< Back Cameron Johnston Queen Mary University of London iGGi PG Researcher Available for placement Cam holds an MPhys in Theoretical Physics from the University of Edinburgh, wherein he combined infectious disease models with fluid dynamics to examine the validity of wastewater-based epidemiology as a method of passively monitoring the prevalence of COVID-19 in an urban population. Cam joined iGGi in 2023 to explore the relationship between video games and physics. A description of Cam's research: The virtual worlds of video games naturally invite a chance to play with the laws of physics. While games regularly tweak their physics, the goal is often to create a simulation of a world that feels realistic and familiar to players. This research seeks to do the opposite: to create virtual worlds that accurately simulate laws of physics unlike our own. Email cameron.johnston@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Josh Reiss Dr Andrew James Wood Themes Creative Computing Design & Development - Previous Next

< Back Toby Best Queen Mary University of London iGGi PG Researcher Available for post-PhD position Toby has always held video games as an integral part of his livelihood, ever since catching his first Pokémon on the Game Boy Color. The ever-developing evolution of technology, from the humble NES and R.O.B. preventing the video game market crash in 1983, to the Wii’s motion controls, to augmented and virtual reality today, has been a key inspiration, and one of the reasons why he studied Mathematical Computation at University College London. He also has a keen interest in tabletop roleplaying games, such as Dungeons & Dragons and Pathfinder. His research interests involve the potential of combining roleplaying games' collective storytelling and interactive narrative with the power of artificial intelligence and deep learning. A description of Toby's research: Artificial Intelligence is the field of creating digital agents capable of decision-making and rational thought to fulfil a core goal or aspect. For tabletop and video games, an implemented AI would attempt to ‘solve’ the game by finding optimal winning strategies. However, tabletop role-playing games (TTRPGs) are driven by the power of collective storytelling and interactive narrative, as opposed to set rules, and therefore have a more open-ended goal - maximising player enjoyment for all participants. This involves a Game Master (GM) player as both narrator and referee, controlling the non-playable characters (NPCs) and the campaign behind the screen, whereas players usually control one player character (PC) each to interact with the world. There is no ‘failure’ state compared to traditional games, as campaigns can continue until players lose interest or the narrative is ‘complete’; even all PCs dying (known as a total party kill) can drive the narrative in a new direction. This project aims to study and piece together the different elements that would go into a Game Master AI, building on current state-of-the-art game-playing AI, such as Director AIs in games such as Valve’s ‘Left 4 Dead’, and studying the implications of such developments for players and game designers alike. For example, whether it could replicate the playing experiences of a human GM as a replacement, or enhance the experience by working with a human GM. Email t.j.best@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Alena Denisova Dr Raluca Gaina Prof. Simon Lucas Featured Publication(s): "Journeys in the Dark"-Towards Game Master AI in Complex Board Games Why Choose You?-Exploring Attitudes Towards Starter Pokémon Themes Design & Development Game AI Player Research https://www.youtube.com/watch?v=sYRhQ-w5yPA Previous Next

< Back Steph Carter University of York iGGi PG Researcher Available for post-PhD position Steph is an interdisciplinary researcher whose work spans psycholinguistics, HCI and game design. Their research investigates how game design can be used improve the participant experience in experimental tasks for second language acquisition research. They are also interested in research on accessibility in games, disability representation, games with a purpose, and have published work examining how AI/ML can be used to support professional roles in the creative industries. Outside of academia, Steph enjoys taking part in game jams, making pixel art and playing with their cats. Steph’s project explores how game design can improve participants’ experiences in Second Language Acquisition experiments while still enabling the collection of high quality, controlled data. Although games and gamification are increasingly used in educational contexts, particularly for language learning, their potential in experimental design for SLA research remains underexplored. This project investigates how incorporating game elements into psycholinguistic tasks can impact the participant experience and maintain data quality. The findings aim to inform the design and development of gamified data collection tools for both language learning and cognitive research, while also providing some practical guidance for researchers interested in gamifying their experimental methods. Email steph.carter@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Abi Evans Featured Publication(s): Beyond the Spotlight: Co-Designing AI for Theatre Audience Communication Themes Accessibility Applied Games Design & Development Game Data Player Research - Previous Next

< Back Dr Daniel Berio Goldsmiths iGGi Alum AutoGraff: A Procedural Model of Graffiti Form. (Industry placement at Media Molecule) The purpose of this study is to investigate techniques for the procedural and interactive generation of synthetic instances of graffiti art. Considering graffiti as a special case of the calligraphic tradition, I propose a "movement centric" alternative to traditional curve generation techniques, in which a curve is defined through a physiologically plausible simulation of a (human) movement underlying its production rather than by an explicit definition of its geometry. In my thesis, I consider both single traces left by a brush (in a series of strokes) and the extension to 2D shapes (representing deformed letters in a large variety of artistic styles). I demonstrate how this approach is useful in a number of settings including computer aided design (CAD), procedural content generation for virtual environments in games and movies, computer animation as well as for the smooth control of robotic drawing devices. Daniel Berio is a researcher and artist from Florence, Italy. Since a young age Daniel was actively involved in the international graffiti art scene. In parallel he developed a professional career initially as a graphic designer and later as a graphics programmer in video games, multimedia and audio-visual software. In 2013 he obtained a Master degree from the Royal Academy of Art in The Hague (Netherlands), where he developed drawing machines and installations materializing graffiti-inspired procedural forms. Today Daniel is continuing his research in the procedural generation of graffiti within the IGGI (Intelligent Games and Game Intelligence) PhD program at Goldsmiths, University of London. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Word-as-image for semantic typography Optimality Principles in the Procedural Generation of Graffiti Style SURFACE: Xbox Controlled Hot-wire Foam Cutter The role of image characteristics and embodiment in the evaluation of graffiti Emergence in the Expressive Machine The CyberAnthill: A Computational Sculpture Sketch-Based Modeling of Parametric Shapes Artistic Sketching for Expressive Coding Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks AutoGraff: Towards a computational understanding of graffiti writing and related art forms Kinematics reconstruction of static calligraphic traces from curvilinear shape features Interactive generation of calligraphic trajectories from Gaussian mixtures Sketching and Layering Graffiti Primitives. Kinematic Reconstruction of Calligraphic Traces from Shape Features Expressive curve editing with the sigma lognormal model Dynamic graffiti stylisation with stochastic optimal control Computer aided design of handwriting trajectories with the kinematic theory of rapid human movements Generating calligraphic trajectories with model predictive control Learning dynamic graffiti strokes with a compliant robot Computational models for the analysis and synthesis of graffiti tag strokes Towards human-robot gesture recognition using point-based medialness Transhuman Expression Human-Machine Interaction as a Neutral Base for a New Artistic and Creative Practice Themes Game AI - Previous Next

< Back Nicole Levermore University of York iGGi PG Researcher Available for placement Nicole's academic background is within Neuroscience, having achieved BSc Neuroscience and Psychology, MSc Translational Neuroscience and an MPhil in Auditory Neuroscience. Outside of her research interests, she enjoys playing video games, hiking and playing the cello. A description of Nicole's research: Video games have enormous potential for research on cognition and mental health. In my project, I will use video games to perform basic research into a common psychiatric disorder (ADHD), paving the way for improved diagnosis, monitoring and therapy. ADHD is typically diagnosed in childhood and is characterised by failures of attentional state maintenance. This project involves using cutting-edge neuroimaging techniques to investigate how subjects with and without ADHD switch between attentional states (for example, ‘engagement’ and ‘flow’) while playing a cognitively engaging video game. The ultimate goal is to use video games to understand how mental health impacts people’s ability to focus on cognitively demanding tasks and, potentially, to develop therapeutic intervention. Email nicole.levermore@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Prof. Alex Wade Themes Accessibility Design & Development Immersive Technology Player Research https://www.youtube.com/watch?v=gRFe1EOPW_4 Previous Next

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

< Back Nicole Levermore University of York Alumni Liaison Nicole, who is currently also an iGGi PhD Researcher (see her PGR profile here ), was hired by iGGi in December 2025 as an additional Administrative Assistant. The role's key aim is to grow and support the iGGi Alumni Network, where past iGGi Students can keep up to date with what each other is doing, stay in contact, collaborate etc. If you are an iGGi Alum and want to be a part of an iGGi Alumni Network, Nicole would love to hear from you. Over the forthcoming months, she will reach out to those who have expressed interest or who will probably be interested, and she will share details about the envisaged concept. Please feel free to contact Nicole about anything iGGi Alumni Network related, as suggestions are always welcome! Email nicole.levermore@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Accessibility Design & Development Immersive Technology Player Research Previous Next

< Back Dr Valerio Bonometti University of York iGGi Alum Game analytics and player psychology: creating reliable models of player motivation Motivation can be loosely defined as a process of the brain and the mind, capable of driving and deeply shaping human behaviour. Motivational processes are embedded in many everyday life situations, exerting their effects via a wide range of incentive mechanisms and objects. Understanding this process in a videogame context, however, requires a more holistic approach considering not just the incentive properties of the game but also the player personal characteristics. My project aims to create reliable cross-games models of player motivation taking into account the contribution of natural inter individual variability. This will be accomplished linking in-game behavioural data and psychological models via a hybrid approach, where findings from small scale experimental studies (hypothesis-driven) will guide the realization of large scale (data-driven) applications for predicting players' characteristics, future behaviour and motivation evolution. Being able to model player motivation and predict the trajectories of its evolution could possibly lead to personalized and dynamic engagement strategies able to adapt accordingly to the player characteristics and in-game behaviour. Achieving a similar goal would be of pivotal importance in industrial and gamification contexts. I obtained my bachelor degree in Psychological Science and my master degree in Clinical Psychology at Padova University (Italy). During my academic path I acquired knowledge in general psychology, cognitive psychology, psychophysiology, neuroscience and research methodology. After my master degree I spent a considerable amount of time as a research trainee, both abroad and in my country, always investigating the reward process and its effects in various contexts. During this period I worked on various projects across different fields ranging from psychophysiology, player research and game analytics. In my free time I enjoy practicing indoor climbing and travelling, I like figurative art in general and more specifically I’m a huge cinema and graphic-novel enthusiast. Supervisors: Prof. Anders Drachen, Dr Sam Devlin Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): From Theory to Behaviour: Towards a General Model of Engagement Modelling early user-game interactions for joint estimation of survival time and churn probability Predicting skill learning in a large, longitudinal MOBA dataset Mind the gap: Distributed practice enhances performance in a MOBA game Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data: A Representation Learning Approach Based on Artificial Neural Networks Themes Player Research - Previous Next

< Back Dr Maximilian Croissant University of York iGGi Alum I’m a psychology researcher, writer and game designer, exploring our emotional connection with games and creating games with purpose. Coming from a B.Sc. and M.Sc. in psychology and neuroscience, I’m now at the intersection of emotion research, design, and human-computer interaction and try to build design-oriented solutions for adapting game content to affective data. My project will include theoretical groundwork, investigating the emotional relationship between player and games and from there build an affective fear-focused VR horror game with specific and practical solutions in terms of emotion measurement, modelling, and adaptation. The ultimate goal is to help fill knowledge gaps that currently hold us back on making commercially viable affective games and provide tools to design games for a deep emotional impact. I’m also the Co-Founder of Vanilla Noir, a small studio working on applied games that aim to promote well-being and satisfying user experiences. For me, games are a great tool to explore psychological phenomena through interactions and the design and development of games based on applied psychology has great potential to help make the world a bit of a better place. Email mc2230@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s) Dr Cade McCall Featured Publication(s): Tired and Wired: Sleep Deprivation Prevents Affective Renormalisation During Exposure to Ambiguous Threat Advancing Methodological Approaches in Affect-Adaptive Video Game Design: Empirical Validation of Emotion-Driven Gameplay Modification Using Virtual Reality to Investigate the Influence of Sleep Deprivation on In-the-Moment Arousal During Exposure to Prolonged Threats Affective Systems: Progressing Emotional Human-Computer Interactivity with Adaptive and Intelligent Game Systems An appraisal-based chain-of-emotion architecture for affective language model game agents Emotion Design for Video Games: A Framework for Affective Interactivity Theories, methodologies, and effects of affect-adaptive games: A systematic review A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic Endocannabinoid concentrations in hair and mental health of unaccompanied refugee minors Progress in Adaptive Web Surveys: Comparing Three Standard Strategies and Selecting the Best Odd and disorganized–comparing the factor structure of the three major schizotypy inventories Themes Design & Development Player Research - Previous Next

< Back Dr Timea Farkas Goldsmiths iGGi Alum Timea is a researcher striving to understand how people engage with technologies—broadly defined—in their everyday lives, and how new technologies can enhance people's experiences of play, creative expression, and beyond. She has always been drawn to learning new things, with a background ranging from creative arts through games to science, which allows her to apply an interdisciplinary outlook towards research. She holds an MA in Sonic Arts from the University of Sheffield and has graduated with a First Class (Hons) degree in music composition and technology with a special award for outstanding achievement and collaboration. A description of Timea's research: This research project centres around understanding board game players' relationship with the immersive capabilities of hybrid board games - board games with a digital component - through finding novel interactions which strengthen the sensory elements of tabletop games. By focusing on physical board game pieces as alternative input devices to touch screens, the goal is to explore the design space of analogue-digital hybrids with a player-centric approach. Email farkasmarimba@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Exploring the Design Space of Analogue-Digital Hybrid Boardgames Using a Player-Centric Approach How Boardgame Players Imagine Interacting With Technology The Effects of a Soundtrack on Board Game Player Experience A Grounded Analysis of Player-Described Board Game Immersion Themes Creative Computing Immersive Technology Player Research - Previous Next

< Back Dr Myat Aung University of York iGGi Alum Immersion is a state in which players are engaged to a degree of total absorption that inhibits the ability to correctly report one’s surroundings or time. Present theory on immersion has developed a coherent model that provides sufficient evidence to distinguish itself from other cognitive concepts such as presence, attention, selective attention, absorption and flow. However, immersion research thus far has been hindered by difficulties with taking in-vivo measurements of cognition and physiological responses during videogame play. This presents an ideal opportunity for implementations of neuroimaging methods to carry out such real time measurements of attention, as well as other cognitive processes and their roles in videogame immersion. Using various combinations of neural and physiological methods such as skin conductance, eye tracking, electroencephalography and even functional magnetic resonance imaging, it is now possible to obtain richer data in immersion research. The goal of this project is to apply such methods in order to better define and measure videogame immersion, identify the cognitive processes and hierarchical models that are involved in immersion and ultimately contribute to the literature in videogame immersion. Though neuroimaging is limited by statistical sensitivity, challenging experimental logistics and non-ideal lab environments, they are still presently the best tools available to obtain fine-grain data of attention and the many other cognitive components of immersion. Such knowledge would contribute significantly to a better understanding of effective development of videogames, as well as educational tools. I am an MPsych Psychology graduate from the University of York, having studied Psychology, Cognitive Neuroscience & Neuroimaging for four years. My Master’s research was primarily in vision, attempting to manipulate and record parahippocampal responses to visual stimuli selected parametrically by computer algorithms. During my degree I also spent much of my time researching videogames, studying the literature on the effects of videogame play on sleep, and working with a IGGI PhD student as a lab assistant. Between my degree and my PhD, I have also been working as a data analyst at Digital Creativity Labs researching skill learning in large gaming populations from Riot Games’ League of Legends. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Different rules for binocular combination of luminance flicker in cortical and subcortical pathways Investigating the non-disruptive measurement of immersive player experience The trails of just cause 2: spatio-temporal player profiling in open-world games Predicting skill learning in a large, longitudinal MOBA dataset Themes Game AI - Previous Next

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