I am currently looking for PhD students that want to work with me in one of the following topics:

Applicants for a PhD position should hold a BSc and/or a Master degree in relevant areas, have a strong motivation for research in one of the above topics and proved academic performance or relevant project experience.

We offer an excellent research environment within the School of Computer Science and a strong supervisory team, with great opportunities to grow. 

If you are interested, please send me an email with your CV and transcripts. Also, a short project proposal would be an indication of your motivation to conduct research in one of these areas.


Dr  Aditya Acharya

Project: HuMaT - Reinforcement Learning for Human-Agent Teams.

The focus of this project is look at multi-agent reinforcement learning (MARL) to improve and automate human-agent teaming. This project is in collaboration with the Alan Turing Institute and ARL.

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Dr Claire Palmer

Project: Automating Deployment of Virtual Reality Training Solutions.

The project focussed on automating the creation of information models to support virtual reality training solutions. The project was sponsored by Innovate UK and was in partnership with Bloc Digital, a leading company in XR solutions located in Derby.

Dr Claire Palmer is now a research associate at Loughborough University.

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Mr Aju Ani Justus

Project: HuMaT - Reinforcement Learning for Human-Agent Teams. 

First supervisor: Prof Christopher Baber.

Reinforcement Learning (RL) has achieved exceptional success in recent years. Recently, a prominent area of research involves the extension to multi-agent reinforcement learning (MARL). The focus of this project is on building reliable and robust human-agent teams. In particular, the aims are: i) explainability – of the agents to the human, and of the human to the agents; ii) team behaviour in multi-human and multi-agent teams.

Mr Ziyue Chu

Project: Provably efficient model-free episodic MARL for collective decision-making, with application in swarms of drones.

Second Supervisor: Prof Christopher Baber.

For multi-agent reinforcement learning systems to learn complicated behaviour, several millions of interactions are frequently needed. Furthermore, the ability to generalise the acquired behaviour is typically limited. The combined difficulties with generalisation and sample efficiency significantly restrict the potential uses of multi-agent reinforcement learning. The goal of this research is to enable agents to learn robust, reusable skills that transfer to different contexts and to learn effective behaviours with less data by utilising distributed information and the multi-agent aspect of such systems. On the application side, the project aims to provide control of a swarm of drones for collective decision-making.

Mr Tuo Zhang

Project: Developing Scalable Multi-Agent Reinforcement Learning Algorithms through Game Theory.

Second Supervisor: Prof Per Kristian Lehre.

MARL is highly promising in solving real-world problems as it learns by sampling from the environment, bypassing the need for explicit problem modelling, which is often infeasible. However, its unpredictability in complex settings hinders its broader application, largely due to the absence of a solid theoretical foundation. Game theory, particularly evolutionary game theory, aligns closely with MARL in form, offering robust tools for theoretical analysis and foundation building. This report includes a literature review, research question statement, completed work, and future plans.


MSc Projects - Summer 2023

Mr Thomas Astley

Title: Using Machine Learning to optimise Laser Powder Bed Fusion process parameters for Dimensional Tolerance

In this dissertation, machine learning combined with image analysis is used to obtain predictions for optimum values for two parameters of the laser powder bed fusion process, beam compensation (BC) and contour distance (CD), when A20X powder is used. The main impact of these parameters is on the dimensional tolerance and accuracy of the smaller sections of the resulting product.

Mr Oliver Halliday

Title: Using Machine Learning to Optimise Process Parameters in Additive Manufacturing

This project aims to reduce the need for lengthy experimentation in additive manufacturing by producing a machine learning (ML) algorithm that utilises a limited amount of training data and is consequently able to predict the relative density of a print, as a measure of its quality.

Mr Ahmed Muadh Faizan Mohamed

Title: Process Parameter Optimisation in Additive Manufacturing: A Reinforcement Learning Approach

The aim of this project was to develop a reinforcement learning approach to optimise process parameters for metal additive manufacturing, in collaboration with Francesco Careri and Prof Moataz Attallah.

Miss Madhurima Sarkar

Title: Optimal Financial Investments to Mitigate Cascading Failures

This study enlightens the broader effects of using artificial intelligence in the financial context. We use a model-free Q-learning approach to mitigate against cascading failures. Our objective is to develop investment strategies in the form of portfolio holdings that would be optimised through a thorough analysis of past performance and potential rewards or penalties.

Mr Aiden Swartzberg

Title: Multi-Agent Reinforcement Learning for Mitigating Cascading Failures in Financial Networks through Adjusting Equity Goals

This project aims to provide a solution to mitigate systemic financial collapse by offering an automated approach for defining the equity goals within a network of interconnected institutions. We introduce a novel cooperative multi-agent reinforcement learning model designed to imitate the role of a central governing body, which sets the equity requirements of each company at different time periods.

Mr Haoming Zhang

Title: Network Model Analysis in Financial Contagion

Financial contagion is a crucial issue and its consequences can lead from the bankruptcy of a single institution to the onset of a financial crisis. This project studies the properties of the Watts-Strogatz network model and the Barabási-Albert network model. This project primarily focuses on the discussion and analysis of the effects of varying share-holding weights among multiple organisations in different network models.

MSc Projects - Summer 2022

Mr Jishag Azhikodan Chenarath

Title: Winter – The Medical Bot. A virtual buddy to keep you in company

The aim of this project was to develop an intelligent system or a bot that could act as a virtual assistant and a front-line defence against growing mental health issues, focusing on user-based information.

Mr Daniel Fitzpatrick

Title: Applying Machine Learning to Analyse Poker Gameplay

In this project, a set of dynamical Logistic Regression and Linear Regression models are compared, to see if we can predict other players' approximate hand qualities and aggression throughout a game in real-time. The models are trained utilising historical data from each player on the table.

Mr Kishan Odedra

Title: Investigating pedagogical approaches for spatial and logical reasoning tasks in virtual reality environments

The report aims to construct and compare a virtual reality application which teaches a learning task to conventional pedagogical mediums. Unity and Oculus Interaction SDK are the tools used for the development of the prototype. The learning task is primarily facilitated through a visualisation of an algorithm and subsequent testing of knowledge with immediate feedback.

Mr Conor Tansey

Title: EscapeNow A new social network built to connect aspiring travellers

The goal of this project was to build a full stack social network web application that facilitated users in finding other people to travel and explore the world with as well as sharing their own adventures and being inspired by others travel experiences.

Mr Henry Taylor

Title: A Comparative Analysis of Q-Learning, Minmax and Monte Carlo Tree Search for Connect-4 Artificial Intelligence

This dissertation aims to answer the question 'What is the strongest algorithm enabling AI to play a game Connect-4?'. Three popular game algorithms (Q-Learning, Minmax, and Monte Carlo Tree Search) are compared to more advanced versions.

Mr Rajesh Kanna Vaidyanathan

Title: Intelligent Connected Autonomous Vehicles Traffic Flow Regulation study based on Unity ML-Agents

This study describes the process of designing, developing, simulating, and evaluating a Multi-Agent Reinforcement Learning method for an intelligent connected autonomous vehicles traffic flow regulation scenario using the Unity ML-Agents platform.

Miss Shangqing Wei

Title: A Scalable Approach For Cooperative Multi-Agent Reinforcement Learning in Minecraft

Scalability issue is one of the bottlenecks of multi-agent reinforcement learning (MARL). As the number of agents increases, especially to a large-scale, the state-action space will grow exponentially, which leads to the curse of dimensionality and high computation complexity. Aiming to address the scalability issue of MARL, this project propose a scalable approach for a cooperative task in Minecraft.