Seminar Abstract:
The rapid growth of information and accessibility to big data provide a unique opportunity to shift toward data-driven decision-making. These real-time paradigms (i) adaptively learn a model that predicts a user-specific outcome for each available decision (prediction) and (ii) harness this model to make data-driven decisions for subsequent users (prescription).

Although there have been tremendous advances in data-driven decision-making, such advances often cannot be applied to operations management problems because of their complexity. This brings forward several challenges and opportunities. In this talk, I discuss two challenges in healthcare and service operations: the need for joint learning and decision-making under limited resources and delayed feedback. I then introduce a data-driven predictive and prescriptive framework with provable performance guarantee to solve a hospital's care unit assignment problem. The effectiveness of this framework is illustrated using hospital system data.

I will end the talk by discussing my broader research agenda on dealing with other practical and societal challenges that arise in developing data-driven decision-making frameworks.


Presenter Bio:
Mohammad Zhalechian is a postdoctoral fellow at the Harvard Kennedy School. His research focuses on data-driven analytics to solve a wide range of problems in healthcare and service operations. He has collaborated closely with hospitals, clinics, and government agencies. Mohammad earned his Ph.D. in Operations Research in Aug 2022 at the University of Michigan, where he was advised by Prof. Mark Van Oyen. He is the recipient of awards, including second place in the 2020 INFORMS Decision Analysis Society Best Paper Award, finalist in the 2020 INFORMS Seth Bonder Scholarship of Health Applications Society, and winner of the 2021 IOE Richard C. Wilson Best Student Paper Award. His research work has also received multiple recognitions in the best paper competitions from MSOM, POMS, and HAS communities.