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Invited Talk 2: Country-Scale Bandit Implementation for Targeted COVID-19 Testing
Hamsa Bastani

Sat Dec 12 08:30 AM -- 08:50 AM (PST) @
in Consequential Decisions in Dynamic Environments »
Event URL: https://hamsabastani.github.io/ »

In collaboration with the Greek government, we use machine learning to manage the threat of COVID-19. With tens of thousands of international visitors every day, Greece cannot test each visitor to ensure that they are not a carrier of COVID-19. We developed a bandit policy that balances allocating scarce tests to (i) continuously monitor the dynamic infection risk of passengers from different locations (exploration), and (ii) preferentially target risky tourist profiles for testing (exploitation). Our solution is currently deployed across all ports of entry to Greece. I will describe a number of technical challenges, including severely imbalanced outcomes, batched/delayed feedback, high-dimensional arms, port-specific testing constraints, and transferring knowledge from (unreliable) public epidemiological data. Joint work with Kimon Drakopoulos, Vishal Gupta and Jon Vlachogiannis.

Author Information

Hamsa Bastani (Wharton)

My research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, revenue management, and social good. Recently, I've been working on the design and application of transfer learning algorithms, e.g., for predictive analytics with small data, dynamic pricing across related products, and speeding up clinical trials with surrogate outcomes. I am also interested in algorithmic accountability and using big data to combat social and environmental harm.

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