Her research aims to advance the state-of-the-art contextual stochastic optimization methodologies, providing tools for making smarter decisions when the future is uncertain.

We study a stochastic multi-resource planning and allocation problem (SRAP) with uncertain demand. Given sets of demand nodes and multiple resource ty…

New data-driven models developed by a USC and Carnegie Mellon-led team could reduce surgery delays, cut hospital costs by up to 60% and improve access to surgery.

New data-driven models developed by a USC and Carnegie Mellon-led team could reduce surgery delays, cut hospital costs by up to 60% and improve access to surgery.

We propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures suitable for optimization contexts. First, we propose a new class of...

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