Since 2009, Google has been supporting top graduate students who want to make an impact on the future of technology. The Google PhD Fellowship program recognises candidates doing important and innovative research in computer science and related fields. In Australia and New Zealand, the program focuses on early-stage candidates. Winners receive fellowships which include a monetary award of $15,000 AUD to cover stipend and other research related activities, as well as a Google Mentor who works on topics related to their field of study and provides guidance. In 2021, we’re pleased to announce four new PhD students in Australia who have been awarded fellowships for their outstanding efforts.
Sampson Wong, Google PhD Fellowship in Algorithms, Optimisations and Markets, The University of Sydney
"Transport networks require regular monitoring and maintenance to sustain a high level of operability. As networks grow and as technologies improve, there is a rising demand for data-driven analysis of transport network data. This has resulted in governments and companies developing domain-specific tools to provide its citizens and users with the best recommendations. The speed and quality of these tools depend greatly on their fundamental building blocks. The goal of my thesis is to develop efficient algorithms for fundamental problems involving geometric movement data on transport networks. We use clustering and other algorithmic methods to detect commuting patterns in geometric movement data, and to select beneficial upgrades for a transport network."
Theekshana Dissanayake, Google PhD Fellowship in Machine Learning, QUT
"Deep learning has shown great success in solving biosignal-based medical diagnostic problems. However, present solutions cannot generalise across multiple datasets captured from different experimental settings. Furthermore, the black-box nature of current solutions hinders the trust associated with the predictions made from a clinical perspective. This PhD research focuses on the generalisability and interpretability of deep learning models designed for biosignal-based medical diagnostics and considers both single and multi-channel biosignals (such as heart signals and brain signals using EEG and ECG)."
Xinlong Wang, Google PhD Fellowship in Machine Perception, Speech Technology and Computer Vision, The University of Adelaide
Xinlong's research interests lie in computer vision and machine learning, specifically in enabling machines to see and understand the environment. Xinlong’s research focuses on object-level recognition, including 2D/3D/video object detection and instance segmentation.
Yun Li, Google PhD Fellowship in Machine Learning, University of New South Wales
"Deep learning has been demonstrating the potential to significantly revolutionise the practice of medicine and the delivery of healthcare. However, low volume, high sparsity, and poor quality of healthcare data and their diverse contexts may limit the efficacy of deep learning methods. In my research, we aim to develop a suite of robust and versatile few-shot machine learning methods to effectively discover personalised, transferable insightful knowledge with very limited data. Specifically, we have identified and proposed the solutions to 1) data-efficient methods for genomics sequencing; 2) medical image argumentation, 3) hierarchical multi-view data analysis; and 4) tinnitus diagnosis. We will continue to improve the explainability, transparency, and personalisation for better clinical translation. Our studies will have a broader impact on a wide range of practical scenarios such as genome study, medical diagnosis, drug discovery, and disease treatment."
In supporting these Australian Fellows we recognise their significant academic achievements and hope that they will go on to be leaders in their respective fields. We look forward to building even stronger links between industry and academia to help support important research in Australia. You can find out more about the Google PhD Fellowship program here
