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Quantifying COVID-19 mitigation strategies effectiveness
Georgia Perakis (email@example.com), and Leann Thayaparan Ph.D. student (firstname.lastname@example.org)
In the wake of the COVID-19 pandemic we are working on combining data, machine learning and optimization to understand the epidemiological and economic impacts of different mitigation strategies to help governments, states in the US and beyond to understand when to institute and remove different interventions. This is a very topical problem that has interesting implications in both modeling techniques and social impact! The project will combine: Predicting the spread of the COVID-19 using epidemiology and machine learning models to understand the importance of mitigation strategies on spread Designing synthetic tests to see how various areas would have reacted if they had instituted different mitigation strategies Finally leveraging results to create prescriptive recommendations for when governments should remove various mitigation strategies and if there is a second wave, when to reinstitute them Responsibilities: (1) You will be a full member of the research team, and the team will meet once or twice weekly. (2) In the meetings we work on data aggregation, modeling, algorithms, discussing results, and setting up next steps. (3) The primary responsibilities will be coding algorithms (with our guidance) and analyzing data. Please send applications to Professor Georgia Perakis (email@example.com), and Leann Thayaparan Ph.D. student (firstname.lastname@example.org). Please email resume + a 2-sentence statement of why you are interested and a good fit for this project.
(1) Experience with R, Python or Matlab is required (2) Availability to work over the summer