< Back Dr Nick Ballou Queen Mary University of London iGGi Alum Hi there! I’m a psychology and human-computer interaction researcher interested in two main topics: how games affect wellbeing, and how we can reform the research ecosystem to be more trustworthy and efficient (aka “open science” or “metascience”). I’m originally from the US, and have bachelor and master’s degrees in linguistics, a topic that prepared me well for social science research, but whose use is relegated to excitedly sharing language fun facts at this point. In my free time, I play tennis, cook and bake, read—and of course play games (mostly deckbuilders, roguelikes, and AAA RPGs). A description of Nick's research: Psychological need frustration—experiences of feeling controlled and coerced, failure and self-doubt, or loneliness and exclusion—is a promising framework for understanding how players engage with video games. Grounded in self-determination theory, one of the most robust psychological theories, need frustration might help explain how and why players (dis)engage with a game and how gameplay impacts well-being. To realize this aim, however, we’re missing key building blocks: 1) a better grasp on when and why need-frustrating situations arise during play; 2) a questionnaire that can assess how much need frustration people experience in games quantitatively; and 3) studies that combine data on need frustration with carefully tracked behavioral data over time, rather than relying on simple self-reports like “how much time did you spend playing video games last week?” My thesis attempts to address all of these one step at a time and is underpinned by a strong emphasis on open and transparent methods. Results so far are promising—contact me to hear more! Email nick@nickballou.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Sebastian Deterding Dr David Zendle Dr Laurissa Tokarchuk Featured Publication(s): "I just wanted to get it over and done with": a grounded theory of psychological need frustration in video games 01 01V A manifesto for more productive psychological games research All BANG, little buck: Need-related experiences are weakly linked with behavior in the video game domain Awareness and Use of Open Research Practices: An International Survey of Researchers Across Disciplines Building collaborative digital behavioural science: A cross-sector agenda from video games research Commentary: Prediction of problem gambling by demographics, gaming behavior and psychological correlates among gacha gamers: A cross-sectional online survey in Chinese young adults From Breakups to Lethargy: Player Accounts of Third Variables Affecting Video Game Playtime and Wellbeing Fundamental Flaws in the Design and Reporting of Chew and Neo (2024) Ill Health and Grief Fuel Gaming, while Lethargy Slows It: Playtime-Wellbeing Confounds from the Player Perspective Open Play: A longitudinal dataset of multi-platform video game digital trace data and psychological measures Perceived value of video games, but not hours played, predicts mental well-being in casual adult Nintendo players Psychological wellbeing, sleep, and video gaming: Analyses of comprehensive digital traces stage 1 programmatic registered report Self-determination theory in HCI: advancing the field Reliving 10 years old: Descriptive Insights into Retro Gaming UKRN Local Network Lead Guidebook Claims for no evidence also need evidence From social media to artificial intelligence: improving research on digital harms in youth The Basic Needs in Games (BANG) Model of Video Game Play and Mental Health (PhD thesis) The Basic Needs in Games (BANG) Model of Video Games and Mental Health: Untangling the Positive and Negative Effects of Games with Better Science The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry Affective Uplift During Video Game Play: A Naturalistic Case Study No evidence that Chinese playtime mandates reduced heavy gaming in one segment of the video games industry A manifesto for more productive psychological games research Four grand challenges for video game effects scholars: How digital trace data can improve the way we study games Perceived value of video games, but not hours played, predicts mental well-being in adult Nintendo players Development of the Brief Open Research Survey (BORS) to measure awareness and uptake of Open Research practices The Basic Needs in Games Scale (BANGS): A new tool for investigating positive and negative video game experiences How does Juicy Game Feedback Motivate? Testing Curiosity, Competence, and Effectance Registered Report Evidence Suggests No Relationship Between Objectively Tracked Video Game Playtime and Well-Being Over 3 Months Themes Game Data Player Research Previous Next

< Back Dr Patrik Huber University of York Supervisor Patrik Huber is a researcher, developer and entrepreneur, working on 3D face reconstruction and face analysis in images and videos using 3D face models. He is a Lecturer (Assistant Professor) in Computer Vision in the Department of Computer Science of the University of York, UK, and he’s the Founder of 4dface.io, a small start-up specialising in 3D face models and realistic 3D face avatars for professional applications. His research is focused on computer vision, in particular, he is interested in the question of how to robustly obtain a metrically accurate, pose-invariant 3D representation of a face from 2D images and videos. He is interested in face tracking, 3D face modelling, analysis and synthesis, metrically accurate 3D face shape reconstruction, inverse rendering, and combining deep learning with 3D face models. Patrik is particularly interested in supervising students with a strong background and interest in computer vision, machine learning, computer graphics, and modern C++/Python, on topics related to creating 3D face avatars of players for immersive playing and social experiences , and using face analytics for professional e-sports . Research themes: 3D face avatars for games AR/VR Serious games and social interaction Immersive 3D player experiences Game Analytics Games with a Purpose E-Sports Email patrik.huber@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Esports Game Data Immersive Technology Player Research - Previous Next

< Back Dr Andrew James Wood University of York Supervisor I am an interdisciplinary researcher at the University of York. My background is in Mathematical Physics but my interests are now in applying computational and mathematical techniques to interesting problems, mostly in Biology. This includes such topics as collective motion (particularly in interaction networks and the role of noise) and microbiology (particularly in metabolism, industrial biotechnology, spatial structure and plasmid dynamics) as well as modelling naval conflicts and glycosylation. I have a natural interest in games and am interested in the interface between games and science, be that in using games to do, or disseminate, science or in utilising mechanisms and insights from research to inspire games. Research themes: Game Analytics Game Design Games with a Purpose Gamification Email jamie.wood@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development Game Data - Previous Next

< Back Dr Florence Smith Nicholls Queen Mary University of London iGGi Alum Florence Smith Nicholls is a game AI PhD researcher based in London. They worked as an archaeologist and heritage consultant for 7 years prior to their doctoral research. Building on their heritage background, they have contributed to the field of archaeogaming through experimenting with archaeological approaches to titles such as Elden Ring and Nier: Automata. They coined the term “generative archaeology games” for their doctoral project on the archaeological recording of procedurally generated content. Their work is motivated by contributing to wider research on games preservation, ethics of game AI and procedural narrative. They are also interested in queer approaches to games, having written about queerness and documenting glitches. They are also a freelance narrative designer, previously a member of the writers' room at the indie studio Die Gute Fabrik. During a placement at the British Library they worked on enhanced curation methods for narrative mobile apps, and were commissioned to produce gameplay footage for the Library's Digital Storytelling exhibition. Email f.c.smithnicholls@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Mike Cook Dr Laurissa Tokarchuk Featured Publication(s): The Dark Souls of Archaeology: Recording Elden Ring “That Darned Sandstorm”: A Study of Procedural Generation through Archaeological Storytelling User-centred collecting for emerging formats Permalife of the Archive: Archaeogaming as Queergaming How To Save A World: The Go-Along Interview as Game Preservation Methodology in Wurm Online Archaeological Gameworld Affordances: A Grounded Theory of How Players Interpret Environmental Storytelling Themes Creative Computing Design & Development Game AI Game Data - 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 Philip Smith Queen Mary University of London iGGi PG Researcher Available for placement I was born and raised in Bermuda, a small island in the Atlantic Ocean with an approximate population of 65,000 people. I finished my undergraduate degree in Computer Science with a Specialist in Game Design at the University of Toronto. For my Master's degree, I studied Computer Games Technology at City, University of London. My goal is to help expand the use of video games from purely recreational activities to viable avenues for aiding in real world problems. A description of Philip's research: My research will be focusing on maximizing player engagement in gamified citizen science as a continuation of my Master's thesis. 'Citizen science' is the practice of employing volunteers from the general public for the collection and/or processing of data with respect to a scientific project. Gamified citizen science projects have relied upon prolonged engagement from volunteers, but the number of long-term participants have been unsatisfactory in current projects. This project attempts to address the lack of sufficient volunteer engagement in gamified citizen science projects. The aim is to build a framework meant to guide game designers in creating an engaging citizen science video game based on the values set by Self-Determination Theory (SDT). These values adhere to the theory’s concept of intrinsic and extrinsic motivators of engagement. Intrinsic motivation relies on the factors of player autonomy, competence, and relatedness during gameplay. Extrinsic motivation relies on external incentives to core gameplay such as in-game rewards. As part of my research, I am evaluating multiple game design frameworks focused on Applied Games and identifying the merits and flaws each have when applied to a citizen science context. The information I gather will formulate a prototype of the Framework that will be iterated upon through design workshops, development, and playtesting. Email p.c.smithii@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development - Previous Next

< Back Dr Marko Tot Queen Mary University of London iGGi Alum Hello! I'm Marko, and welcome to my page! As a part of the IGGI programme and Game AI research group, I'm working on adapting Statistical Forward Planning methods for complex environments. Statistical Forward Planning methods have proven to be effective in some simpler domains and, without requiring any prior learning, they provide a good out of the box AI algorithm. However, while these algorithms shine in certain games, they struggle to perform well in cases where the reward received from the game is sparse. In games where it takes a series of optimal actions to reach the goal, without any significant feedback from the environment in between, their performance drops significantly. My research is centered on solving this problem through automatic sub-goal generation and utilisation of local learned forward models. Creation of the sub-goals could be used to simulate the feedback from the environment and give regular rewards to the agent even in sparse and complex environments. I started my journey in video games when I got my first PC at the age of six, and at that point it was decided that I'm going to make a career out of it. So here I am, ~20 years later, a PhD. student at Queen Mary University of London, trying to make AI agents that can play games, and regularly spending too much time playing games under the excuse that it's all for 'research purpose'. Email m.tot@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): Adapting a world model for trajectory following in a 3d game Bootstrap Your Own Teacher: Online Policy Distillation for Multi-Game Reinforcement Learning Statistical Forward Planning Algorithms World and human action models towards gameplay ideation Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search Making Something Out of Nothing: Monte Carlo Graph Search in Sparse Reward Environments What are you looking at? Team fight prediction through player camera Themes Game AI - Previous Next

< Back Prof. Massimo Poesio Queen Mary University of London Supervisor Massimo Poesio is a cognitive scientist whose primary field is Computational Linguistics / Natural Language Processing. He is interested in the interdisciplinary study of language processing using evidence from computational modelling, corpora, psychological studies, and neuroscience; specific interests include computational models of anaphora resolution (coreference); the study of disagreement on language interpretation through the creation of large corpora containing multiple judgments (an area in which he pioneered the use of games-with-a-purpose with the development of Phrase Detectives, http://www.phrasedetectives.org ); the interpretation of verbal and non-verbal communication in interaction; and the study of conceptual knowledge using a combination of methods from human language technology and neuroscience. He has also been involved in a number of projects applying NLP methods to real life problems, such as detecting deception online, or identifying human rights violations reports in social media. He holds a European Research Council grant on identifying disagreements in language through Games-With-A-Purpose, DALI and is a co-founder of the open access journal Dialogue and Discourse . Using conversational agents in games Applying games to label data for AI Research themes: Game AI Game Design Games with a Purpose Email m.poesio@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game AI - Previous Next