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< 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. athanasios dot kokkinakis *at* z)!gmail*com Email https://www.researchgate.net/profile/Athanasios-Kokkinakis Mastodon Other links Website https://www.linkedin.com/in/athanasios-kokkinakis-8b79101a4 LinkedIn BlueSky Github 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 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 m.f.aichmueller@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/michael-aichmueller/ LinkedIn BlueSky https://github.com/maichmueller Github Supervisor(s): Prof. Simon Lucas Dr Raluca Gaina Themes Applied Games Game AI - Previous Next

< Back Ross Fifield University of York iGGi PG Researcher Available for placement I am a user-centred games designer and researcher with a background in both practical and theoretical dimensions of play. I hold a BA and MA in Games Design from Falmouth University and have recently been engaged in teaching further and higher education courses in games development. My work sits at the intersection of design innovation, player psychology, and emerging technology, with a particular focus on how people find, engage with, and sustain play in social contexts. Currently undertaking a PhD as part of the iGGi programme, my research investigates the social and psychological factors that influence whether and how individuals choose to play with others. I aim to develop actionable insights that reduce barriers to engagement, support better player matchmaking, and encourage more inclusive and sustainable multiplayer experiences. I am particularly interested in live data applications and their potential to inform adaptive matchmaking systems and enhance game discovery. My practice draws from speculative and disruptive design methodologies, with a commitment to developing future-proof solutions that benefit academic, educational, and commercial communities alike. I maintain professional interests in affective psychology and digital heritage. As a player, I take an agnostic approach to genre, though I have a particular affinity for First Person Shooters, MMOs, sandbox games, and live-action roleplay. I am seeking placement opportunities with studios and organisations that are open to collaboration on live, data-driven projects focused on social play, player engagement, matchmaking and game discovery. My goal is to contribute meaningfully to real-world game development while refining methodologies that support more empathetic, inclusive, and dynamic player experiences. ross.fifield@york.ac.uk Email https://bsky.app/profile/rossfifield.bsky.social Mastodon http://www.rossfifield.com Other links Website https://www.linkedin.com/in/rossfifield/ LinkedIn https://bsky.app/profile/rossfifield.bsky.social BlueSky Github Supervisors: Dr Joe Cutting Prof. Paul Cairns Themes Player Research Previous Next

< Back Dr Nuria Peña Pérez Queen Mary University of London iGGi Alum Nuria got her bachelor’s in biomedical engineering in Spain before moving to London. After studying an MSc in Neurotechnology and working in robotic neurorehabilitation at Imperial College London, she discovered the enormous potential of serious games in the field of human-robot interaction. She joined IGGI in 2018. Her PhD research involves studying human motor control and learning during bimanual tasks to investigate how the dynamics of the interaction can serve to develop better training systems. This is done through the development of interactive gaming environments that are compatible with rehabilitation robotic devices. The modelling of the recorded human neuromuscular data allows to explore how to better help patients to restore their motor function. Her work is a collaboration between the Advanced Robotics group at Queen Mary University of London and the Human Robotics group at Imperial College London. As part of her PhD she has worked for the company GripAble, developing games for the assessment and training of hand function (February 2020-August-2020). n.penaperez@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor(s): Dr Ildar Farkhatdinov Featured Publication(s): Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks Dissociating haptic feedback from physical assistance does not improve motor performance Bimanual interaction in virtually and mechanically coupled tasks The impact of stiffness in bimanual versus dyadic interactions requiring force exchange How virtual and mechanical coupling impact bimanual tracking Lateralization of impedance control in dynamic versus static bimanual tasks Is a robot needed to modify human effort in bimanual tracking? Exploring user motor behaviour in bimanual interactive video games Quartz Crystal Resonator for Real-Time Characterization of Nanoscale Phenomena Relevant for Biomedical Applications Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Applied Games - Previous Next

< Back Joshua Kritz Queen Mary University of London iGGi PG Researcher Available for placement Graduated in Applied Mathematics in computer science, however my love for games pushed me to dedicate myself for studying them. This led me to brave many areas of knowledge, such as: psychology, design, education, production and entrepreneurship. My work as a teacher allowed me develop many of these skills in practice, besides invoking a new perspective about the world. On a personal level, I love new experiences that can teach me new knowledge and, most important, I am very open minded and easy to talk to! I believe discussion leads to enlightenment. A description of Joshua's research: Card games, in particular Trading Card Games (TCGs) thrive on using the synergy between the cards to create emergent and interesting gameplay. However, these games usually have hundreds of different cards to create such rich experience, with some older TCGs featuring thousands of different cards. With such a huge amount of different cards playtesting these games present a big challenge. In example a new set of Magic the Gathering takes over 3 years of development to be fully designed. But even considering simpler exemplars like Dominion or Assencion can be difficult to balance, and both games are known to need a few expansions of experience to indeed provide a well balanced experience. One way to make this task faster and easier is to use automated agents to playtest the game exhaustively and provide much needed data. Whilst this would assist card game development, it is not used in practice, the playtesting of card games is still completely done by players. Even with systematic playtesting there is a limit of how much of the possibilities humans can test. However, implementing playtesting of card games have two big challenges, which are the main reason it has not been implemented in practice yet. First: Automated agents are not great when playing a game with too many variables (different cards) Second: The possible combinations of cards used in a deck or set of a single game is huge. My research aim to address the second issue by using a theory of synergy between cards to reduce the search space necessary to properly evaluate a card game. j.s.kritz@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/joshua-kritz-38808379/ LinkedIn BlueSky Github Supervisor: Dr Raluca Gaina Featured Publication(s): A FAIR catalog of ontology-driven conceptual models A Conceptual Model for the Analysis of Investigation Elements in Games A Vocabulary of Board Game Dynamics Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis When 1+ 1 does not equal 2: Synergy in games Towards an Ontology of Wargame Design Themes Applied Games Design & Development Game AI Previous Next

< Back Susanne Binder Queen Mary University of London iGGi Manager iGGi Admin iGGi Manager @ QMUL ; alongside David Hull (iGGi Manager @ UoY) , and supported by Shopna Begum , Helen Tilbrook and Oliver Roughton, she's mostly in charge of making things run at iGGi with particular focus on iGGi-QMUL-specific admin iGGi-QMUL-specific student concerns PR, website and social media industry liaison s.binder@qmul.ac.uk Email https://dizl.de/@sus4nn3b1nd3r/ Mastodon Other links Website https://www.linkedin.com/in/susanne-binder-b1184647/ LinkedIn https://bsky.app/profile/susannebinder.bsky.social BlueSky Github Themes - Previous Next

< Back Dr William Smith University of York Supervisor William Smith is a Reader in the Computer Vision and Pattern Recognition research group in the Department of Computer Science at the University of York. He is currently a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow and an Associate Editor of the journal Pattern Recognition. His research interests span vision, graphics and ML. Specifically, physics-based and 3D computer vision, shape and appearance modelling and the application of statistics and machine learning to these areas. The application areas in which he most commonly works are face/body analysis and synthesis, surveying and mapping, object capture and inverse rendering. A wide variety of tools and areas of maths are often useful in his research such as: convex optimisation, nonlinear optimisation, manifold learning, learning/optimisation on manifolds, computational geometry and low level computer vision (e.g. features and correspondence). He leads a team of five PhD students and one postdoc and has published over 100 papers, many in the top conferences and journals in the field. He was General Chair for the ACM SIGGRAPH European Conference on Visual Media Production in 2019 and is Program Chair for the British Machine Vision Conference in 2020. Research themes: Game AI Game Design Computational Creativity Graphics and rendering Content creation william.smith@york.ac.uk Email Mastodon https://www-users.cs.york.ac.uk/wsmith/ Other links Website https://www.linkedin.com/in/william-smith-b5421a70/ LinkedIn BlueSky https://github.com/waps101 Github Themes Creative Computing Design & Development Game AI Player Research - Previous Next

< Back Janet Gibbs Goldsmiths iGGi Alum Janet is exploring how multi-modal perceptual feedback contributes to a player's sense of presence in the virtual world. Jaron Lanier described Virtual Reality (VR) as the substitution of the interface between a person and their physical environment with an interface to a simulated environment. This interface is of particular significance in understanding how presence depends on the nature, extent and veridicality of our sensorimotor interaction with the virtual environment, and how that relates to our normal engagement with the real world. In practice, only selected parts of the interface are substituted - we are never fully removed from our physical environment. Our perceptual apparatus evolved to make sense of changing sensations in multiple modalities originating naturally and coherently from the same event or percept. By contrast, in VR, individually crafted feedback using different technologies for each modality are coordinated to appear as if from a single source. VR benefits from a long history of visual and audio technologies, developed in harness for virtual experiences from cinema to computer games. Haptics is a relative newcomer that must be blended with them to create coherent multimodal perceptual experiences. Additionally, haptics is closely related to proprioception, and to the wide range of tactile senses—texture, heat, pain etc—that current VR systems do not address. Building on sensorimotor theory of perception, Janet aims to establish how our perceptual system responds to multi-modal feedback that almost, but not quite, matches what we are used to, in making sense of the simulated environment of VR. JGIBB016@gold.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Investigating Sensorimotor Contingencies in the Enactive Interface A comparison of the effects of haptic and visual feedback on presence in virtual reality Novel Player Experience with Sensory Substitution and Augmentation Investigating sensorimotor contingencies in the enactive interface Themes - Previous Next

< Back Dr Lorenzo Jamone Queen Mary University of London Supervisor I am a Lecturer in Robotics and Director of the CRISP group (Cognitive Robotics and Intelligent Systems for the People) at the School of Electronic Engineering and Computer Science (EECS) of the Queen Mary University of London (QMUL). The CRISP group is part of ARQ (Advanced Robotics at Queen Mary). Since October 2018, I have been a Turing Fellow at The Alan Turing Institute. I am interested in understanding human (and animal) intelligence, by using computational techniques that include computer simulations and real robots. My research topics include: human creativity and creative problem solving, human perception, human-human non-verbal communication, object affordances, tool use, body schema, eye-hand coordination, dexterous manipulation and object exploration, human-robot interaction and collaboration, tactile and force sensing. I am interested in supervising students with an engineering, computer science or behavioural sciences background on the following topics: Creating computational models of human creativity Creating computational models of decisional agents l.jamone@qmul.ac.uk Email Mastodon https://lorejam.wixsite.com/crisp Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing - 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. n.m.john-mcdougall@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/vethan4/ LinkedIn BlueSky Github Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Themes Design & Development Game AI - Previous Next

< 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 oliver@gamba.dev Email Mastodon https://www.ojscholten.com Other links Website https://www.linkedin.com/in/ojscholten LinkedIn BlueSky https://github.com/ojscholten Github 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 Dr Daniel Hernandez University of York iGGi Alum With the games industry as his target, Daniel Hernandez’s main research objective is to design and implement algorithms that, without any prior knowledge, generate strong gameplaying agents for a wide variety of games. To tackle this “from scratch” learning, he uses, and contributes to, the fields of Multiagent Reinforcement Learning, Game Theory and Deep learning. Self-play is the main object of study in his research. Self-play is a training scheme for multiagent systems in which AIs are trained by acting on an environment against themselves or previous versions of themselves. Such training scheme bypasses obstacles faced by many other training approaches which rely on existing datasets of expert moves or human / AI agents to train against. Daniel’s hope is that further development in Self-play will allow game studios of all sizes to generate strong AI agents for their games in an affordable manner. A storyteller by nature, Daniel has a strong track record of outreach through talks and workshops both in the UK and internationally. By sharing his journey, insights and discoveries he hopes to both inspire and instruct students, researchers and developers to realise the potential that Reinforcement Learning has to improve the games industry. His passionate work on Machine learning goes beyond crafting strong gameplaying agents. He sees the potential of using AI to simplify and automate a wide range of tasks in the games industry. He has led successful projects which used machine learning aimed at automating multiagent game balancing to alleviate the burden of manual game balancing. Daniel received an MEng in Computing: Games, Vision & Interaction from Imperial College London. Wanting to combine the power of AI and the creativity of videogames, Daniel began a PhD journey to explore the misty lands of Multi Agent Reinforcement Learning (MARL). Please note: Updating of profile text in progress Email Mastodon https://danielhp95.github.io Other links Website https://www.linkedin.com/in/dani-hernandez-perez-1106b2107 LinkedIn BlueSky https://github.com/Danielhp95 Github Featured Publication(s): A comparison of self-play algorithms under a generalized framework A generalized framework for self-play training Metagame Autobalancing for Competitive Multiplayer Games Themes Game AI Player Research - Previous Next