Respiratory diseases such as COVID-19 are known to physically damage our airway and lungs, which in turn alters the produced respiratory sounds (e.g., cough, breadth). During the pandemic, cough classification has emerged as an accessible, low-cost, and environmentally friendly COVID-19 screening alternative, needing only a smartphone to collect and process cough samples. However, audio processing … full description “AI-powered COVID-19 detection via cough sounds (available)”
Category: Machine Learning
Algorithms for lifelong deep learning (available)
Continual learning [1], also known as lifelong learning, refers to the ability of an artificial intelligence system to continuously learn and adapt from new experiences over time. This is an important capability as it allows AI models to acquire new knowledge and skills as more data becomes available, without forgetting previously learned information. Continual learning … full description “Algorithms for lifelong deep learning (available)”
Are we alone? Discovering Earth-like exoplanets with Conformal Prediction (available)
Billions of exoplanets are orbiting around their stars outside our solar system [1]. But are they similar enough to our Earth so that life may have developed there? Often you can answer this question if you know the planet’s mass, radius, and orbiting period. Given the astronomic distances between us and the planets, however, you … full description “Are we alone? Discovering Earth-like exoplanets with Conformal Prediction (available)”
Attacking Large Pre-trained Programming Language Models (PLMs) via Backdoors (completed)
Project Description: Backdoors refer to a class of Machine Learning (ML) attacks where an adversary trains an ML model to intentionally misclassify any input to a specific label [1]. This is typically achieved by poisoning the training data, such that inputs are misclassified to a target label when the backdoor trigger is present. For instance, … full description “Attacking Large Pre-trained Programming Language Models (PLMs) via Backdoors (completed)”
Building a Full Causality Chain Across an Enterprise System (completed)
Data Provenance refers to records of the inputs, entities, systems and process that influence data of interest, providing a historical record of the data and its origins. To provide a holistic view of the data provenance in an enterprise system, the provenance records of the activities carried out on a client workstation is important. Last … full description “Building a Full Causality Chain Across an Enterprise System (completed)”
Computer Vision for Extreme Environments (available)
The use of data from extreme environments in computer vision have shown an increase of interest in recent years as drones and autonomous vehicles were introduced into new uses. Nuclear plants, deep underwater and space vehicles are some of the areas computer vision can be applied to develop a fully autonomous system. Furthermore, the development … full description “Computer Vision for Extreme Environments (available)”
Cybercrime and ransomware groups – data analysis (available)
Project background Cybersecurity is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks, in an ever increasingly complex threat landscape. These attacks constitute a variety of computer-enabled and computer-dependent crimes, broadly categorised as ‘cybercrime’. In order to be in a position to defend against these attacks and minimise … full description “Cybercrime and ransomware groups – data analysis (available)”
Deep learning based underwater image segmentation and object recognition (available)
In the past decade, advances in marine object recognition have been dramatically boosted for monitoring of underwater ecosystems. Traditional statistical analysis and ocean model simulation heavily depend on the availability of visual features. However, Due to light attenuation and scattering problem, the underwater images captured by optical imaging system are heavily degraded. As a result, … full description “Deep learning based underwater image segmentation and object recognition (available)”
Deep Learning-based Environmental Sound Classification (available)
Automatic sound classification attracts increasing research attention owing to its vast applications, such as robot navigation, environmental sensing, musical instrument classification, medical diagnosis, and surveillance. Sound classification tasks involve the extraction of acoustic characteristics from the audio signals and the subsequent identification of different sound classes. In this project, we will explore diverse deep neural … full description “Deep Learning-based Environmental Sound Classification (available)”
Distilling large language models (available)
Model distillation for large language models (LLMs) presents a key challenge in AI research: how to compress massive, computationally expensive models into smaller, more efficient versions while preserving their performance. Large models, such as GPT-4, require vast amounts of memory and processing power, making them impractical for real-time applications on edge devices or personal computers. … full description “Distilling large language models (available)”
Exploring optimization algorithms for deep sequence models (available)
Deep sequence models such as recurrent neural networks (RNNs) are a key type of architecture in modern deep learning, particularly for processing sequential data such as language text, speech, video, and time series data. RNNs have loops that allow information to persist and be passed from one step to the next, enabling them to effectively … full description “Exploring optimization algorithms for deep sequence models (available)”
Implementation of biologically inspired efficient deep learning models (available)
As deep learning models continue to grow in size and complexity to tackle increasingly difficult tasks, the need for efficient and scalable models becomes ever more important. Extremely large language models like GPT-4 require massive computational resources and expensive hardware to train and run. This makes them impractical to deploy at scale in many real-world … full description “Implementation of biologically inspired efficient deep learning models (available)”
Machine Learning for Crystal Structure Prediction (completed)
Crystal Structure Prediction (CSP) is one of the major problems in computational chemistry with numerous applications in real life. This is essentially the (global) minimisation of a continuous, high-dimensional, complicated function. Many heuristic methods have been proposed for CSP and recently new methods based on Machine Learning were introduced. The goal of this project is … full description “Machine Learning for Crystal Structure Prediction (completed)”
Machine Learning Library for OCaml (completed)
Frameworks for machine learning include Python’s TensorFlow [1]. These frameworks provide standard ways of specifying models that can be optimised by machine learning algorithms. OCaml [2] is a mature functional programming language with an expressive type system. DecML [3] is a prototype OCaml extension that facilitates implementing machine learning tasks, based on specifying models as … full description “Machine Learning Library for OCaml (completed)”
Mixed Nash Equilibria in Net Coordination Games (completed)
Net Coordination Games form a special class of many-player games with several applications in Theoretical Computer Sciene, Multi Agent Systems, and Semi Supervised Learning. Nash equilibria correspond to the stable outcomes and they are the prominent solution concept in games. It is known that Net Coordination Games possess a pure Nash equilibrium, but unfortunately, it … full description “Mixed Nash Equilibria in Net Coordination Games (completed)”
OSN Mining Platform – Building a public dataset for fake news research (available)
Project Description According to the Statista, in 2018 the UK had 44 million (66% of the population) active Online Social Networks (OSN) users. Whereas, globally, active OSN user population is 3,397 million. The most popular reason for using the OSN is to stay in touch with friends and family (42% of respondents) and stay up … full description “OSN Mining Platform – Building a public dataset for fake news research (available)”
Predicting and Explaining Drug Toxicity with Graph Neural Networks (available)
This student research project focuses on predicting and explaining drug toxicity using advanced machine learning techniques, specifically Graph Neural Networks (GNNs). Graph Neural Networks are a modern, powerful class of machine learning models widely used across diverse fields, from chemistry to social networks. However, one of the major challenges with traditional GNNs is their “black-box” … full description “Predicting and Explaining Drug Toxicity with Graph Neural Networks (available)”
Predicting Debug Symbols for Closed Source Binaries (completed)
Reverse engineering binaries, whether malicious or benign, is made more difficult by the absence of debug information. Variables and functions have had their identifiers “stripped”, so reverse engineers have to manually name them during analysis based on human understanding of the code functionality. The goal of this project is to use machine learning to predict … full description “Predicting Debug Symbols for Closed Source Binaries (completed)”
Quantum computing without quantum computers (available)
Quantum Technologies (QT) may revolutionise data science but are often unreliable. Classical and quantum noise makes most of the existing systems highly unstable. This generalised unreliability has limited their applicability to real-world computational problems. In special cases, quantum systems can be simulated on classical computers. As classical simulations are noise-free, they can be used to … full description “Quantum computing without quantum computers (available)”
Transparent Machine Learning – Shining the light in a black box world (completed)
Project Description Autonomy, based on Artificial Intelligence (AI), is at the very centre of many existing and future innovative and enabling technologies including autonomous vehicles, urban air mobility, smart cities and Industry 4.0. All decisions made by an AI system are based on the underlying algorithm design and its training/profiling set. An AI algorithm whether … full description “Transparent Machine Learning – Shining the light in a black box world (completed)”
Video Action Classification (available)
Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this project, we will explore a variety of deep neural networks for video action classification, owing to their great efficiency in spatial-temporal feature learning.