Visionary: Assistant Professor Jonathan Scarlett
Group testing is a widely used technique for fast testing in medical applications (e.g. blood tests or nasal PCR tests). It has been a popular and powerful strategy during the height of the COVID-19 pandemic when testing resources worldwide were scarce and costly.
The group testing problem consists of determining a small set of defective items (e.g. abstractly representing infected individuals in medical testing) from a larger set of items based on tests on groups of items. It can be thought of as a combinatorial search problem with a flavour of sparse inference.
Asst Prof Jonathan Scarlett from NUS Computer Science and NUS Mathematics has spent years in better understanding the mathematical algorithms and theory behind group testing. His work provided new precise characterisations of the performance bounds for algorithms and impossibility results, which are the fundamental mathematical limits of the problem.
He has also broadened the scope of the problem by making fundamental contributions on issues such as partial recovery (e.g. tolerating a small number of false positives or false negatives), proving phase transition behaviour on how many tests are required, proving achievability and impossibility results on the performance of group testing algorithms under noise, and several other variations on the problem.
Asst Prof Scarlett also adapted his mathematical studies of group testing to other seemingly distinct signal acquisition problems, which are relevant in applications such as MRI.
“It’s an honour to be selected for this award, and I am deeply grateful to all of my collaborators and students that worked together with me on group testing and related topics; their efforts have truly been invaluable in pushing this research forward,” said Asst Prof Scarlett.