Research projects in Information Technology

This project examines how films produced in Asian markets perform in terms of commercial success and critical recognition using real-world industry data. Students will compile a dataset of films from regions such as Hong Kong, China, South Korea, and Southeast Asia, drawing on publicly available sources to analyse indicators such as production budget, box office revenue, streaming platform release, and awards. Using quantitative data analysis methods, the project aims to identify patterns and factors associated with successful film outcomes.

This project explores the development of digital tools that measure the carbon footprint and nutritional impact of meals to support sustainable eating. It aims to integrate environmental and nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions.

Goal Recognition is the task of inferring the goal of an agent from their action logs. Goal Recognition assumes these logs are collected by an independent process that is not controlled by the observer. Active Goal Recognition extends Goal Recognition by also assigning the data collection task to the observer. This Ph.D. project will provide a unified probabilistic and decision-theoretic perspective to fundamentally solve the central question: how should an observer act in an environment to actively uncover the goal of the agent?

We have several PhD  opportunities available in areas such as Multimodal Large Language Models (MLLM) for human understanding, MLLM safety, and Generative AI.

If you have published in top-tier conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, etc.), you will have a strong chance of receiving a full PhD scholarship.

Two PhD scholarships are available, funded through a DECRA, which explore the use of deep learning models for uncertainty quantification. 

While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one hospital or population often fail when applied elsewhere due to distributional shifts.  Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and privacy constraints, robust solutions are essential.

We have several PhD,  Research Assistant (RA) and master research thesis’s opportunities available in areas such as Multimodal Large Language Models (MLLM) for human understanding, MLLM safety, and Generative AI.

If you have published in top-tier conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, etc.), you will have a strong chance of receiving a full PhD scholarship.

As education systems increasingly adopt AI to support teaching and learning, the automation of assessment and feedback processes has emerged as a critical area of innovation. Large-scale learning environments—such as MOOCs, online degrees, and data-intensive Learning Management Systems—necessitate scalable solutions to provide timely, high-quality feedback. However, existing AI-powered assessment systems often raise ethical, pedagogical, and fairness concerns, including issues of bias, explainability, and learner agency.

We are interested in understanding genetic variation among individuals and how it relates to disease. To do this, we study genomic markers or variants called single nucleotide polymorphisms, or SNPs for short. A SNP is a single base position in DNA that varies among human individuals. The Human Genome Project has found that these single letter changes occur are all over the human genomes; each person has about 5M of them!  While most SNPs have no effect, some can influence traits or increase the risk of certain diseases.

Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying structure.