Announcing the 2019 PhD Fellowships to support leading computer science research in Australia

The Google PhD Fellowship program supports PhD students in computer science and related fields, building strong relationships with the global academic community. In the most recent round, four PhD students in Australia have been recognised for their outstanding efforts.

Hua Hua, Google Phd Fellowship in Human-Computer Interaction (ANU)

Research Proposal: Learning and Reasoning about Human Place Knowledge 

Hua's research aims to make human place knowledge understandable and usable by computers. As a member of the Artificial Intelligence Research Group at ANU, he focuses on solving this problem by combining qualitative representation and reasoning with machine learning (ML) techniques.

Xuanyi Dong, Google PhD Fellowship in Machine Perception (UTS) 

Research Proposal: Automated ML with Imperfect Data for Machine Perception

Xuanyi focuses on solving real-world computer vision problems with deep learning techniques. He has been fascinated by automated ML model design and optimization, making machines better understand images and videos and making AI accessible to everyone.

Zhanna Sarsenbayeva, Google PhD Fellowship in Human-Computer Interaction (University of Melbourne) 

Research Proposal: Quantifying the Effects of Situational Impairments on Mobile Interaction

Zhanna's interest about technology and its impact on people let her pursue a career in Human-Computer Interaction. Her research is focused on a systematic understanding of the effects of different situational impairments on mobile interaction when designing accessible technology as it can benefit users of all abilities.

Zhi Tian, Google PhD Fellowship in Machine Perception (University of Adelaide)

Research Proposal: Fully Convolutional One-Stage Instance-level Recognition

Zhi Tian was awarded the Google PhD Fellowship for his work in high-performance dense per-pixel prediction for scene understanding with deep neural networks. His research looks at developing simple yet accurate deep learning techniques for fundamental computer vision tasks such as semantic pixel labelling, object detection, instance-level segmentation in images.

In supporting these Fellows we recognise their significant academic achievements and contribution. We look forward to building even stronger links to support important research in Australia.