May 31, 2024 - In The Media, Events, Latest News

JMSS are the champions at SIMC 2024

The Singapore International Maths Challenge for 2024 was recently held at the National University of Singapore Science School. 42 schools were invited to send a team of 3 students to the challenge that has been running biennially since 2008. Participating Schools were from Singapore, Korea, Thailand, Japan, Australia, Russia, Poland, Hungary, England, Malaysia, China, Vietnam, Philippines, USA, Nepal, Indonesia, Netherlands and India.

This challenge involves solving a mathematical modelling problem set by University academics who this year were from the National University of Singapore. The problem for this year was built around the topic of Single Particle Imaging and the 3D structure of proteins. This involved sorting, rotating, comparing and analysing image sets of pixelated protein patterns. Student teams had 30 hours to develop solutions. The team from John Monash Science School (JMSS) comprised two Year 12 students Douglas Shuttleworth and Janneke Delhey-Peters and one Year 11 student Joel Tan. This team had to create solutions to the problem set as well as present their solutions to a panel of judges as well respond to any questions about the methods used.

For the first time, a school from Australia won the SIMC with the JMSS team being declared the Champion of the Challenge.

The team in explaining their solution to the audience on accepting their award commented that the essence of their winning solution lay in their methodical approach to the difficult task, going the extra mile to find all possible alternative solutions to the problem beyond using Principle Component Analysis and the K-Means algorithm, as they tackled vectors in 625 dimensions!

They also mentioned that they explored other alternatives such as Factor Analysis and the Gaussian Mixture Model, slowly unravelling their suspicions of the type of noise i.e., whether it is normally distributed. Not completely pleased with their findings, they came up with their own Heuristic Reconstruction Algorithm to optimise the data and reduce noise even further – a true reflection of their humility, grace and tenacity in their desire to keep improving their solutions.


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