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2022 INFORMS Optimization Society Conference in Review

The University of Michigan Industrial and Operations Engineering (U-M IOE) department had a strong presence at this year’s conference, with over 10 presentations from U-M IOE faculty and students.

By:

Jessalyn Tamez

The 2022 INFORMS Optimization Society Conference took place in downtown Greenville, South Carolina from March 13-15, 2022. The theme of the conference was Bridging Data and Decision Making.

“It was great to be at an in-person conference after more than two years,” said U-M IOE Assistant Professor, Albert S. Berahas. “The organizers did a superb job at putting together a high-quality technical program, and all COVID-19 protocols were followed throughout the conference.”

The University of Michigan Industrial and Operations Engineering (U-M IOE) department had a strong presence at this year’s conference, with over 10 presentations from U-M IOE faculty and students.

“It was fantastic to see such a strong IOE presence at the conference,” said Berahas. “Many Ph.D. students presented their research, some for the first time at an in-person conference, and also organized sessions.”

Presentations from U-M IOE faculty and students included:

  • Albert S. Berahas
    • SQP Methods for Nonlinear Equality Constrained Stochastic Optimization
    • Analysis of Line Search and Trust Region Methods with Noise
  • Zhongzhu Chen
    • On computing with some convex relaxations for the maximum-entropy sampling problem
  • Rohan Ghuge
    • Non-Adaptive Stochastic Score Classification and Explainable Halfspace Evaluation
  • Huiwen Jia
    • Online Learning and Pricing for Service Systems with Reusable Resources
  • Ruiwei Jiang
    • Projection Cuts for Two-Stage Stochastic Mixed-Integer Programs
  • Jianhao Ma
    • Implicit Regularization of Sub-gradient Method in Robust Matrix Recovery
  • Kati Moug
    • Shared-Mobility-Based Evacuation Planning under Demand Uncertainty
  • Haoming Shen
    • Locating Charging Stations for Battery Electric Buses: a Data-Driven Optimization Approach
    • Convex Chance-Constrained Programs with Wasserstein Ambiguity
  • Jiahao Shi
    • Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Stochastic Optimization using Predictive Variance Reduction
  • Luze Xu
    • Gaining or Losing Perspective for Convex Multivariate Functions
  • Xian Yu
    • On the Value of Multistage Stochastic Facility Location with Risk Aversion

U-M IOE session chairs included:

  • Albert Berahas
    • Nonlinear and Stochastic Optimization
  • Jiahao Shi
    • Constrained Stochastic Optimization
  • Huiwen Jia
    • Learning under Uncertainty
  • Ruiwei Jiang and Haoming Shen
    • Stochastic and Robust Optimization
  • Xian Yu
    • Two-Stage and Multi-Stage Stochastic Programming

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