Stochastic Simulation” by Park, Ko, Shashaani, and Byon was accepted in INFORMS Technometrics. (available online)
• “Uncertainty Quantification using Simulation Output: Batching as an Inferential Device” by Jeon, Chu, Pasupathy and Shashaani was accepted in Journal of Simulation.

Sep 24: Paper by Ha, Shashaani, and Menickelly, “Two-stage Sampling and Variance Modeling for Variational Quantum Algorithms“, accepted for publication in INFORMS Journal on Computing.

Aug 24: Shashaani’s “Simulation Optimization: Introductory Tutorial on Methodology” to appear on 2024 Proceedings of Winter Simulation Conference. The work differs from many other simulation optimization tutorials in its focus.

Jun 2024: Four papers, on nested partitioning, digital twins, data farming for optimization, and new metrics for distributionally robust optimization, were accepted for publication in 2024 Proceedings of Winter Simulation Conference.

Apr 24: The group had a good night at the 2024 ISE CA Anderson Awards.

Ha received the ISE Distinguished Dissertation Award, which recognizes recent PhD graduates who have written original, innovative dissertations that reflect outstanding technical contributions and are likely to have significant scientific and societal impact! He will be the department’s nominee for the IISE Pritsker Dissertation Award and/or INFORMS Dantzig Dissertation Award. (Jeon-Ha’s friend, teammate and basketball buddy) received the award on Ha’s behalf as he was traveling. Congratulations Yunsoo!!

Shashaani received the ISE Faculty Scholar Award, which recognizes current ISE (tenure track) assistant and associate professors for excellence in research, teaching, and service over the past three calendar years and the potential for becoming a University Faculty Scholar.

Apr 24: Houser presents his research on robust and fast feature selection with screening and partitioning methods at the 2024 NC State Graduate Student Research Symposium. His poster will be added to the groups website!

Mar 24: Ha presented First Order Trust Region Methods with Adaptive Sampling at the 2nd INFORMS Optimization Society’s Conference in Houston, TX.

Mar 24: Paper “Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization” accepted for publication in IISE Transactions.

Jan 24: New paper “Robust Variational Quantum Algorithms Incorporating Hamiltonian Uncertainty” submitted.

Jan 24: New paper “Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling” submitted.

Nov 2023: New paper “Statistical Inference on Simulation Output: Batching as an Inferential Device” with Y. Jeon and R. Pasupathy submitted.

Oct 2023: Shashaani’s group have 5 presentations at the 2023 INFORMS annual meeting:

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 “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.