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Research projects in Information Technology

Displaying 1 - 10 of 102 projects.

Self-aware neural networks

This project is similar in flavour to the Conscious AI project but rather than come from a Philosophical/Neuroscience/Math/Theory angle, this project aims to build self-aware neural networks that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both and embed self-awareness modules within these systems.

Supervisor: Dr Levin Kuhlmann

Living AI

In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning that occurs within these biological neural networks, so that these networks can be leveraged for AI applications.

Supervisor: Dr Levin Kuhlmann

Explainable AI (XAI) as Model Reconciliation

Creating efficient and beneficial user agent interaction is a challenging problem. Challenges include improving performance and trust and reducing over and under reliance. We investigate the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans.

Supervisor: Dr Mor Vered

Explainable AI (XAI) for Disobedient Robotics

As humans we have the ability, and even the necessity of distinguishing between orders that are ethical, safe and necessary and orders that may be harmful or unethical. Theoretically this ability should exist in robots. To coin Assimov’s second law “A robot must obey orders given by human beings except where such orders would conflict with the First Law '', the first law being not injuring or allowing any human to be injured.

Supervisor: Dr Mor Vered

Enhancing Service User Care Pathway Experience through AI-Driven Personalisation

This scholarship is open to Australian and New Zealand Citizens and Permanent Residents

The project is a partnership with headspace National Youth Mental Health Foundation

We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and resource recommendations, ultimately optimising the overall user journey. This project will improve the care pathway experience for young people and families accessing mental health services through the headspace website.

Supervisor: Teresa Wang

Conscious AI

What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest neuroscience based theories about how consciousness emerges in the brain, as well as the latest AI methods and examine what if any consciousness current AI methods might have and how we might define whether an AI is conscious based on what we know about consciousness in the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level.

Supervisor: Dr Levin Kuhlmann

Generative AI for Recommender Systems

A recommender system is a subclass of information filtering/retrieval system that provides suggestions for items that are most pertinent to a particular user without an explicit query. Recommender systems have become particularly useful in this information overload era and have played an essential role in many industries including Medical/Health, E-Commerce, Retail, Media, Banking, Telecom and Utilities (e.g., Amazon, Netflix, Spotify, Linkedin etc).

Supervisor: Teresa Wang

Causal Reasoning for Mental Health Support

This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from the data in literature. As the number of causal relationships is usually much smaller than that of associations, the proposed techniques will achieve explainability by making causes and effects interpretable to psychologists.

Supervisor: Dr Lizhen Qu

Bayesian-network models for human-machine collaboration to protect pollinator-plant interactions in agriculture and natural ecosystems

Ecological systems are dynamic and complex. Many ecosystems support human food production and in turn are impacted by human food production activity. This creates feedback loops between ecosystems, human society and our agriculture, that are typical of complex systems. Ecosystem and social system modelling therefore, including simulation, can play a key role to understand food production and ecosystem interactions.

Formal Explainability in Artificial Intelligence

Artificial Intelligence (AI) models are widely used in decision making procedures in many real-world applications across important areas such as finance, healthcare, education, and safety critical systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find an ever growing range of practical applications, many of which will have to do with various aspects of humans' lives including privacy and safety.

Supervisor: Alexey Ignatiev