Designing Optimal Voting Rules

A central task in voting is to aggregate the ranked preferences of voters over a set of alternatives (candidates) to select a winning alternative. However, despite centuries of research, the natural question of which voting rule is “best” has remained elusive. A recent approach from computer science offers hope. By proposing a natural quantitative measure of the “efficiency” of a voting rule, called distortion, it allows us to define and seek the most efficient voting rule.

In a series of joint works, we...

Can Machine Learning and Mobile Phone Data Improve the Targeting of Humanitarian Assistance?

Will algorithms save our planet and will we regret it when they do?

We live in a time of unprecedented global environmental and ecological change: a warming planet, vanishing biodiversity, overfishing and intensifying ecosystem change from fire to draught to invasive species. Not only are these challenges frequently intertwined, all are closely coupled to social, economic, political components which play out in diverse and unequal ways. At the same time, we suddenly have access to ecological and environmental data at a scale we never imagined, thanks to...

Efficient and targeted COVID-19 border testing via reinforcement learning

Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates....