I am currently looking for PhD students that want to work with me in one of the following topics:
Game Theory: theory and applications of game theory, evolutionary game theory and mean-field game theory. Examples include bio-inspired models, consensus, cooperation and competition, rational and irrational players.
Reinforcement Learning and Multi-Agent Systems: assessing generalisability, robustness, efficiency and scalability of multi-agent reinforcement learning algorithms in control and games.
Automatic control: the study of dynamical systems for a range of domains, including biology, additive manufacturing, chemical reactions, and the use of data-driven control approaches to ensure safety, controllability and robustness.
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.
Project: Safeguarded AI-enabled Biopharmaceutical Manufacturing. 2023-present
The focus of this project is combine mathematical modelling of degradation and purification processes with probabilistic guarantees for machine learning in this domain. This project is in collaboration with AstraZeneca.
Project: Safeguarded AI-enabled Biopharmaceutical Manufacturing. 2023-present
The focus of this project is combine mathematical modelling of degradation and purification processes with probabilistic guarantees for machine learning in this domain. This project is in collaboration with AstraZeneca.
Project: HuMaT - Reinforcement Learning for Human-Agent Teams. 2023-2024
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.
Project: Automating Deployment of Virtual Reality Training Solutions. 2020-2022
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.
Project: Learning to Cooperate and Cooperate to Learn.
Co-supervisor: Dr Iran Mansouri.
Start date: February 2025.
Project: Learning Non-stationary Mean-field Games.
Second supervisor: Dr Mirco Giacobbe.
Start date: September 2024.
Project: HuMaT - Reinforcement Learning for Human-Agent Teams.
First supervisor: Prof Christopher Baber.
Start date: September 2024.
Project: Provably efficient model-free episodic MARL for collective decision-making.
Second Supervisor: Prof Christopher Baber.
Start date: January 2024.
Project: Multi-agent learning with theoretical guarantees from evolutionary game dynamics.
Second Supervisor: Prof Per Kristian Lehre.
Start date: January 2023.
Project: A combined multi-agent reinforcement learning and MDP framework for coverage control in swarm of drones.
Supervisors: Dr Gian Paolo Incremona, Prof Patrizio Colaneri.
Project: A physics-based reinforcement learning approach for the characterisation of dross in L-PBF additive manufacturing.
Supervisors: Prof Moataz Attallah, Dr Diego Giovanni Manfredi, Prof Mariangela Lombardi.
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Title: Developing a simulation framework for coverage control using MARL in swarm robotics.
Title: Agent-based modelling for collective decision-making in honeybees.
Title: Physics-based NNs for accurate measurements in additive manufacturing.
Title: A web application for process parameter optimisation in additive manufacturing.
Title: A reinforcement learning approach for the characterisation of dross in additive manufacturing.
Title: A game theoretic framework to solve the changing lane problem in autonomous driving.
Title: Cascading failures in financial markets via mean-field games.
Title: A multi-agent reinforcement learning approach to minimise cascading failures in financial markets.
Title: A reinforcement learning framework with enthalpy-based reward functions for process parameter optimisation in additive manufacturing.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.