< Back Dr Ryan Spick University of York iGGi Alum Deep Learning for Procedural Content Generation in Virtual Environments Ryan Spick is a PhD student with a computer science background, working on methods to improve how content (models, terrain, assets etc.) is created with an autonomous focus, with the main focus on generative deep learning to augment real-world data through a series of neural network layers to learn unlying properties of these data. Ryan has published a variety of papers around his main topic of generating content, such as terrain generation using generative adversarial networks and 3D voxel coloured model generation, to collaborations on other topics using deep learning, such as death prediction in a multiplayer online game and applying a recent map-elites algorithm. He has also worked with several leading industry researchers/games companies to further develop his research skill.If you have any ideas or collaboration opportunities please get in contact through any of the mediums below. Please note: Updating of profile text in progress Email ryan.spick@hotmail.co.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): System and Method for Point Cloud Generation System and method for training a machine learning model Robust Imitation Learning for Automated Game Testing Behavioural Cloning in VizDoom Utilising VIPER for Parameter Space Exploration in Agent Based Wealth Distribution Models Human Point Cloud Generation using Deep Learning Naive mesh-to-mesh coloured model generation using 3D GANs Realistic and textured terrain generation using GANs Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Time to die: Death prediction in dota 2 using deep learning Themes Game AI - Previous Next

< Back Dr David Gundry University of York iGGi Alum Using Applied Games to Motivate Speech Without Bias (Industry placement Lightspeed Research) Eliciting linguistic data faces several difficulties such as investment of researcher time and few available participants. Because of this, many language elicitation studies have to make do with few subjects and coarse sampling rates (measured in months). It would be ideal if a game could crowd-source relevant linguistic data with frequent, short game sessions. To this end, David’s research is looking into how games shape and elicit players’ linguistic behaviour. The established design patterns of gamification do not apply to a domain that lacks a ‘correct’ answer like language or personal beliefs and attitudes. David’s research shows how a player’s strategic goals will systematically bias data collection. It also shows how to design around this. The conclusion: The player’s choice of how to express a given datum must be strategically irrelevant in the game. David can remember the halcyon days when he had the free time to play games. Now he’s doing a PhD and has a one-year-old. He has an background in linguistics. He loves writing expressive code and designing clever little games. He wants to show that research games can be fun, not just effective. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Trading Accuracy for Enjoyment? Data Quality and Player Experience in Data Collection Games Designing Games to Collect Human-Subject Data Validity threats in quantitative data collection with games: A narrative survey Busy doing nothing? What do players do in idle games? Intrinsic elicitation: A model and design approach for games collecting human subject data Themes Applied Games - Previous Next

< Back Dr Laura Helsby University of York iGGi Alum Laura has been gaming since she was five years old and got her first Gameboy Colour. She achieved a BSc in Psychology from the University of York and a MSc in Foundations in Clinical Psychology from Newcastle University. Currently she is employed as a Lecturer in Psychology at LJMU. Her research interests include the impact of technology on wellbeing and mental health, including research into games, social media, AI and other forms of technology use. She has a particular interest in qualitative work and is interested in both academic and industry collaborations. Laura's PhD focused on how people with persistent low mood play and experience games, and how this interacts with their mood and wellbeing. It explored gaming preferences, motivations for play, and changes in mood and wellbeing experienced during play. Overall, this thesis made three key contributions to knowledge: 1) People with persistent low mood desire low-effort gaming experiences, 2) Games have an overall positive impact on persistent low mood and this impact motivates play, and 3) Persistent low mood symptoms may reduce the benefits gained from gaming. This research has applications in game design, mental health and helps contribute to the understanding of player experience. Email l.j.helsby@ljmu.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Paul Cairns Dr Jo Iacovides Featured Publication(s): ‘The bandwidth comes and goes’: Gaming preferences, habits and attitudes in a persistent low mood population Exploring the player experience of people with persistent low mood "Leave our kids alone!": Exploring Concerns Reported by Parents in 1-star Reviews "Leave our kids alone!": Exploring Concerns Reported by Parents in 1-star Reviews Do People Use Games to Compensate for Psychological Needs During Crises? A Mixed-Methods Study of Gaming During COVID-19 Lockdowns Themes Applied Games Player Research - Previous Next

< Back Dien Nguyen Queen Mary University of London iGGi PG Researcher Available for placement I graduated from the University of California, Irvine with a BSc in Computer Game Science where I became interested in the intersection of games and artificial intelligence – applying search, planning, evolutionary methods and knowledge representation to game playing and game design. My long-term goal is to work on the problem of formalizing game elements, representing game systems in a way that allows for automatic reasoning and inference. I also enjoy playing games where I can customize and theorycraft my playstyle to satisfy certain gameplay fantasies while beating the game. My current research is within the field of Automated Game Design Learning, an emerging field in AI research with the purpose of learning game design models through playing. The current strategy is to play out the full game in thousands of iterations, which can be impractical for complex games with large state space and computationally expensive forward models. My research will focus on applying Go-Explore—a recent exploration paradigm that outperforms many state-of-the-arts—to improve the efficiency of automated playtesting of tabletop games by using an archive of interesting game states to reduce the time needed for self-play. The research will be primarily conducted within the TAG framework and aim to be game-agnostic. On successful completion, this research will improve game development cycles, resulting in higher-quality games, and potentially give unique insights into the game design process. Email d.l.nguyen@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Diego Pérez-Liébana Featured Publication(s): Game-Agnostic Value Functions through Automatic JSON Feature Extraction JSON-Bag: A generic game trajectory representation Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis Themes Applied Games Creative Computing Design & Development Game AI - Previous Next

< Back Dr Nathan John Queen Mary University of London iGGi Alum After graduating with a MEng in Computer Science from the University of Bristol, Nathan joined the games industry as a programmer, working for Climax Studios, Gaming Corps and Freejam, before moving to a career as a general software engineer, while still developing indie games on the side. His experiences across a range of industries sparked a passion for testing, and left him wondering if there were was to improve the automated testing in game development. Borne from an experiment Nathan had performed training AIs to play his indie game WarpBall, in which he found the agents solved for exploits in the authored AI rather than playing the game well, his research project proposes a novel method for improving the quality of behaviour of human authored agents by pitting them against trained agents and observing what bad behaviours/exploits the trained agents reveal. Authored agents refer to AI agents whose actions are explicitly designed by programmers using traditional techniques such as Utility functions, Behaviour Trees and state machines; trained agents refer to agents whose behaviour is learned by playing many games against the authored agents. Email n.m.john-mcdougall@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Adversarial Behaviour Debugging in a Two Button Fighting Game Why is debugging video game AI hard? Themes Design & Development Game AI - Previous Next

< Back Dr Kyle Worrall University of York iGGi Alum Kyle is a final-year PhD researcher at the Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI) at the University of York, where his work centers on pioneering deep learning-driven music tools for video game composers. In addition to his research, Kyle is a Lecturer in Games Programming at Edge Hill University, where he encourages the next generation of game developers to appreciate the critical role of audio in interactive experiences. Beyond academia, Kyle is the Founder of Cocreative Technology, an ethical AI music startup on a mission to empower musicians with cutting-edge, AI-driven tools that amplify creative expression, combat burnout, and elevate the emotional depth of game soundtracks. Kyle's research explores how deep learning and generative AI can enhance the creative workflow of video game composers, and improve the experience of players by reducing musical repetition. His work spans symbolic music generation, and real-time adaptive music systems, aiming to improve the emotional expressiveness and of game audio. His recent publications focus on deep learning models for interactive music authoring, expressive performance modelling, the ethical considerations in AI-assisted creativity, and the integration of neural networks with procedural music generation in games. By combining symbolic AI and audio signal processing, Kyle develops tools that support composers in ideation, iteration, and adaptive composition, while remaining transparent and musically intuitive. An experienced speaker, Kyle has presented at leading industry events, including Airwiggle's AirCon 2025, Game Sound Con 2024, Audio Dev Con 2024, the Global Arts and Psychology Symposium 2023, the Play Again Symposium 2024, and the Digital Creativity, Industry and Culture Conference 2022. He is also a regular contributor to the IGGI Conference (2020–2024), and has been featured in TechCrunch, Dazed, The Story of the Sound, and The Audio Programmer podcast, as well as featured on a panel with leaders in game audio from Meta and Sony. Email kyle.worrall@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Jon Hook Dr Tom Collins Dr Josh Reiss Featured Publication(s): Expressive Performance Rendering for Reduced Music Repetition in Video Games RenCon Report: Cue-Free Express & Pedal Final Fantasy VII Remake Music Redesign for Evolved Expectations Across Console Generations Considerations and Concerns of Professional Game Composers Regarding Artificially Intelligent Music Technology Comparative evaluation in the wild: Systems for the expressive rendering of music Reflection Across AI-based Music Composition The Ethics of Creative AI Themes Creative Computing Game Audio https://www.youtube.com/watch?v=m5vCJCB2-2A https://www.youtube.com/watch?v=AllYuKKxks8 Previous Next

< Back Younès Rabii Queen Mary University of London iGGi PG Researcher Available for post-PhD position Younès is an awarded game designer and generative AI researcher. Their current research is concerned with the relationship between a game's rules, its narrative, and how to build AI systems that can understand these relationships, manipulate them, and invent new ones. Younès also has been a game developer for the past 10 years. They specialize in crafting new forms of play and making it accessible for their peers. Their work has been previously exposed in the French embassies and international conferences like the Game Developers Conference, the Gamedevs of Color Expo and the A MAZE Festival. A description of Younès' research: Younès' research goal is to bring to video games some of the most interesting properties of roleplaying games: their ability to trust every player with building a part of the game, and their ability to generate both new narrative and gameplay on the fly. Younès is working both on the AI techniques needed to allow that, and how to design the social spaces around those games in a way that won't hurt players or abuse creators. For the end of their PhD, Younès is designing a prototype in that new genre, counting among the first games to contain a form of Live Automated Game Design. Email yrabii.eggs@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Mike Cook Dr Jeremy Gow Featured Publication(s): "Hunt Takes Hare": Theming Games Through Game-Word Vector Translation " Hunt Takes Hare": Theming Games Through Game-Word Vector Translation Why Oatmeal is Cheap: Kolmogorov Complexity and Procedural Generation Revealing game dynamics via word embeddings of gameplay data Themes Creative Computing Design & Development Game AI - Previous Next

< Back Evgenii Kashin University of York iGGi Alum Evgenii, a Computer Science enthusiast, began crafting games in school using the Warcraft3 editor. He spent five years as a Machine Learning Engineer, excelling in computer vision and graphics. His work at Snap included creating engaging lenses and researching 3D object capturing. An ECCV2020 article on face manipulation, with over 100 citations, is a testament to his prowess. Away from work, he enjoys bouldering, hiking, racing, and gaming. My research is dedicated to establishing a cost-effective approach for creating and generating 3D scenes for game development, a critical aspect of modern VR/AR applications. Harnessing the potential of generative visual content, I aim to develop algorithms capable of realistically completing 3D scenes from a few images. This could revolutionize the entertainment and creative industries, particularly game development. Picture having only a couple of images from your favourite film and envisioning the entire scene. Such technology can enhance the efficiency of 3D artists, democratize game development, and serve as entertainment in itself. Currently, I am developing an algorithm to achieve this goal. The proposed solution employs a general pretrained text-to-image model for supervision, with a NeRF 3D representation of the scene. The central concept involves iterative outpainting, where each iteration updates the NeRF weights. Email evgenii.kashin@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor Dr William Smith Featured Publication(s): The Sky's the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and Outside-In Visibility Stylegan2 distillation for feed-forward image manipulation Themes Creative Computing - Previous Next

< Back Océane Lissillour University of York iGGi PG Researcher Available for placement I started my journey as an undergrad in psychology specialising in ergonomics. It was then that I realised that I could study video games for a living. This led me to relocate myself to the south of France to, mostly enjoy the sun, but also undertake my dream MSc in Technological Innovation and Cognitive Psychology. As part of my master’s internship I had the pleasure to work in collaboration with researchers from the CLLE, a laboratory branch of the national research centre. This helped me develop my knowledge and awareness of video games and motivational processes and convinced me that if I could survive the bureaucracy of the French Government I could probably do a PhD. Afterwards, I moved on to work in Aeronautics for a brief time but landed back to academia in 2024 to join the wonderful iGGI community as a postgraduate researcher. A description of Océane's research: My research focuses on the act of taking a regular activity and ‘gamifying’ it by applying mechanics commonly found inside video games (such as points). This technique has been widely applied within research but its psychological aspects have seldom been studied. The aim of this thesis is to assess the efficacy and applicability of this technique, particularly the score element mechanic. It focuses on understanding the functionalisation of numerical values in games as this has not been studied previously in the literature. This research hopes to contribute to the better understanding of the impact of the design of points on motivation and behaviour. It has been proposed in precedent literature that the use of gamification will provide a partial solution to the critical decline in learners’ motivation and engagement the schooling system faces today. Email oceane.lissillour@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Abi Evans Prof. Sebastian Deterding Themes Design & Development Player Research - Previous Next

< Back Prof. Simon Colton Queen Mary University of London iGGi Co-Investigator Supervisor Simon Colton is an AI researcher with particular focus on issues of Computational Creativity, where we engineer software to take on creative responsibilities in art and science projects. He undertakes projects advancing the state of the art in generative technologies such as evolutionary approaches and deep learning, and uses these to help develop software such as The Painting Fool, The WhatIf Machine, the Wevva game designer, the HR3 automated code generator, and the Art Done Quick casual creator for visual art. In turn, these software systems and their output are used in cultural projects such as a poetry readings, art exhibitions, game jams, and even the production of a West-End musical. This enables Simon to undertake much public engagement, with coverage from the BBC, The Guardian, MIT Tech Review, The New Scientist and many others. These practical and cultural projects inform an evolving philosophical discourse around what it means for machines to be creative, and Simon has co-authored numerous essays driving forward our understanding of this important topic. In this way, he has helped to introduce ideas such as automated framing of products and processes, issues of authenticity and the notion of the machine condition, i.e., what the lived experience of a machine is, and how this could be expressed by that machine through creative production. He is particularly interested in supervising students in project where we apply generative technologies to applications in videogame design, visual art, software engineering, music and text generation. One particular current interest is stretching the boundaries of both what can be achieved by, and our understanding of, generation deep learning methods such as generative adversarial networks (GANs) and auto encoders. Another current interest is the design of casual creators, which are creativity support tools where the focus is on users having fun, rather than on efficient, professional production of artefacts. He is currently developing a casual creator for visual art called Art Done Quick for public release, which employs evolutionary and deep learning techniques to deliver a fun-first experience while users make decorative art pieces. Any project involving generative technologies is of interest to Simon. Research Areas: Game AI Game Audio and Music Game Design Computational Creativity Player Experience Casual Creators Generative Deep Learning Email s.colton@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Accessibility Creative Computing Game AI Game Audio Player Research - Previous Next

< Back Dr Anne Hsu Queen Mary University of London Supervisor Anne Hsu’s research includes machine learning, artificial agents, natural language processing and learning, human decision making, interaction design, and well-being technology. Her interests include developing interactive systems that use machine learning and understanding of human psychology to improve human behaviour. She is particularly interested in supervising students with a machine learning, design, HCI, or behavioural sciences background on the following topics: understanding and designing for curiosity in games design for behaviour change motivational/educational games Research themes: Game AI Game Design Games with a Purpose Player Experience Gamification Email anne.hsu@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development Esports Player Research - Previous Next

< Back Peyman Hosseini Queen Mary University of London iGGi PG Researcher Peyman Hosseini is a PhD candidate working on Agentic AI and building on efficient solutions with small language models and post-training of large and small language models to enable these LLMs to be powerful on-device assistants. He has interned for the last 12 months at Samsung Research in the UK where he has led 3 paper sumbissions and 2 patent submissions on post training foundation models with reinforcement learning algorithms as well as building efficient on-device memory agents. A description of Peyman's research: Peyman's Rsearch targets post-training of foundation models, specifically large language models, to deliver personalized and powerful AI-powered solutions that are deployable on edge devices, such as mobile phones and personal computers. This is specifically important as Large Language Models (LLMs) are powerful yet impossible to deploy on edge-devices to their computational requirements. On the other hand, Small Language Models (SLMs), i.e., language models between 2-32B params, are more efficient but yet unable to handle complex tasks. Fine-tuning these models to work well in complicated setting enables a lot powerful, privacy-preserving AI-powered applicatios, such as personalized on-device recommendation systems and agents capable of memorizing users' habits and interests. Email s.hosseini@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Ignacio Castro Prof. Matthew Purver Featured Publication(s): DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation CG-TTRL: Context-Guided Test-Time Reinforcement Learning for On-Device Large Language Models Cost-Effective Attention Mechanisms for Low Resource Settings: Necessity & Sufficiency of Linear Transformations Efficient solutions for an intriguing failure of llms: Long context window does not mean LLMs can analyze long sequences flawlessly Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks You Need to Pay Better Attention: Rethinking the Mathematics of Attention Mechanism GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks Lon-ea at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction GRACER: Improving the Accuracy of RACER Classifier Using A Greedy Approach Themes Game AI Player Research - Previous Next