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< Back Dr Oliver Scholten University of York iGGi Alum Oliver Scholten is working on understanding the use of cryptocurrency technologies for gambling and gaming. His work provides researchers with the tools and context needed to understand player behaviours in these technologically advanced domains. He is the creator of gamba - a python library designed to enable quick replication of existing player behaviour tracking studies. He has also published several peer reviewed articles, and had written evidence published by the UK House of Lords which describes the mechanics behind decentralised gambling applications. As a PhD student, his thesis focuses on decoding and analysing cryptocurrency gambling and cryptocurrency gaming transactions. These transactions offer a more granular insight for researchers into both gambling and gaming than has been historically possible, this work therefore lays the foundations for explorations across different schools of research, and more specifically, advanced player transaction analytics. Please note: Updating of profile text in progress Email oliver@gamba.dev Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): On the Evaluation of Procedural Level Generation Systems On the Behavioural Profiling of Gamblers Using Cryptocurrency Transaction Data Inside the decentralised casino: A longitudinal study of actual cryptocurrency gambling transactions Decentralised Gambling Overview Decentralised Gambling: The York Combined Transaction Set Unconventional Exchange: Methods for Statistical Analysis of Virtual Goods Utilising VIPER for Parameter Space Exploration in Agent Based Wealth Distribution Models Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities Themes Game Data - 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. Email r.sasso@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Paulo Rauber Featured Publication(s): Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark 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 Emily Marriott University of Essex iGGi Alum Automated Story Generation for Games Emily is researching automated story generation for video games, focusing on the use of Planning for real-time, dynamic generation. Ideally, the stories created will reflect choices made by the player during gameplay and will update continually throughout gameplay. The aim of this research is to create a system that could be easily utilised in the development of more adaptive games. This could improve player enjoyment, increase re-playability, and allow for the inclusion or exclusion of content that may only appeal to niche audiences. Emily’s current focus is on investigating story structures and pacing to create a template for generating good stories specifically for games that are consistent, well-structured and interesting. This involves studying the pacing requirements in existing games to establish what these are and how they differ the requirements for film and TV. The system will ideally be integrated with existing game-development tools and provide an easy-to-use interface to make the creation of adaptive games easier and quicker. The eventual goal is a full story-generation system would support both the creation of quests that emerge from story requirements and a game world that fits the environment required for the story. Emily graduated from Glyndŵr University with a BSc in Computer Games Development before completing an MSc in Computer Science at Oxford Brookes University. The substance of the MSc dissertation involved generating dungeon levels and quests using grammars based on the play style the player appeared to favour. Emily enjoys playing both tabletop and computer roleplaying games, especially ones in which player actions can have a dramatic effect on the game’s progression. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Player Research - Previous Next

< Back Dr Carlos Gonzalez Diaz University of York iGGi Alum Carlos is finishing his PhD at the University of York. He holds an MSc in Serious Games at the University of Skövde (Sweden) and a BSc in Software Engineering (Spain). He is been closely connected with industry throughout his PhD, having worked in the last years for Microsoft Research, Sony Interactive Entertainment R&D, Musemio Ltd R&D and Goldsmiths, UoL; as well as done consulting for tech companies such as Unity Technologies. A description of Carlos' research: The purpose of my PhD research is to advance game technologies by democratising the use of ML techniques among non-experts through innovative tools and plugins for game engines. I developed ML specific visual scritping languages and used mixed-methods research approaches to understand how to better support developers in creating VR interactions and the challenges behind human-AI interaction. I had several technical jobs throughout my PhD, as my expertise is highly applicable in both industry and academia. Thanks to the broad range of expertise that I gathered through many years of industrial work and academic study, I can tackle the challenges emerging from the inter-disciplinary nature of modern work: where user psychology, immersive technology and artificial intelligence intersect. Please refer to my website for completely up-to-date information regarding publications. Feel free to reach out if you want more information or want to chat about my/your work. I am looking for positions starting on February 2023 onwards. Email carlos.gonzalezdiaz@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Sebastian Deterding Featured Publication(s): Prototyping by Moving for Virtual Reality Embodied, in-medium design of VR game motion controls using interactive supervised learning Automatic Game Tuning for Strategic Diversity Programming by Moving: Interactive Machine Learning for Embodied Interaction Design InteractML: Node Based Tool to Empower Artists and Dancers in using Interactive Machine Learning for Designing Movement Interaction Movement interaction design for immersive media using interactive machine learning Using Machine Learning to Design Movement Interaction in Virtual Reality Interactive machine learning for more expressive game interactions Making Space for Social Time: Supporting Conversational Transitions Before, During, and After Video Meetings InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media Interactive Machine Learning for Embodied Interaction Design: A tool and methodology Bodystorming in SocialVR to Support Collaborative Embodied Ideation Themes Creative Computing Design & Development Game AI Immersive Technology https://www.youtube.com/watch?v=taVry9IQUjE https://www.youtube.com/watch?v=kkKU3MyBspM https://www.youtube.com/watch?v=WHiPav2l5gA Previous Next

< Back Dr Athanasios Vasileios Kokkinakis University of York iGGi Alum Videogame Correlates of Real-Life Cognitive Traits Video-games have been increasingly gaining momentum and popularity, both with the public but also with the scientific world who has seen their usefulness in multiple areas. Researchers have been making bold claims of Videogames increasing Intelligence monopolizing the public’s attention and taking it away from what Videogames are excellent at; serving as diagnostic tools examining constructs such Reaction Times, Memory and fluid Intelligence. The sharp decline of the aforementioned concepts has been linked to multiple diseases such as the prodrome of Schizophrenia, Alzheimer’s and Dementia. Moreover, their measurement has been linked to important life outcomes such as Academic Achievement, Time in Unemployment, Unwanted Pregnancies and Mathematical Achievement among others. In my doctoral thesis I have correlated these constructs with the massively played video-game League of Legends. By cross-validating Psychometric measurements with Video-game metrics we can possibly identify at risk populations and stage Health Interventions or even identify “gifted” children or children that lag behind at an early age and place them in appropriate training curricula. He acquired his BSc in Psychology from the University of Bangor and he then went to complete his MSc in Cognitive Neuroscience at the University of York. In his first experiment he attempted to see whether “expert video-gamers” would show less Attentional Resources when compared to a control group of non-gamers and whether a short training session of approximately a week had any effects on the non-gamer group. His MSc, although not related to gaming, gave him valuable experience with EEG and MEG which he hopes to incorporate into his future experiments. In his most recent experiments he correlated psychometric Intelligence with Videogame Scores, more specifically League of Legends Tiers. He believes that these scores may give us insight on multiple developmental trajectories for instance healthy aging. Email athanasios dot kokkinakis *at* z)!gmail*com Website LinkedIn Mastodon BlueSky GitHub Other Link Prof. Alex Wade Prof. Peter Cowling Featured Publication(s): Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports Videogame Correlates of Real Life Traits and Characteristics. Exploring the relationship between video game expertise and fluid intelligence Temporal and spatial localization of prediction-error signals in the visual brain What's in a name? Ages and names predict the valence of social interactions in a massive online game MEG adaptation resolves the spatiotemporal characteristics of face-sensitive brain responses Predicting skill learning in a large, longitudinal MOBA dataset Automatic Generation of Text for Match Recaps using Esport Caster Commentaries WARDS: Modelling the Worth of Vision in MOBA's DAX: Data-Driven Audience Experiences in Esports Time to die 2: Improved in-game death prediction in dota 2 Themes Esports Game AI Player Research Research Gate Google Scholar Previous Next

< Back Prof. Damian Murphy University of York Supervisor Damian Murphy is Professor in Sound and Music Computing at the Department of Electronic Engineering AudioLab, University of York, where he has been a member of academic staff since 2000, and is the University Research Theme Champion for Creativity. He started his career in the Performing Arts Department at Harrogate College and has previously held positions at Leeds Metropolitan University and Bretton Hall College. His research focuses on virtual acoustics and he has published over 130 journal articles, conference papers and books in the area. He is a member of the Audio Engineering Society, a Fellow of the Higher Education Academy, and a visiting lecturer to the Department of Speech, Music and Hearing at KTH, Stockholm. Prof. Murphy is also an active sound artist and the Director of the £15m XRStories Creative Industries R&D Partnership exploring interactive and immersive storytelling for the UK’s creative and cultural sectors. He is interested in supervising students with interests in sound design, acoustics and audio signal processing and with a particular focus on: Interactive and immersive audio environments for real-time systems Room acoustics simulation and auralisation Assessment of immersive audio content for gameplay and competitive advantage Interactive/immersive audio storytelling Acoustic scene classification using spatial and spectral feature Audio for immersive environments. Research themes: Game AI Game Audio and Music Games with a Purpose Player Experience Email damian.murphy@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game Audio Immersive Technology - Previous Next

< Back Dr Tom Cole iGGi Alum + Supervisor Games should be studied as interactive systems, but are more often studied using techniques reserved for non-interactive media. As developers, we are ‘selling ourselves short’, and not exploring the creative and expressive potential of digital games to their fullest. Out of the myriad of affective experiences possible, we generally only design and experience a fraction of what could be offered. Tom hopes to help address this by studying how game mechanics, gameplay systems and control methods can be used and interpreted to create meaning and elicit a wider range of emotional responses than is commonly seen in digital games at present. Broadening and deepening emotional engagement with an emphasis on mechanics and systems. (Industry placement at Bossa Studios) Video games, with their unique properties such as interactivity, agency, control mechanics, feedback loops and gameplay systems, have the potential to impart deep emotional experiences – some already do of course. However, study of this emotional engagement remains lacking. Reliance on techniques and theory appropriated from film, literature and cultural studies yields limited results. There is relatively little understanding of how procedural elements such as control mechanisms and gameplay systems can be leveraged (or synergised with narrative and/or audio-visual elements) for emotional affect. Tom was previously at Supermassive Games where he was a designer on the BAFTA award-winning horror game Until Dawn and artist on Killzone Shadow Fall. Tom got his BSc in Biology with Industrial Experience from Manchester. After teaching science in secondary schools for a while, he decided games were more interesting and got his MA in Digital Games Theory and Design at Brunel. After time at Goldsmiths, University of London and the University for Creative Arts, Rochester, Tom is now Lecturer in Games Development at the University of Greenwich where he teaches games development, design and production. From 2016 to 2024 he led the organisation of Adventurex - the Narrative Games Convention, a sold out international conference which grew from 100 to 650 people during his time leading it. Email tom@tommakesgames.com Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): The History of Nintendo Emotional exploration and the eudaimonic gameplay experience: A grounded theory More than a bit of coding:(un-) Grounded (non-) Theory in HCI Eudaimonia in Digital Games Thinking and doing: Challenge, agency, and the eudaimonic experience in video games "Moments to Talk About": Designing for the Eudaimonic Gameplay Experience Grounded Theory in games research: making the case and exploring the options Emotional and functional challenge in core and avant-garde games The Tragedy of Betrayal: How the design of Ico and Shadow of the Colossus elicits emotion Themes Design & Development Game AI https://www.youtube.com/playlist?feature=share&list=PL_17c-ELEJ5334QRqxhRLnnoX8aNdpHL- - https://www.youtube.com/watch?v=8pe5FfHTk-4 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 Themes Design & Development Game AI - Previous Next

< Back Dr Jen Beeston University of York iGGi Alum + Supervisor Jen is currently working as a Lecturer in HCI in the Department of Computer Science (University of York) whilst writing up her PhD. She has a multidisciplinary background, from studying subjects such as environmental science and media production and having worked in various jobs such as grassland research, flood risk management, and plasterboard quality. She feels extraordinarily fortunate to have been able to do research into her lifelong hobby of playing digital games. In particular, Jen feels it is important that everyone has the opportunity to enjoy gaming should they wish. As such, her research has been aimed at exploring the experiences of people with disabilities in playing games, beyond how various technologies can support play. Jen’s research is focused particularly on the social experiences of players with disabilities in-game and within the broader gaming community. She has worked alongside the charity AbleGamers with the aims of investigating these player’s experiences of gaming, what effects alternative controls have upon play, and what it’s like for these players in multiplayer or online games. Jen is broadly interested in HCI, user experience, player experience, inclusivity, social play, and game communities. Outside of her work, she enjoys walking, thinking, reading, crochet, art, and tabletop roleplaying games. Email jen.beeston@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Validation and Prioritization of Design Options for Accessible Player Experiences Social experiences of people with disabilities in playing (in) accessible digital games Enabled players: The value of accessible digital games Accessible player experiences (APX): The players Characteristics and motivations of players with disabilities in digital games Perceptions of Telepresence Robot Form Themes Accessibility - Previous Next

< Back Dr Oliver Withington Queen Mary University of London iGGi Alum Available for post-PhD position Oliver Withington is a AI and games researcher working on novel methods for evaluating content generation systems for games. Following a successful career in the healthcare technology industry he decided to combine his life long love of games and interest in AI research into a PhD with the iGGi CDT in 2020. He lives in London with his wife and two young daughters, and when he is not writing about, thinking about, or talking about games you can probably find him in either his local bouldering gym, or in the park either pursuing or being pursued by two small children. A description of Oliver's research: Oliver's primary motivation is to make the evaluation of novel content generators more standardised, robust and straightforward for both researchers and game designers. Currently his focus is on techniques for producing informative visualisations of the output spaces of content generators. His work has been published at many of the leading conferences in his field, and he has also taken his work and ideas to the game industry, most recently in the form of a talk at GDC 2025's AI Summit. Email owithington@hotmail.co.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Featured Publication(s): Designer Difficulties: Visualizing the Possibility Spaces of Dynamic Difficulty Adjustment Systems Exploring the Possibility Space of 1 Billion Spells Exploring Minecraft Settlement Generators with Generative Shift Analysis HarmonyMapper: Generating Emotionally Divers Chord Progressions for Games. The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods Visualising Generative Spaces Using Convolutional Neural Network Embeddings Compressing and Comparing the Generative Spaces of Procedural Content Generators Illuminating Super Mario Bros: quality-diversity within platformer level generation Themes Creative Computing Design & Development Game AI https://www.youtube.com/watch?v=4m1gYriq_pc Previous Next

< Back Andrew Martin Queen Mary University of London iGGi Alum Applications in game development for programming language theory and AI Modern game development is highly iterative. Iteration is usually discussed in terms of a team completing design iterations, but can also be considered at the level of an individual developer attempting to complete a task, or experimenting with some ideas. At this level, the feedback loop provided by the tool becomes critical. Programming environments in particular often have a very poor feedback loop. Programming feedback can be thought of in terms of how quickly and seamlessly the user is able to observe the results of their work. This process is usually plagued with manual tasks and long pauses. It is common that a user will need to recompile, relaunch their program, and then manually recreate whatever state is required to observe the behaviour that they are working on. Frameworks like Elm, React and Vuejs are establishing a new norm of automatic hot-reloading with state preservation. These systems represent a branch of programming language research that is strongly focused on developer experience. In order to improve upon this work for game development, we must overcome the unique challenges that game development entails. Although the systems mentioned are all quite recent, there is a rich vein of research to draw on, which can be traced through dataflow programming, Smalltalk, Erlang, functional-reactive programming, Lisp and more. Predictive completions are considered by many to be a natural next-step in the evolution of live programming environments. An AI programming assistant would propose program fragments as completions or alternatives. The agent may seek to anticipate the user’s intent, or to provide creative suggestions. There is much relevant research in the fields of program synthesis, inductive logic programming, machine learning and genetic programming. One significant problem is how to smoothly and safely integrate a system like this into the user’s workflow. Many of the properties useful for safely enabling live programming features, such as isolation of side-effects, will also permit an AI agent to safely generate and execute code. Andy graduated from Imperial College London with an MEng in Computing in 2011. Following this he worked on game engine tools and technology at a startup called Fen Research, and then as a senior developer at a software consulting firm called LShift. In 2016 he spent six months working as a Research Associate in the Computational Creativity group at Goldsmiths, before starting his PhD. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Game AI - Previous Next

< Back Dr Yizhao Jin Queen Mary University of London iGGi Alum Currently a student at Queen Mary University of London (QMUL), I have delved deep into the realms of artificial intelligence and game design. With a passion for understanding the complexities behind real-time strategy (RTS) games and their dynamic, unpredictable nature, I have committed myself to contribute novel insights to this domain. Research: My primary research area is Hierarchical Reinforcement Learning (HRL) for Real-Time Strategy (RTS) games. RTS games, known for their intricate mechanics and vast decision spaces, present a formidable challenge for traditional AI approaches. By employing HRL, I aim to develop agents that can not only understand the multi-layered tactics and strategies of these games but also learn to adapt to ever-changing game scenarios efficiently. The main objectives of my research are: Better Generalization: To create agents that can seamlessly transition between different RTS games or various maps within the same game without extensive retraining. This involves understanding common strategic threads across multiple game domains. Efficient Training: RTS games are inherently time-consuming due to their vast decision spaces and prolonged gameplay. My research seeks ways to optimize the training process, ensuring that AI agents can learn faster and with fewer computational resources. Email acw596@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Greg Slabaugh Prof. Simon Lucas Themes Game AI Previous Next