Initiated by then Head of School Professor Celine Boehm, the Grand Challenges intends to showcase opportunities for physics to drive research and breakthroughs that could transform the world.
The event requires researchers to present a five-minute pitch to an audience of staff, students and a panel of judges comprised of Physics Foundation members and distinguished industry colleagues.
The successful projects are:
Led by Associate Professor Stefano Palomba – Winner: $250,000
There are more than one billion people with disability in the world, which corresponds to approximately one in seven.
Associate Professor Stefano Palomba’s team aims to create a bi-directional nerve interface that directly connects nerves of the peripheral nervous system (PNS) on one side with any corresponding bionic device on the other, enabling people with PNS-related disabilities (e.g. in need of a prosthetic limb or a bionic eye, etc.) to regain not only control over the affected organ but also to regain sensation. Furthermore, this technology can potentially mitigate other neural-related diseases like chronic pain and cerebral palsy, and also underpin fundamental research in neuromorphic intelligence and neurodegenerative disorders.
The intention is to create a Universal Neurophotonic Interface (UNI), where light is used to bi-directionally communicate with individual neurons. The technology will see the development of two photonic chips; one that generates an action potential that will constitute the sensory feedback delivered directly to the nerves and a second that collects the action potential from nerves emitting signals in reverse to generate a command signal.
The team’s goal is to create a proof of concept by developing and testing the two photonics chips capable of bi-directionally interfacing with individual neurons.
Led by Professor Rongken Zheng – Winner: $250,000
This project aims to revolutionise a real-world technology and thus is expected to be very attractive to undergraduate and research students, a key goal for the Grand Challenges. They will be able to develop new X-ray detectors and imagers with higher sensitivity and resolution at a lower dose, lower cost, and higher confidence.
X-ray imaging has been extensively used in medical diagnosis and non-destructive inspection. Low-dose and high-sensitivity X-ray imaging is in high demand to reduce health concerns and for wider applications. Metal halide perovskites, emerging materials widely researched for solar cells, hold great promise for X-ray detection, thanks to their strong X-ray absorption, long carrier diffusion length, large mobility-lifetime product, and easy synthesis.
Professor Rongken Zheng's team will explore the direct integration of metal halide perovskites with pixelated sensing arrays and readout integrated circuits for next-generation X-ray imaging, drawing upon the expertise of the multidisciplinary team in physics, materials, electronics, and medical imaging.
Led by Dr Sahand Mahmoodian –$50,000 Seed Funding
The challenge around differentiating between normal brain waves when a person’s eyes are closed vs a seizure is called time-series classification, and tackling it is central to problems across science and technology. These types of problems are everywhere, including in the diagnosing of heart disease, detecting process faults on assembly lines, deciding when to intervene during child labour, detecting human falls from smartwatch data and identifying impurities in single-origin coffee beans.
Dr Sahand Mahmoodian’s team will adapt tensor network methods for studying entangled quantum systems (Tensors are the building blocks of a machine learning model – Codecraft.tv). Their methods will deliver impactful advances to time-series classification problems studied across the School and University, from astronomy (classifying large star surveys) to geosciences (identifying ore deposits from geophysical time-series data from Earth evolution models).
This project combines two important and exciting topics in modern science: quantum physics and machine learning, for the analysis of time-dependent signals. In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order.
Time-dependent signals are ubiquitous and are central to solving problems in industries as diverse as biomedicine, mining, social media, and finance. The project brings together world-leading expertise in quantum many-body physics, time-series machine learning, and neural networks to develop cutting-edge time-series classification methods that leverage powerful techniques from quantum mechanics for the first time.
Read about our previous winners here.