Many modern technologies rely on finding the best possible option from a potentially huge number of permissible choices. Everyday examples of this include navigation apps that find the fastest route to a given destination, and streaming services that recommend movies based on what users are mostly likely to enjoy. Problems of this kind are too complex to be solved by a human, so we must rely on computers to automate the process. The challenge then becomes: how do we design methods that allow computers to do this efficiently and reliably?
A/Prof Tam works in the field of “optimisation”, the branch of mathematics concerned with problems whose goal is to find the “best” possible solution. His research combines the design of new computational methods with rigorous mathematical analysis to establish when algorithms work, how quickly they find a solution, and how they can be improved. He has developed tools in a range of applications including smart management of virtual power plants to make renewable energy more usable, reconstruction of high resolution images in x-ray ptychography to allow advances in material science, and reducing energy consumption in the “training-phase” of machine learning models to improve their sustainability.