Honours and Masters project

AI can help an individual find information, generate alternatives and make decisions. Yet a group may lose something when everyone relies on the same tools. Independent exploration can decline, errors can become correlated, and a population can settle on a common answer that is worse than the alternatives it no longer considers. Individual improvement does not guarantee collective intelligence.

This project studies how patterns of AI use spread through a population and what they do to diversity, competence and welfare. Possible questions include:

Peer review asks scientists to judge work produced by their competitors, usually under severe time pressure. AI may help authors produce and submit more papers, but it may also help reviewers check arguments, code and data. The effects depend on how authors, reviewers and journals respond to one another.

AI safety often asks whether one system follows instructions or reaches the correct answer. Many important failures arise only when agents interact. Individually capable agents may exploit one another, fail to cooperate with unfamiliar partners, manipulate signals of trust, or pursue immediate rewards at the expense of accurate beliefs.

Energy systems generate large volumes of heterogeneous information, including meter and sensor measurements, weather forecasts, electricity-market data, network models, operational procedures, technical reports and regulatory documents. Converting this information into timely and reliable decisions normally requires specialised modelling tools and considerable domain expertise.

Electricity markets are becoming increasingly dynamic as variable renewable generation, utility-scale batteries, distributed energy resources and flexible demand play larger roles in the energy system. Market participants must make decisions under uncertain prices, renewable generation, electricity demand, network conditions and market requirements while coordinating assets across multiple services and timescales.

The rapid growth of electric vehicles (EVs) is transforming both transportation and energy systems. Private EVs, commercial fleets, autonomous vehicles and shared-mobility services can contribute to cleaner urban transport, but their charging demand also creates new challenges for electricity networks, transport systems and charging-service providers.

Households are becoming active participants in the energy system. In addition to consuming electricity, many now generate, store and manage energy through consumer energy resources (CERs), including rooftop solar, household batteries, electric vehicles, smart appliances and flexible loads. At the same time, smart electricity, gas and water meters—as well as appliance, weather and other contextual data—are creating new opportunities for artificial intelligence to understand and improve household energy use.

Large Language Models (LLMs) are increasingly used as AI agents that can remember information across sessions, use tools, and take actions on behalf of users. For example, an AI agent may remember a user’s preferred contacts, coding conventions, project notes, research rules, or previous task instructions. This long-term memory can make agents more useful and personalised, especially for tasks that continue over time. However, long-term memory also introduces new security risks. An agent may remember

Large Language Model (LLM) agents are increasingly extended through external skills, tools, plugins, and reusable instruction modules. These skills allow agents to perform complex tasks such as file processing, web search, code execution, data analysis, and workflow automation. However, current security checks usually evaluate each skill in isolation. A skill may appear benign when tested alone, but become harmful when combined with other skills in the same agent workflow.

This project is an exciting opportunity to work on a multidisciplinary initiative aimed at improving the efficiency and effectiveness of energy control room operations. By focusing on human factors, best practices in visualisation, decision support, explainable AI, and optimisation, this project explores how to present information effectively for control room operators, ultimately contributing to the stability and efficiency of energy grid operations.