May 24: Shashaani is awarded a grant for a three-year project on “Fast and Scalable Stochastic Derivative-free Optimization” from Office of Naval Research. Shashaani will lead this effort seeking to excel stochastic optimization in theory and practice for black-box noisy problems of large scales.

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.

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 Optimizationaccepted for publication in IISE Transactions.

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.