< Back Alex Fletcher Queen Mary University of London iGGi Alum Alex Fletcher is a freelance audio engineer and junior game developer working on understanding the perceived flow and player experiences in mobile rhythm games and how a dynamic difficulty adjustment system would improve these experiences. The function of EEG and other biosensors as an additional measurement of player experience is of particular interest as further research in its use as an adaptive system. Other areas of research interest include game-based learning and games with a purpose. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game Audio Player Research - Previous Next

< Back Dr Søren Riis Queen Mary University of London Supervisor Søren Riis has more than 15 years of experience in teaching computability, complexity and the art of creating fast efficient algorithms. He has a strong interest in reinforcement learning and generative adversarial networks (GANs) related to strategy games. Riis has been actively involved in computer chess, and is listed on the wiki of influential people in chess programming https://www.chessprogramming.org/ Søren Riis is a strong player of strategy games including Chess, Shogi, Go and Bridge at an internal level. He has worked as a consultant for an AI company and is involved in applying deep learning for the card game of bridge. For the last 5 years he has been working on technical projects related to machine learning and reinforcement learning. He has practical experience and interest in scientific computing on super computers, and in creating C and C++ libraries to run from within python. Søren Riis is particularly interested in supervising students with a strong technical and/or maths background. Aptitude for strategy games with an interest in one the following ares is an advantage. Games requiring inductive reasoning combined with exploration. Hidden identity games (Werewolf, Resistance/Avalon, Mafia etc) Using GANs to sample realistic scenarios during gameplay Deep Reinforcement Learning in multi-agent strategy games Building and analysing games for investigating evolution of communication. Research themes: Game AI Game Design Game Creativity Games and mathematics Email s.riis@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game AI Game Data - Previous Next

< Back Michael Aichmüller Queen Mary University of London iGGi Alum My background lies in physics and statistical mathematics with a later specialization in optimization in the fields of Reinforcement Learning (RL) and Causal Inference. My first encounters with RL occurred during my Masters when studying how to create strong policies in perfect information games using algorithms, such as MinMax, MCTS, DQN, and later AlphaZero variants. My favorite game application remains the board game ‘Stratego’. In the meantime I investigated the estimation of causal parents influencing a target variable from interventional datasets for my Master’s thesis. Specifically, how well Deep Learning estimations could replace exponentially scaling graph search methods with approximations requiring only polynomial runtime. A description of Michael's research: My research focuses on the state-of-the-art in game-playing solutions for imperfect information games (think games like Poker, Stratego, Liar’s Dice etc.). I am particularly interested in the application of No-Regret (and related) methods which seek to learn those actions that provided the most benefit (or least regret) compared to the benefit all possible actions provided on average. These methods learn such via iterative play to find a Nash-Equilibrium (NE), a game-theoretic concept comparable to an optimal policy known from Single-Agent RL, but for all partaking players at once. Particularly, variants of Counterfactual Regret Minimization (CFR) remain the state-of-the-art algorithms for computing NEs in 2-player zero-sum games due to their success in tabular form so far. Yet, prohibitive complexity and memory scaling bars them from large-scale applications. Hence, research of recent years seeks to couple CFR (and other No-Regret methods) with function approximation, such as Deep Learning, to scale up the size of applicable games with already notable successes (Deepstack, Libratus, Pluribus, DeepNash). My research seeks to contribute to this endeavour by first analyzing the specifics of established methods and finding ways to introduce Hierarchical RL concepts to No-Regret learning. Please note: Updating of profile text in progress Email m.f.aichmueller@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Simon Lucas Dr Raluca Gaina Themes Applied Games 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 Dino Ratcliffe Queen Mary University of London iGGi Alum Teaching AI agents transferable skills for game playing My research focuses on the ability of an AI agent to be able to evaluate the various skills it would need to master a game, such as in an FPS (first person shooter) like doom. If the agent can learn to cluster actions that may split into strategies such as attacking enemies, gathering ammo/health and avoiding enemy fire this information could then be used in similar games. This information would also provide a base for being to evaluate players on a skill level, giving a much more granular view of their strengths and weaknesses in any of these games. This could then be used for better matchmaking in team games, placing players into teams whose skill sets complement each other. Other applications include being able to guide the player into situations that give them more experience in the areas they are weakest. Dino started a MSci in computer science at the University of Essex in 2011. During the next 4 years, he focused on modules that involved improving technical skills and Artificial Intelligence. He was the winner of the K.F Bowden Memorial prize in two separate years. Dino worked at the London startup Signal Media during the summer of 2014 and continued to work for them part time during my masters year. He graduated with a 1st class degree. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Cross-lingual style transfer with conditional prior VAE and style loss Author's declaration Win or learn fast proximal policy optimisation Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games Clyde: A deep reinforcement learning doom playing agent Themes Game AI - Previous Next

< Back Dr Cade McCall University of York Supervisor Cade McCall is an experimental psychologist. He uses games and virtual environments to study emotion, cognition, and behaviour during threatening experiences. His work explores how threat unfolds over time as revealed by dynamics in motion tracking data, psychophysiological measures, and experience-sampling. McCall is interested in supervising projects with a psychological focus, including: ● human interactions with autonomous systems ● the use of games to manipulate emotions ● social interactions within games Research themes: Games with a purpose Player experience Game analytics Email cade.mccall@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game Data Player Research - Previous Next

< Back Dr Guifen Chen Queen Mary University of London Supervisor Dr Guifen Chen is a Lecturer in Neurobiology at QMUL. Her work focuses on the neuronal basis of multisensory integration, spatial cognition and memory. Her lab uses state-of-the-art techniques such as immersive virtual reality and in vivo electrophysiological/probe recording in mice. Her research is currently supported by funding from BBRSC and the Royal Society. Dr Chen completed her undergraduate studies in both biology and computer science at East China Normal University in China. She then pursued PhD in neuroscience, conducting research at both East China Normal University and Boston University in the USA. Following that, she undertook postdoctoral research at University College London in the UK. Her work has been published in high-impact journals such as Nature Communications, eLife, and Current biology. Email guifen.chen@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Design & Development Immersive Technology Player Research - Previous Next

< Back Tamsin Isaac University of York iGGi PG Researcher Tamsin has been a lifelong gamer ever since receiving her first Game Boy and has long been fascinated by how people engage with games emotionally, socially, and behaviourally. She joined the iGGi CDT in 2023 after completing a BSc and MSc in Psychology at the University of Plymouth, where she developed a growing interest in player motivation, disengagement, and live-service game design. Her PhD research focuses on limited-time events (LTEs) in digital games—temporary content designed to encourage engagement and re-engagement in live-service games. Through this work, she explores how LTEs shape player behaviour, routine, anticipation, disengagement, and return play across platforms and genres. Tamsin’s research combines large-scale content analysis with qualitative diary-and-interview methods to investigate both the structure and lived experience of LTEs. She is currently developing a cross-platform taxonomy of LTEs using data from over 2,600 Steam and Google Play games, alongside player-focused research exploring how individuals decide whether events are “worth” participating in during everyday play. Her work aims to support more ethical, sustainable, and player-friendly approaches to live-service game design by helping researchers and developers better understand how event structures influence player experience and long-term engagement. She is open to collaboration opportunities with game studios interested in live-service systems, player behaviour, engagement design, and event analysis using player data or design insights. When not researching or analysing games, Tamsin enjoys baking, reading, playing cosy indie games, and quietly grinding dailies. Email tamsin.isaac@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Prof. Paul Cairns Themes Applied Games Design & Development Player Research https://www.youtube.com/watch?v=n32ngtGYNQ8 Previous Next

< Back Dr Alan Pedrassoli Chitayat University of York iGGi Alum Available for post-PhD position Alan is a researcher that focuses on audience experience within esport broadcast. His Machine Learning background allows him to extract complex patterns from game and game related data in order to derive meaningful insights that can be utilised in broadcast. Having worked in the esport industry, both as a software engineer as well as researcher, Alan has experience with both technical and research problems. His research aims to explore the factors that improve the audience experience within esports. This is catered to esport broadcast of all levels, from highly produced professional tournaments to regular streams by content creators and it could be in the form of: Measuring and representing different forms of audience engagement. Exploring the different ways to visualise and utilise Machine Learning to enhance and integrate existing broadcast pipelines. Investigating how community-led narratives can be generated through data. Email alan.pchitayat@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr James Walker Prof. Anders Drachen Featured Publication(s): AI vs. the Algorithm: Measuring success on Twitch Applying machine learning to enhance esport broadcast narratives Beyond the Spotlight: Co-Designing AI for Theatre Audience Communication How Could They Win? An Exploration of Win Condition for Esports Narratives Applying and Visualising Complex Models in Esport Broadcast Coverage From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports What are you looking at? Team fight prediction through player camera Echo Suite of Software (Showcase Brochure) 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 Themes Design & Development Esports Game Data - Previous Next

< Back Dr Dan Franks University of York Supervisor Dr Franks is an interdisciplinary researcher and data scientist interested in AI and machine learning. He is experienced in developing and applying evolutionary computation and machine learning methods to understanding behaviour. He is an internationally recognized leader in interdisciplinary research, has published in top journals such as Science and PNAS. Some of his papers are in the top 1% of all papers for media coverage (altmetric), and his work is regularly covered by The New Scientist, National Geographic, Wired, The BBC, The Guardian, The Times, among others. As Reader in the York Centre for Cross-disciplinary Systems Analysis, Dan works on applying AI, machine learning, and agent-based modelling, to problems in other disciplines. Particular interests involve the development of machine learning methods for creating intelligent AI and for understanding complex systems. Research themes: Game AI Game Analytics Email daniel.franks@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Game AI Game Data - Previous Next

< Back Dr Fiona McNab University of York Supervisor During a postdoc at the Karolinksa Institute in Stockholm, Fiona investigated working memory and attention, providing empirical support for a role for the basal ganglia in the control of access to working memory and identification of changes in the dopamine system related to working memory training. At The Wellcome Trust Centre for Neuroimaging, UCL, with a Wellcome Trust Career Development Fellowship, she designed the working memory game in the large-scale smartphone study; “The Great Brain Experiment ”, leading to studies of different types of distraction in younger adults as well as in healthy ageing. In 2013 she moved to Birmingham University, where she conducted fMRI and behavioural studies of attention and working memory, and behavioural studies of the effects of competition on working memory. Fiona is now a lecturer in the Department of Psychology at the University of York. She is using fMRI and behavioural studies to investigate what limits working memory, how different types of distractors are successfully ignored and how working memory changes through development, with healthy aging, as well as in certain patient groups. Part of her work uses data from a new set of working memory games, which are currently available to play (York Memory Games, YORMEGA ). She is particularly interested in supervising students on the following topics: Understanding the limitations of working memory and the role of attention using games Understanding age-related changes in cognition using games, Cognitive training using games. Research themes: Game Design Games with a Purpose Player Experience Gamification Games for Cognition Research Games for Cognitive Training Email fiona.mcnab@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games - 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