Neuro-Reasoning Lab

Mon, 01 Jan 0001 00:00:00 +0000

Hi, I’m Ruoyu, a PhD student supervised by Yang and Morri. My current research interests include vision–language models, parameter-efficient fine-tuning, domain-generalised semantic segmentation, and self-supervised learning. I also have extensive experience in nuclei segmentation and image enhancement.

In addition, I am interested in large language models and their reasoning capabilities.

Previously, I completed a Master of Information Technology (coursework) at UNSW, during which I undertook both COMP9991 and COMP9992 research projects supervised by Yang. These projects focused on noise-robust weakly supervised nuclei segmentation.

Some of my research topics are outlined below. If you are interested in any of them, I would be happy to discuss further. My publications and outputs can also be found on my GitHub and Google Scholar pages.

Mon, 01 Jan 0001 00:00:00 +0000

I am an ARC Future Fellow, Scientia Associate Professor and Associate Head of School (Research) in the School of Computer Science and Engineering, Faculty of Engineering, UNSW Sydney. I graduated with a BEng in Computer Engineering from Nanyang Technological University, Singapore, and obtained a PhD degree in Computer Science from the University of Sydney in 2013.

I received the highly competitive Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) in 2015, and was an ARC DECRA Fellow at the University of Sydney before joining UNSW as a Lecturer in 2018. I also received a Dean’s Research Award from the Faculty of Engineering, University of Sydney in 2017. In 2019, I was awarded the prestigious ARC Future Fellowship, which provides support for excellent mid-career researchers to undertake high quality research in areas of national and international benefit. In 2020, I was awarded a Scientia Fellowship from UNSW, which supports career development of outstanding researchers. In 2021, I received two grant awards from Google and the Faculty of Engineering Research Excellence Award. In 2022, I received a large NHMRC Ideas Grant on computational brain imaging led by my collaborator at Macquarie University. In 2023, I received a Women in AI award for AI in Innovation in the Asia-pacific region. I also received an ARC Linkage Project Grant on rip current detection in collaboration with Surf Life Saving Australia, and an Award for Inclusion Research from Google. In 2024, I am one of the chief investigators receiving a large ARC grant to establish the Industrial Transformation Research Hub for Human-Robot Teaming. In 2025, I received an ARC Discovery Project grant on contextualised commonsense reasoning with neuro-symbolic AI. I have been listed among the World’s Top 2% Scientists by Stanford University and Elsevier since 2022.

Mon, 01 Jan 0001 00:00:00 +0000

My Ph.D. dissertation (1996) investigated The Role of Abductive Reasoning within the Process of Belief Revision (click on this link to view the abstract and, if you wish, download a gzip’ed postscript version). It deals mainly with the belief revision framework developed by Alchourrón, Gärdenfors and Makinson (AGM) and uses this theory to investigate various abductive belief revision operators.

My main research interests lie in Artificial Intelligence:

Knowledge Representation and Reasoning
Belief Change
Cognitive Robotics
Reasoning About Actions
Computational Machine Ethics
Neurosymbolic Machine Learning
Trustworthy Artificial Intelligence
Reasoning and Truthfullness in Large Language Models
Robotic Vision

Currently I am the Deputy Dean (Education) of the Faculty of Engineering at UNSW.

Previously I served as the Head of the School of Computer Science and Engineering (CSE) from July 2010 to September 2019.

Other roles I currently hold include:

Fellow, Australian Academy of Technological Sciences & Engineering (ATSE)
Deputy Director of Creative Robotics Lab (CRL) and Co-Director of the Intelligent Environments Lab
Professorial Fellow at the iCinema Centre for Interactive Cinema Research
Director & Chair Lifetime Awards, Pearcey Foundation
Vice President, Computing Research and Education Association of Australasia (CORE)
Executive, Australian Council of Engineering Deans (ACED)
College of Experts, Australian Research Council (ARC)
Member, Artificial Intelligence (AI) & Ethics Committee, Australian Computer Society (ACS)

Previous roles I have held include:

President (mid-2016 – mid-2018) and the Immediate Past President (mid-2018 – mid-2020) of the Australian Council of Deans of ICT (ACDICT)
Chair of NICTA (now Data61; Australia’s national ICT centre of excellence) University Partner Committee
Chair, NSW Steering Committee for Digital Careers.

Mon, 01 Jan 0001 00:00:00 +0000

Hey, I’m Ari. I was a PhD student at UNSW from 2020 to 2024 supervised by Yang Song and Erik Meijering. My PhD was focused on investigating the use of deep learning to aid understanding of brain structure and function. I now work as a research scientist at Seeing Machines where I primarily focus on the use of deep learning for drivery safety purposes. More generally, I’m interested in AI, robotics, and virtual reality.

Mon, 01 Jan 0001 00:00:00 +0000

I am Cong Cong, currently a Postdoctoral Fellow at the Australian Institute of Health Innovation, Macquarie University, working with Associate Professor Sidong Liu. Before this, I completed my PhD at UNSW with the thesis “Computer Vision in Histopathology Image Analysis: Preprocessing and Classification”, supervised by Associate Professor Yang Song and Professor Maurice Pagnucco.

My research mainly focuses on developing deep learning methods for digital pathology and brain MRI, including stain normalisation, whole-slide image (WSI) classification, and privacy-preserving multimodal modelling for cancer diagnosis. Beyond these topics, I am also interested in general image-related computer vision problems such as long-tailed classification, parameter-efficient fine-tuning (PEFT), and dataset distillation. A full list of my research outputs can be found on my Google Scholar page. Please feel free to reach out if you are interested in potential collaborations.

Mon, 01 Jan 0001 00:00:00 +0000

I received my PhD from UNSW Sydney in 2024, specializing in computer vision and machine learning

Mon, 01 Jan 0001 00:00:00 +0000

Dr Priyanka Rana is a researcher in the Centre for Health Informatics (CHI), Australian Institute of Health Innovation (AIHI). She received her PhD in Computer Science from UNSW Sydney in March 2023.

She specializes in AI-based deep learning for biomedical image analysis at both the cellular or whole slide levels.

https://researchers.mq.edu.au/en/persons/priyanka-rana/

Mon, 01 Jan 0001 00:00:00 +0000

Hi, my name is Ray. I completed my PhD under the supervision of Morri and Yang in 2024. My main research topic during my studies was machine ethics, particularly top-down machine ethics using epistemic logic. I explored various interpretations of ethics proposed by different ethicists and philosophers, and implemented them using formal methods across a range of scenarios.

At the moment, I am focusing on developing verifiable architectures for autonomous robots. This involves capturing the necessary requirements and translating them into machine-understandable languages, applying various verification techniques such as model checking, satisfiability checking, and runtime monitoring, and developing design patterns to support reusability and integration.

Mon, 01 Jan 0001 00:00:00 +0000

I have received his PhD degree in Computer Science from the University of New South Wales in February 2025. My PhD study primarily focuses on vision-based human trajectory prediction and computer vision.

I am now an ARC Laureate Postdoctoral Research Fellow at iCinema Centre for Interactive Cinema Research, as well as its Associate Director Research (AI). My current interdisciplinary research encompasses the fields of wildfire spread prediction, wildfire simulation and deep learning.

Mon, 01 Jan 0001 00:00:00 +0000

Hi, I’m Kunzi, a PhD student working in deep learning for medical image analysis.
My research focuses on medical image segmentation, image registration, and microscopy image analysis, particularly in histopathology and electron microscopy. I am interested in developing robust and efficient models that can reduce annotation requirements, improve generalisation, and support high-resolution biomedical imaging.

My work explores weakly supervised learning, domain-aware regularisation, and transformer-based architectures to enhance segmentation and registration performance under challenging imaging conditions. I aim to design methods that are both computationally practical and clinically meaningful.

Previously, I completed research projects in automated microscopy image analysis, focusing on noise-robust nuclei segmentation and weakly supervised label utilisation.

Some of my research topics are listed below. If you are interested in any of these areas, I would be happy to discuss further.
My publications and outputs can be found on my Google Scholar and GitHub pages.