Student topics

Accurately mapping large-scale infrastructure assets (power poles, bridges, buildings, whole suburbs and cities) is still exceptionally challenging for robots.The problem becomes even harder when we ask robots to map structures with intricate geometry or when the appearance or the structure of the environment changes over time, for example due to corrosion or construction activity.The problem difficulty is increased even more when sensor data from a range of different sensors (e.g. lidars and cameras, but also more specialised hardware such as …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Research centre(s)

Centre for Robotics

Software vulnerability is a major threat to the security of software systems. Thus, the successful prediction of security vulnerability is one of the most effective attack mitigation solutions. Existing approaches for software vulnerability detection (SVD) can be classified into static and dynamic methods. Powered by AI capabilities, especially with the advancement of machine learning techniques, current software has been produced with more sophisticated methodologies and components. This has made the automatic vulnerability proneness prediction even more challenging. Recent research efforts …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Computer Science

The construction industry plays a vital role in the global economy and there is a growing interest in utilising artificial intelligence (AI) to improve its productivity and efficiency. Despite the industry's significant contribution to the economy, it has faced challenges such as large cost overruns, extended schedules, and quality concerns. Nevertheless, AI is making significant strides to remove these issues by revolutionising various aspects of the construction industry. This is evident from enhancing project planning and design to improving construction …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Architecture and Built Environment

The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …

Study level

PhD, Master of Philosophy, Honours

School

School of Electrical Engineering and Robotics

Re-localisation in robotics involves the process of determining a robot's current pose, consisting of its position and orientation. This can either be within a previously mapped and known environment (i.e. prior map) or relative to another robot in a multi-agent setup. Re-localisation is essential for enabling robots to perform tasks such as autonomous monitoring and exploration seamlessly, even when they encounter temporary challenges in precisely tracking their location in GPS-degraded environments. For instance, consider the 'wake-up' problem, where a robot …

Study level

PhD

School

School of Electrical Engineering and Robotics

Meta-heuristics are powerful search algorithms for solving intractable optimization problems. There are many population based approaches, like genetic algorithms, evolutionary algorithms, particle swarm, etc. but most of these have a static population size.Viruses arise and attack populations periodically. They typically appear when populations become abundant. Viruses infect population members, and often reduce the number of individuals. Viruses create spaces for more individuals and balance competition.The concept of viruses may be mimicked and could be a useful optimization paradigm.

Study level

Honours

Faculty

Faculty of Engineering

School

School of Mechanical, Medical and Process Engineering

How can robots best learn to navigate in challenging environments and execute complex tasks, such as tidying up an apartment or assist humans in their everyday domestic chores?Often, hand-written architectures are based on complicated state machines that become intractable to design and maintain with growing task complexity. I am interested in developing learning-based approaches that are effective and efficient and scale better to complicated tasks.Especially learning based on semantic information (such as extracted by the research in semantic SLAM above), …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

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

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

Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Satellite and drone/UAV data has a great potential to provide large-scale analytics for many domain applications. However, the wide range of data of diverse nature (e.g., optical vs. SAR, high-resolution vs. wide-coverage, mono- vs. hyper-spectral, 2-D vs. 3-D) also poses significant challenges for analytics.Deep learning holds great promise to deal with these tasks. While the number of research in this area is increasing, there still exists challenges such as co-learning of multimodal data, limited data annotation, and uncertainty in the …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

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