News

Our latest study on algorithmic bias in the education system is now featured on WBEZ NPR. Listen to the segment here and read the full paper here.

Explore more coverage on Diverse Education here, Optimal Partners here, and AERA here.

Our latest study on algorithmic bias in the education system is highlighted on ACM Tech News here.

Our research group attended the INFORMS 2023 Annual Meeting in Phoenix, AZ.

We held three talk sessions in addition to poster sessions to present our recent research papers:

• • Nezami, N. and Anahideh, H., 2023. Dynamic Exploration-Exploitation Pareto Approach for High-Dimensional Expensive Black-Box Optimization. Available at SSRN 4382756.

  • Almasi, M., Anahideh, H. and Rosenberger, J.M., Exploring Nonlinear Kernels for Lipschitz Constant Estimation in Lower Bound Construction for Global Optimization.

  • Di Carlo, F., Nezami, N., Anahideh, H. and Asudeh, A., 2023. FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness. arXiv preprint arXiv:2304.04679.

  • Anahideh, H., Nezami, N., Haghighat P. and Gandara D., 2023. Unpacking the Impact of Imputation on Fairness.

We will present our recent paper "Kernelized Lipschitz Constant Estimator: A Non-Linear Lower Bound Construction Approach for Efficient Global Optimization" at INFORMS 2023 annual meeting.

We will present our recent paper "Dynamic Exploration-exploitation Pareto Approach For High-dimensional Expensive Black-box Optimization" at INFORMS 2023 annual meeting.

We will attend the 2023 INFORMS annual meeting in Phoenix, Arizona. 

We had two poster sessions for our papers at MMLS 2023 in Chicago, Illinois.

Dr. Anahideh and her collaborators hosted a workshop on Algorithmic fairness in machine learning at SDM 2023.

Francesco Di Carlo and Simone Lazier presented their recent papers at the AFair-AMLD workshop on Algorithmic fairness in machine learning, which was held at SDM 2023 in Minneapolis.