Honours and Masters project

AI is starting to be used for reviewing in international AI conferences.  For instance, http://paperreview.ai as announced by Andrew Ng as an “Agentic Reviewer” trained on ICLR 2025 reviews, and Google is field testing an AI-based Paper Assistant Tool for authors submtting to NeurIPS 2026.  Significant research is going into developing these tools, and some good test data exists collected from previous conferences.  In this project we will work with the VinUniversity AI group who is developing a benchmarking framework for evaluating AI reviewing.  Research papers usually contain important…

It is said that ChatGPT is ‘not trying to be right, it’s just trying to be plausible.’  While the LLM community talk about hallucination, it is a complex phenomenon and a product of their construction.  The training and theory of LLMs has no notion of truth, they generate text with no critical evaluation of content or sources.  Many text sources are opinions, some may have subtle or not so subtle propaganda, some reflecting misinformed views, and LLMs reproduce this mess.  Moreover, the training and theory of LLMs has no notion of epistemic uncertainty, they have no sense of uncertainty. …

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

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This is a Winter Student Research Internship. Please apply here: https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter

Advances in artificial intelligence (AI) are reshaping work across computing-related professions. Although AI systems can perform some technical and cognitive tasks, they continue to rely on human judgement, responsibility, and contextual understanding. Identifying where human contribution remains critical is therefore a substantive question for disciplines concerned with socio-technical systems.

Pharmacists handle thousands of medications daily, requiring constant verification of prescriptions, allergy checks, and inventory management across multiple digital and physical systems. This fragmented workflow leads to frequent context switching, high cognitive load, and increased risk of workflow inefficiencies and medication errors. 

This project addresses the language ignoring problem in embodied AI, where robots learn visual shortcuts instead of following instructions. Building on our preprint establishing the relationship between planning width (instruction granularity) and learning difficulty, you will develop WALR—a reward design framework that adapts to instruction complexity. WALR scales language grounding rewards based on instruction granularity (coarse vs.

This project develops PACE-Drone, an intelligent drone planning system that learns from experience rather than following pre-programmed scripts. Unlike current drones that treat each mission independently, PACE-Drone maintains a persistent belief over user preferences via Bayesian learning, actively discovers implicit constraints from historical mission logs, and balances exploration with task completion based on instruction granularity.

DeFi (Decentralised Finance) [Link] provides financial instruments and services through smart contracts on block chain systems. Comparing to traditional finance systems, DeFi eliminates the need of brokerages, exchanges, or banks as intermediates, and allows users to perform various financial activities including lend/borrow funds, trade cryptocurrencies, and earn interests. 

The research project aims to build:

- An Energy Market Simulator for Future Energy Systems (Smart Grid). 

Future energy systems are envisioned to be running decentrally with full automatic control, high proportion of renewable energy (e.g., wind & solar), and abundant storage facilities. With many types of renewable energy sources are weather and climate dependent, accurate simulators with good visualization and data analytic capabilities are needed for operators to control the grid.

This project focuses on understanding and applying control charts as a tool for monitoring and improving process quality. It involves designing some basic control charts and evaluating their performance in detecting process variations under different conditions. The evaluation will be based on key metrics such as Average Run Length (ARL), false alarm rate, and detection speed, providing insights into the effectiveness of various chart types in maintaining quality standards.