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< Back Dr Mona Jaber Supervisor Mona Jaber is a lecturer in Internet of Things (IoT) who’s research is centred at the intersection of IoT and machine learning for sustainable development goals. In particular, she is interested in harnessing IoT data to model mobility trends in a digital twin platform that allows users to test future measures in a verisimilar virtual environment. Her research is grounded in privacy-preserving measures for capturing and analysing IoT data. She is the winner of a new investigator award research grant (DASMATE £500K) in which she examines distributed acoustic sensors systems and a privacy-preserving alternative data source to model active travel. She is interested in supervising students on the topic of serious game building that engages the public in shaping their neighbourhood through interventions in the virtual environment towards sustainable 15 minutes city goals. m.jaber@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/profiles/jabermona.html Other links Website https://www.linkedin.com/in/mona-jaber/ LinkedIn BlueSky Github Themes Accessibility Applied Games Game AI - Previous Next

< Back Dr James Goodman Queen Mary University of London iGGi Alum James has picked up degrees in Chemistry, History, Mathematics, Business Administration and Machine Learning. After a career in Consultancy and IT Project Management he is now finally doing the research he always wanted to. James is interested in opponent modelling, theory of mind and strategic communication in multi-player games, and how statistical forward planning can be used in modern tabletop board-games (or other turn-based environments). With a constrained budget, how much time should an agent spend thinking about it's own plan versus thinking about what other players might be doing to get in the way. How does this balance vary across different games? His secondary research interests are in using AI-playtesting as a tool for game-balancing and game-design. james.goodman@qmul.ac.uk Email Mastodon https://www.tabletopgames.ai/ Other links Website https://www.linkedin.com/in/james-goodman-b388791/ LinkedIn BlueSky Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): Seeding for Success: Skill and Stochasticity in Tabletop Games From Code to Play: Benchmarking Program Search for Games Using Large Language Models Skill Depth in Tabletop Board Games Measuring Randomness in Tabletop Games A case study in AI-assisted board game design Following the leader in multiplayer tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games MultiTree MCTS in Tabletop Games Visualizing Multiplayer Game Spaces TAG: Terraforming Mars Fingerprinting tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games AI and Wargaming Metagame Autobalancing for Competitive Multiplayer Games Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game Weighting NTBEA for game AI optimisation Re-determinizing MCTS in Hanabi Noise reduction and targeted exploration in imitation learning for abstract meaning representation parsing UCL+ Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound Themes Design & Development Game AI - Previous Next

< Back Dr Christian Guckelsberger Queen Mary University of London iGGi Alum + Supervisor Intrinsic Motivation in Computational Creativity with Applications to Games. (Industry placement at Splash Damage and Microsoft Research) This research investigates how we can engineer artificial systems that are creative in their own right. Christian addresses this challenge with computational models of intrinsic motivation (IM). Intrinsically motivated agents perform an activity for its inherent satisfaction rather than for some instrumental outcome. A classic example is to act in order to satisfy one’s curiosity. In both theoretical and applied studies, he demonstrates that models of IM can give rise to general, robust and adaptive creative systems. Christian has shown how models of IM can be used to create highly general non-player characters. Such characters can potentially be used in a wide range of games without previous knowledge of the game mechanics, reducing costs and effort in game development while increasing robustness and behavioural variety Christian’s ongoing research stretches beyond video games, investigating the role of computational models of IM for intentional agency, open-ended development and creativity in minimal lifeforms and artificial systems. Christian studied Computer Science, History of Art and Business in Germany and the UK and is now based in London, working towards a PhD in Artificial Intelligence. His work challenges the question how computers could ever become genuinely creative with a highly interdisciplinary approach based on Computing, Cognitive Science and Philosophy. Over the last few years, he published papers on a wide range of topics, held a tutorial on intrinsic motivation in video games, organised workshops on computational serendipity and spent three months at NYU’s Game Innovation Lab for a research collaboration. Christian has substantial industry experience, looking back at three years in the R&D department of SAP SE and a recent internship at Microsoft Research Cambridge. He enjoys working in an international environment with open-minded, passionate people. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/in/christianguckelsberger LinkedIn BlueSky Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Predicting game difficulty and engagement using AI players Embodiment and computational creativity Intrinsic Motivation in Computational Creativity Applied to Videogames. PhD Thesis. 306 pages. The Relationship of Future State Maximization and von Foerster's Ethical Imperative Through the Lens of Empowerment On the Machine Condition and its Creative Expression. Understanding and Strengthening the Computational Creativity Community: A Report From The Computational Creativity Task Force. Action Selection in the Creative Systems Framework Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (CORGIS) Generative design in Minecraft: Chronicle challenge Towards Mode Balancing of Generative Models via Diversity Weights Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Themes Game AI - Previous Next

< Back Remo Sasso Queen Mary University of London iGGi PG Researcher I hold a BSc and MSc in Artificial Intelligence at the University of Groningen (NL) and am currently a PhD student at the Queen Mary University of London under the supervision of Paulo Rauber. In addition to my academic work, I have worked as a Machine Learning engineer, and am currently the Head of AI at xDNA, an AI/Cybersecurity-based start-up from the Netherlands. Here I'm leading the initiative Project Aletheia, where we develop AI-driven tools to optimize the workflow of professional fact-checkers, with the overarching goal of ensuring information integrity in the world. Foundation World Models and Foundation Agents for Reinforcement Learning My research focuses on developing reinforcement learning algorithms that are both scalable and sample-efficient through Bayesian methods and model-based approaches, recently with a particular emphasis on Large Language Models (LLMs). My previous research focused on principled, efficient and scalable exploration algorithms for reinforcement learning, e.g. Poster Sampling for Deep Reinforcement Learning (ICML 2023), where we developed a reinforcement learning algorithm that can be considered state-of-the-art in Atari games. Currently I'm particularly interested in the integration of LLMs in the reinforcement learning framework, both as decision-making agents and simulators. My current research, called "Foundation World Models and Foundation Agents for Reinforcement Learning" investigates this integration in-depth and shows that large models show significant potential in various reinforcement learning tasks, ranging from decision-making in stochastic environments to serving as world models. r.sasso@qmul.ac.uk Email https://remosasso.github.io/ Mastodon Other links Website https://www.linkedin.com/in/remo-sasso-b9837a1ba/ LinkedIn BlueSky https://github.com/remosasso Github Supervisor: Dr Paulo Rauber Featured Publication(s): VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts Making Connections: Neurodevelopmental Changes in Brain Connectivity after Adverse Experiences in Early Adolescence Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Simultaneous multi-view object recognition and grasping in open-ended domains Posterior Sampling for Deep Reinforcement Learning Themes Game AI - Previous Next

< Back Prof. David Beer University of York Supervisor Professor Beer has been researching new and digital media since completing his PhD in 2006. This has included work on social media, mobile devices and algorithms. Over the last decade he has developed work exploring the social implications of data and metrics. His work has explored how automated decision making is impacting upon social connections and has looked at how the data that accumulates about us shaped the way individuals are understood and judged. He has recently conducted a study of the data analytics industry and produced a report into online targeting. His research areas for supervision include: The social power of algorithms Data analytics The power of data and metrics Critical analyses of data visualization The metricisation of everyday life Social media and social media data Online targeting Data harvesting and inequality Research themes: Game AI Game Analytics Game Design Games with a Purpose Computational Creativity Gaming data Algorithms in gaming Gamification and the social world david.beer@york.ac.uk Email Mastodon https://davidbeer.net/ Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing Game AI Game Data Player Research - Previous Next

< Back Peter York University of York iGGi Alum PhD student working in analytics and machine learning for esports broadcast and understanding. In particular working with Weavr on various projects related to broadcast and learning tools for Dota 2. Please note: Updating of profile text in progress Email Mastodon https://pete-york.github.io Other links Website LinkedIn BlueSky Github Featured Publication(s): Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports DAX: Data-Driven Audience Experiences in Esports A generalized framework for self-play training Themes Esports Game AI - Previous Next

< Back George Long Queen Mary University of London iGGi PG Researcher Available for placement George is an IGGI PhD student interested in AI assisted game design, particularly in how it can be used to assist in the creation and balancing of game mechanics. After graduating with a BSc in Computer Science at the University of Essex, he joined IGGI in 2021 to be able to research how Artificial Intelligence can be applied specifically to reduce the prevalence of Min-Maxing in Role-Playing Games. A description of George's research: My research focuses on the concepts of Min-Maxing and Meta in Role-Playing Games, and how we can use AI assisted game design to reduce their prevalence. Min-Maxing in Role-Playing Game refers to the idea of building a character in a Role-Playing Game by maximising their positive traits while minimising negative ones, often through exploiting game mechanics. This can cause optimal strategies to emerge which not only have the potential to upset the game balance, but when these strategies become prominent enough in the community to form a Meta, it can have wider consequences such as the shunning of players deemed not to be using optimal strategies, and loss of creative choice when building characters. There are two methods I am looking into to reduce the effectiveness of Min-Maxing. The first is using AI to discover these Min-Maxed strategies. Secondly, how AI can be used in the game balancing process to identify and modify the mechanics which enable these strategies. Currently, I am focusing on the first method, with my research looking into how we can measure the effectiveness of units in combat scenarios to identify which units could be considered unbalanced. g.e.m.long@qmul.ac.uk Email Mastodon http://www.longhouse.dev Other links Website https://www.linkedin.com/in/georgelonghouse/ LinkedIn BlueSky Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Themes Design & Development Game AI Game Data - Previous Next

< Back Dr Yongxin Yang Queen Mary University of London Supervisor Dr Yongxin Yang is a lecturer in financial technology at Queen Mary University of London, UK and he is also a part-time professor in finance at Southwestern University of Finance and Economics, China. His research is in the area of meta learning and its interactions with other machine learning paradigms like reinforcement learning. He has broad interests in applied machine learning, esp. for finance problems, for example, portfolio optimization and financial derivatives pricing. For the project of meta reinforcement learning, we want to explore the learning algorithms that can transfer an existing RL agent into a new task (e.g., a new game episode) with the minimal effort on retraining it. For the project of AI Economist, we are going to create a multi-agent system, where each agent behaves like a human being who will interacts with the environment and other agents (e.g., produce and trade), then we study how a certain policy (e.g., monetary and tax) affects the economy. yongxin.yang@qmul.ac.uk Email Mastodon https://yang.ac/ Other links Website LinkedIn BlueSky https://github.com/wOOL/ Github Themes Applied Games Game AI Game Data - Previous Next

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Locations (All) iGGi is a collaboration between Uni of York + Queen Mary Uni of London: the largest training programme worldwide for doing a PhD in digital games. Locations iGGi Queen Mary University of London (QMUL) iGGi QMUL is located at the heart of East London on Queen Mary, University of London's Whitechapel campus. Read More University of York (UoY) iGGi York is located just outside the City of York's centre, on University of York's East Campus. Read More Goldsmiths, University of London (Goldsmiths) iGGi Goldsmiths is located in New Cross, South East London, five miles from central London. Read More University of Essex (UoE) iGGi Essex is located two miles from the historic city of Colchester and set in over 200 acres of beautiful parkland. Read More

iGGi PhD Projects - listing iGGi PhD Projects 2023 This page displays the supervisor-proposed PhD projects on offer for our 2023 intake: If you are interested in any of the projects listed and would like further details and/or to discuss, please email the respective project supervisor. Please note that you can also frame your own project independently as long as you have secured a supervisor's support. For a list of available supervisors please see the accepting students section of our website. While iGGi has checked that the project descriptions listed below are within iGGi's scope , we wish to highlight that you are still responsible for ensuring that your proposal, too, is in line with this scope, and we would further like to point out that supervisor-framed projects are not prioritised in the application selection process: they are judged by the same criteria as applicant-framed proposals. For guidance to make sure that the proposal you submit (regardless of whether it has been supervisor-framed or created entirely by you) sits within iGGi's scope please refer to this link: https://iggi.org.uk/iggi-scope Select by Theme: Game AI Design & Development Player Research Game Audio Game Data Immersive Technology Creative Computing E-Sports Applied Games ALL Projects Design and Development Realistic physical interaction of 3D point cloud objects This project will investigate geometric and machine learning approaches to developing 3D game assets. Price Design and Development Duration Miles Hansard, Changjae Oh Read More Game Data Building Player Profiles in Mobile Monetisation: A Machine Learning Approach This project aims to use machine learning techniques to segment and profile mobile gamers in terms of their in-game spending. Price Game Data Duration David Zendle Read More Creative Computing Novelty Optimisation Can we identify and automatically balance the right amount of novel content we serve to players? Price Creative Computing Duration Jeremy Gow, Sebastian Deterding Read More Player Research Modelling the interactions in metaverse videogames This project will seek to inform AR and VR enabled videogames by analysing existing online platforms supporting these technologies. Price Player Research Duration Ignacio Castro Read More Game AI Automatic Evaluation of Tabletop Games This project proposal aims to research and develop methods to accurately evaluate the impact of modern Tabletop Games components in different aspects of gameplay. Price Game AI Duration Diego Pérez-Liébana Read More Game AI Principled and Scalable Exploration Techniques for Reinforcement Learning In this project, you will develop principled and scalable exploration techniques based on reducing model uncertainty. Price Game AI Duration Paulo Rauber Read More Creative Computing Novel video narrative from recorded content This project constructs novel video narratives from recorded content under employment of deep learning tools and techniques. Price Creative Computing Duration Nick Pears Read More Game Audio Machine Learning of Procedural Audio This work will investigate whether procedural audio can fully replace the use of pre-recorded sound effects. Price Game Audio Duration Joshua Reiss & Nemesindo Read More Immersive Technology Places That Don't Exist The goal of this project is to combine state-of-the-art 3D computer vision and procedural content generation to create game-ready scene models and assets from existing media. Price Immersive Technology Duration William Smith Read More Immersive Technology Tactile Interaction With Virtual Reality Content In this project the student will explore the use of vibrating motors distributed over the human hand (e.g. using a wearable glove) to give tactile feedback about the physical interactions happening in a VR. Price Immersive Technology Duration Lorenzo Jamone & Valkyrie Industries Read More Game AI Evolving Perception for Game Agents This project will investigate game agents in open world games which evolve their senses and world representation alongside learning what actions to take in each state. We will evolve game agents with highly alien behaviours which nevertheless have high fitness in the open world environment, while investigating important scientific questions about how senses and world representations evolved in humans. Price Game AI Duration Alex Wade, Peter Cowling Read More Game Data Understanding ongoing mental states using video games: applications to mental health research. This project will use a combination of neuroscience and advanced data analysis methods to examine the link between video game play and the brain. We will use a combination of cutting-edge data analytic techniques applied to large, existing video game telemetry datasets and neuroimaging experiments designed to measure changes in ongoing mental states while people play simple video games. Price Game Data Duration Alex Wade Read More Load More

iGGi Unconference Group Outcomes (List) iGGi is a collaboration between Uni of York + Queen Mary Uni of London: the largest training programme worldwide for doing a PhD in digital games. iGGi Research Retreat "Unconference" Since Summer 2024, iGGi has been running a "Research Retreat" (aka "Unconference") at a secluded cottage village in Derbyshire. We gather 30 people made up of iGGi PGRs, Alumni, Staff and Industry Partners and ask: What are the nagging questions from your PhD research (or, for industry partners: in your work or from your pet project) where you could use new eyes and approaches? What are the most intriguing research questions in your area? What ideas has your research/work/hobby thrown up that you'd love to take a closer look at? What are the key research ideas and questions in other areas? The ideas to be explored emerge during the retreat: Everyone can propose an idea/topic/"problem", and participants then choose which small group they would like to join to explore further. To play with new ideas. Below you can find a selection of group outcomes from the 2025 iGGi Unconference iGGi Research Retreat "Unconference" August 2025 The Future of AI This group discussed what the "future of AI" might look like, how it will change us as a society and what possibilities it could create. Read More iGGi Research Retreat "Unconference" August 2025 Social Simulation Game on a Graph / Network This group started off with the idea of a cellular automaton and set off to investigat how such a simple structure could be rendered as a playable simulation of social dynamics. Read More iGGi Research Retreat "Unconference" August 2025 Generative AI, Abstraction and Epistemology This group tried to come up with the skeleton of a short presentation for technologists in the former CIS region about the topic. Read More iGGi Research Retreat "Unconference" August 2025 Trust and Freedom in Transformative Games This group discussed how games build or break trust and the factors involved in creating tustworthy games. Read More