Please join us for our next KRYPTON seminar of the Fall 2021 semester. We will meet at the usual time, 1600h, on Friday October 29, 2021. 

We will continue to have a virtual option, but will also meet in Room 2214 ENGR for those who want to attend in person. I will be traveling and will try to call in from the airport before boarding my plane. Dr. Hadi El-Amine will attend in person in Room 2241 ENGR.

You can check Krypton events through our calendar at:
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Krypton Seminar Series - Fall 2021

Date: October 29, 2021
Time: 4:00PM - 5:30PM

Venue: Zoom and Room 2241 ENGR

Link for remote participation:
Marwan Shams Eddin
PhD student, Systems Engineering and Operations Research, GMU

Title — Large-scale Infectious Disease Screening in Dynamic and Variable Environments

Abstract — 
In this work we focus on large-scale infectious disease screening in dynamic and highly variable environments. Large-scale screening is crucial to any well-functioning healthcare system as it has direct impact on human life as was demonstrated in the ongoing COVID -19 pandemic. Problems that arise in this context are often sequential as they involve taking screening actions, observing outcomes (which are often affected by uncertainty), and then adjusting decision accordingly. Given the nature of the problems involved, decision that are more conservative (as opposed to ones that rely on average-case performance) are desirable. Therefore, we rely on tools such as robust dynamic programming and optimal control to solve the resulting sequential decision-making problems. We focus on two aspects of the problem. The first deals with large-scale screening of established infections in which disease evolution can be predicted with a fairly good level of accuracy. In this setting, screening policies do not have a substantial impact on disease prevalence in the short-to-medium term and thus interactions between screening and populations characteristics can be relaxed. The second aspect focuses on large-scale screening for emerging pandemics. In this setting, modeling the interaction between screening decisions and disease dynamics on the short-term is crucial and thus must be explicitly incorporated in the decision-making framework. This unfortunately complicates the problem from a modeling perspective. For both problem aspects, we propose efficient algorithms and present case studies in the context of large-scale screening of donated blood in blood centers and of COVID-19 in the U.S. 

Bio —Marwan Shams Eddin is a Ph.D. student in the Systems Engineering and Operations Research department at George Mason University. Marwan received his B.E. in civil engineering from the Lebanese University and a Master's in operations research and engineering management at the American University of Beirut. His academic research generally focuses on optimization under uncertainty and particularly on sequential decision-making using stochastic and robust dynamic programming. Marwan’s current work focuses on large-scale population screening in dynamic settings and under operational constraints. This work covers determining optimal screening polices during emerging pandemics. Methods used in this work include dynamic programming, robust optimization, reinforcement learning, among others.  
Zoom Link
Meeting ID: 923 5693 8266
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