Honours and Masters project

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.

Passive acoustic monitoring is a well-established, non-invasive technique for wildlife monitoring, with growing interest in bioacoustic individual-level recognition—the ability to distinguish individual animals based on their vocalisations. While existing approaches perform well when all individuals are known a priori, wildlife populations are inherently dynamic, making such closed-set assumptions unrealistic in natural environments.

This project aims to design and implement an Agentic AI for Human Assistance (the “assistant agent”) to support everyday information work, e.g. email coordination. Unlike conventional chatbots, the proposed system follows an agentic workflow: it can plan, use tools, and execute multi-step tasks, while incorporating Human-in-the-Loop (HITL) mechanism at critical decision points to ensure trustful, safety, controllability, and explainability.

Introduction

Rare neurodegenerative diseases, including the hereditary cerebellar ataxias, pose significant challenges for disease monitoring. Small patient cohorts, heterogeneous progression patterns, and slow rates of progression make it difficult to track disease change using conventional biomarkers. Although clinical rating scales remain the standard for assessing severity, they are subjective, prone to measurement noise, and often lack sensitivity to subtle longitudinal decline.

Despite an increase in the usage of AI models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end-user. Understanding why a model entails specific conclusions is crucial in many domains. A natural example of this need for explainability can be drawn from the use of a medical diagnostic system, where it combines patient history, symptoms and test results in a sophisticated way, estimate the probability that a patient has cancer, and give probabilistic prognoses for different treatment options.

Edge intelligence and Tiny Machine Learning (TinyML) have become key enablers for real-time, on-device intelligence in industrial IoT applications, such as predictive maintenance, anomaly detection, and process monitoring. TinyML allows inference to be performed locally, reducing latency, enhancing data privacy, and lowering energy consumption compared to cloud-based solutions.

In today's digital landscape, cyberattacks are increasingly impacting organisations by disrupting critical services and compromising sensitive data. As these attacks grow in volume and complexity, security teams are increasingly challenged to safeguard sensitive data and maintain operational continuity. Manual efforts of security assessment often led to inconsistent and delayed results, high operational costs, and increased window of opportunity for potential attackers. To effectively mitigate these risks, there is a pressing need for automated security assessment.