Spyros Zoumpoulis is an assistant professor of Decision Sciences at INSEAD. His research is on using analytics to optimize decision making, with applications in marketing, healthcare, and revenue management. His current focus is on investigating how to design, and use data from, experiments in order to make optimal personalized decisions, as well as how to evaluate policies that make personalized decisions, such as targeting decisions in marketing and personalized treatment decisions in healthcare.
More generally, he is broadly interested in problems at the interface of learning with data and decision making. His research has appeared in leading management science academic journals such as Management Science and Operations Research.
Spyros has worked with companies including Microsoft, LinkedIn, IBM, Oracle, and Accenture and serves on the advisory board of start-ups in the areas of his expertise. At INSEAD, he teaches the MBA core course on uncertainty, data and judgment, the MBA electives on data science for business and decision models, the MBA business foundations course on quantitative methods, the PhD courses on probability and statistics, and the INSEAD-Sorbonne business foundations course on uncertainty, data and judgment. He has won the Dean’s Commendation for Excellence in MBA Teaching award numerous times and has been nominated for the best MBA elective professor award.
Spyros received the B.S., M.Eng., and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.
| Name of Speaker | Dr Spyros Zoumpoulis |
| Schedule | 18 February 2022, 10am – 11.30am
(60 min talk + 30 min Q&A) |
| Link to Register | https://nus-sg.zoom.us/meeting/register/tZIocu2rpzwtGNDoe-8hOR8eA_-nL7H_Fp6r |
| Title | Quantifying the Benefits of Targeting for Pandemic Response |
| Abstract | Problem definition: To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Since targeting is potentially contentious, rigorously quantifying its benefits is critical for designing effective and equitable pandemic control policies.
Methodology/results: We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation focused on the Île-de-France region of France, based on commonly available public data. We find that targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses, as well as the time in confinement for each age group. Optimized dual-targeted policies are interpretable: by fitting decision trees to our raw policy’s decisions across many problem instances, we find that a feature corresponding to the ratio of marginal economic value prorated by social contacts is highly salient in explaining the confinements that any group – activity pair experiences. We also quantify the impact of fairness requirements that explicitly limit the differential treatment of distinct groups, and find that satisfactory trade-offs are achievable through limited targeting. Implications: Given that some amount of targeting of activities and age groups is already in place in real-world pandemic responses, our framework highlights the significant benefits in explicitly and transparently modelling targeting and identifying the interventions that rigorously optimize overall societal welfare. |
