Student topics

Advances in sequencing technologies over the past two decades have led to an explosion in the availability of genomic sequence data and an increasingly urgent need for scalable clustering and search facilities. One approach is to encode sequences as binary vectors in a high-dimensional space, simplifying the comparison and allowing it to be computed very rapidly using bit-level operations.Coupled with these ideas is the need to provide clustering methods and efficient indexing and lookup in response to search queries. One …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

In the 21st Century, there is an abundance of data, often containing insights that could benefit a number of stakeholders. However, despite this opportunity, it is often the case that the data is sensitive and can not be released by organisations or government agencies due to privacy concerns. One possible solution to the above dilemma is to instead carefully construct a 'twin' data set that contains similar information (and ideally, the same insights) as the original data set, but without …

Study level

Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

Genomic sequencing has changed radically since the first public sequencing projects more than 25 years ago. The original human genome project cost more than two billion dollars; sequencing a human genome now costs as little as a thousand, and we may sequence whole viruses and bacteria as a matter of routine.The challenge now lies in rapidly analysing these genomes as they appear, and understanding quickly whether there is anything interesting in the new sequence to warrant further inquiry. This project …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

The missing data problem is often unavoidable for real-world data collection systems because of a variety of factors, such as sensor malfunctioning, maintenance work, transmission errors, and so on. Filling in missing information in a dataset is an important requirement for many machine-learning algorithms that require a complete dataset as input. Data imputation algorithms aim at filling the missing information in a dataset. Many missing data imputation techniques exist in the literature, with applications demonstrated on various types of datasets. …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Civil and Environmental Engineering

Research centre(s)

Centre for Data Science

Cancer is an extremely heterogeneous disease, particularly at the cellular level. Cells within a single cancerous tumour undergo vastly different rates of proliferation based on their location and specific genetic mutations. Capturing this stochasticity in cell behaviour and its effect on tumour growth is challenging with a deterministic system, e.g. ordinary differential equations, however, is possible with an agent-based model (ABM). In an ABM, cells are modelled as individual agents that have a probability of proliferation and movement in each …

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

In the age of Big Data, machine learning methods, and modern statistics the adage "all models are wrong but some are useful" has never been so true. This project will investigate data science approaches where more than one model makes sense for the data. Is it better to choose a single model or is there something to be gained from multiple models?This project will look at variable selection methods, penalised regression, Bayesian model averaging and conformal prediction. The research has …

Study level

Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

Several open-source projects drive modern-day IT applications. However, some open-source projects get compromised by malicious attackers, who include malware to the code to compromise the security of the application users.This project will investigate approaches for securing the open-source software.

Study level

Honours

Faculty

Faculty of Science

School

School of Computer Science

Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science
Centre for the Environment

The Computational Communication and Culture research program within the Digital Media Research Centre investigates how rapid advances in computation and human-machine communication are transforming society, through automation and AI, the Internet of Things, and disintermediating technologies such as blockchain. We draw on and extend computer science and critical humanities theory and methods (including agent-based modelling, machine vision, critical simulation, and information visualisation) to help explore and explain emergent phenomena in the digital media environment, including the fundamental transformation of communication …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Creative Industries, Education and Social Justice

School

School of Communication

Research centre(s)

Digital Media Research Centre

Networks have been extensively used to capture social interactions, by representing individuals as nodes and their relationships as edges.Such networks have been used to model the spread of epidemics. A few nodes are 'infected', and over time they gradually infect their neighbours on the network, who in turn infect their neighbours, etc. This type of model can then be used to simulate different intervention strategies aimed at containing outbreaks.However, an important limitation is the difficulty to visualise these networks when …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

Bayesian statistics provide a framework for a statistical inference for quantifying the uncertainty of unknowns based on information pre and post data collection.This information is captured in the posterior distribution, which is a probability distribution over the space of unknowns given the observed data.The ability to make inferences based on the posterior essentially amounts to efficiently simulating from the posterior distribution, which can generally not be done perfectly in practice.This task of sampling may be challenging for various reasons:The posterior …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science