Research projects in Information Technology

There is an opportunity to prototype digital water play systems and examine users’ aquatic body-environment interactions to derive an understanding of digital technology’s opportunities to facilitate novel bodily water play interactions in-water, on-water and underwater. 

With drones getting smaller and smaller, we regard them as physical pixels that can be placed anywhere in space, allowing us to experience digital content in the physical world in novel playful ways.

There is an opportunity to enrich indoor rock-climbing or bouldering through interactive technology. This project builds on prior work and combines bouldering with Hololens and motion capture.

This practice-based research involves further development of the AirSticks, a hardware/software package which allows the triggering and manipulation of sound and visuals in a 3D playing space, as a gestural instrument for live electronic music performance, music education and general health and wellbeing in collaboration with our interdisciplinary team at SensiLab. This can be done through new performances, new software or new hardware. How can we reinvent the connect between our bodies, our ears and our creativity, and what new applications for the AirSticks can be discovered?

This project will seek to further the research into and development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep neural networks for image, video, audio, social network, and/or text classification.

This project aims to identify novel methods for inferring where and when photographs and videos were recorded from features of the material itself. A key requirement of image processing in a Law Enforcement (LE) context is to augment classification of material by identifying its spatio-temporal context.

Advanced strongly typed languages like Haskell and emerging type systems like refinement types (as implemented in Liquid Haskell) offer strong guarantees about the correctness of programs.  However, when type errors occur it can be difficult for programmers to understand their cause.  Such errors are particularly confusing for people learning the language.  The situation is not helped by the cryptic error messages often produced by compilers.

We live and work in a world of complex relationships between data, systems, knowledge, people, documents, biology, software, society, politics, commerce and so on.  We can model these relationships as networks or graphs in the hope of reasoning about them - but the tools that we have for understanding such network structured data (whether algorithmic analytics or visualisation tools) remain crude.  Emerging display and interaction devices such as augmented and virtual reality headsets offer new ways to visualise and interact with data in the world around us rather than on screens.  This…

Testing self-driving cars is extremely difficult, as one has to account for a very large space of possible scenarios. In this project, we will explore the application of automated testing techniques, mainly in the area of search-based software testing to verify that the AI components of self-driving cars work as they should. This project is in collaboration with Professor Hai Vu and the Monash Connected Autonomous Vehicle (MCAV) team.

Defect prediction has been developed for more than four decades. Yet, a multitude of human aspects (i.e., both developers and end-users) have been rarely considered and incorporated. Thus, this project aims to focus on inventing theories and approaches for human-centric defect prediction to efficiently predict and explain non-functional requirement defects (e.g., accessibility issues and usability issues in Mobile Apps) that have the largest impact on end-users and humanity.