Traditional active learning helps reduce labeling costs by selecting the most useful examples from a large pool of unlabeled data. However, in many real-world cases, such a large pool doesn't exist or is expensive to collect. This project explores a new approach using large language models to create synthetic unlabeled text data instead. Rather than just picking data to label, the model will also generate new examples that are diverse and potentially helpful for learning.
Honours and Minor Thesis projects
Displaying 1 - 10 of 219 honours projects.
Primary supervisor: Chetan Arora
This project focuses specifically on LLM applications: chatbots used in customer support (e.g., healthcare). The goal is to investigate how user requirements (e.g., “the bot should de-escalate frustrated users”) can be systematically translated into prompt templates or prompt strategies.
Primary supervisor: Hui Cui
This project aims to develop privacy-preserving deepfake detection techniques that enable accurate and secure identification of synthetic audio and video content without exposing sensitive user data. Traditional detection methods often require access to raw audio or visual inputs, raising significant privacy concerns, especially in scenarios involving personal or biometric data.
Primary supervisor: Monica Whitty
Mis/disinformation (also known as fake news), in the era of digital communication, poses a significant challenge to society, affecting public opinion, decision-making processes, and even democratic systems. We still know little about the features of this communication, the manipulation techniques employed, and the types of people who are more susceptible to believing this information.
This project extends upon Prof Whitty's work in this field to address one of the issues above.
Primary supervisor: Hui Cui
This project focuses on enhancing security and privacy protection in blockchain-based systems for verifying education credentials, with the goal of combating the proliferation of fake certificates. By leveraging the immutable and decentralized nature of blockchain, the project aims to develop a secure credential verification framework that ensures the authenticity and integrity of academic records while safeguarding users' personal information.
Primary supervisor: Levin Kuhlmann
Background and motivation
As intelligent agents make decisions, any project aiming to realize human-like AGI should model decision-making. As we have been pursuing the WBA approach to create AGI by learning from the architecture of the entire brain, we request you to model the decision-making of the mammalian brain.
Primary supervisor: Mor Vered
Markov Decision Processes (MDPs) are frameworks used to model decision-making in situations where outcomes are partly random and partly under the control of a decision maker. While small MDPs are inherently interpretable for people, MDPs with thousands of states are difficult to understand by humans. The K-MDP problem is the problem of finding the best MDP with, at most, K states by leveraging state abstraction approaches to aggregate states into sub-groups. The aim of this project is to measure and improve the interpretability of K-MDP approaches using state-of-the-art XAI approaches.
Primary supervisor: Sarah Goodwin
This is a Winter Student Research Internship ONLY not an honours or minor thesis project at this time.
Please apply here if you are interested in the role before the deadline:
https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter
__________
Primary supervisor: Mor Vered
Goal recognition is defined as the problem of determining an agent’s intent from observations of its behaviour. Current research in goal recognition has focused on observing agents that are trying to achieve their goals in a rational manner. Other research has focused on observing agents that are deliberately trying to trick an observer into believing they are pursuing alternative goals to the ones they are actually pursuing. However there is also a need to recognise when a behaviour is suspicious, regardless of the goal that is being tried to be achieved.
Primary supervisor: Rajeev Gore
Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.