< 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 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 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 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 Prof. Matthew Purver Queen Mary University of London Supervisor Matthew Purver is Professor of Computational Linguistics, and leader of QMUL’s Computational Linguistics Laboratory. His research has covered many aspects of natural language processing (NLP), with a £4m grant portfolio including projects on fundamental techniques like cross-lingual processing and incremental language understanding, and applications to news media, social media analysis and mental health diagnosis. His work has been covered by the Guardian, Telegraph, Independent, LA Times, NBC and Scientific American, among others. He is also a senior researcher at the Jožef Stefan Institute, Slovenia, and in 2011 he co-founded the company Chatterbox Labs Ltd. He is interested in supervising students with a background in NLP, linguistics or machine learning and an interest in analysis or generation of natural language. Research themes: Language in Games Game AI Computational Creativity Email m.purver@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game AI - Previous Next

< Back Stefan Stoican University of Essex iGGi Alum Understanding human crowd behaviour via virtual environments: feedback loop between games & research This project uses computer game experiments to explore decision-making in a virtual evacuation simulation. Can one be “saved by the gaze”? Currently, Stefan is investigating how innate social cognition components such as gaze-cuing might inform one’s egress. Do “Us versus Them” scenarios occur? He is also testing how one’s feelings of social identification with the surrounding crowd might modulate one’s risk-taking. Does hoarding prevent herding? Lastly, the project is looking at how cultural differences might affect egress time, when one insists to save personal possessions. More broadly, Stefan’s research concentrates on two key open questions in human crowd behavioural research. Firstly, how do social groups (that the player observes or is a member of) within the simulated crowd of agents affect both individual decision-making and the emergent behaviour of the crowd? Secondly, both empirical and virtual experiments of human crowds have not fully explored the effect of agent or player interactions with underlying landscape features (e.g. layout, signage, debris, large objects and other obstacles, etc). The outcomes of the experimental studies using real human participants will subsequently be used to develop more realistic decision-making and behavioural response algorithms and hence improve the behaviour of simulated agents in follow-on computer games. Stefan’s academic background may lie in Mathematics and Psychology, but his interdisciplinary mindset has constantly pushed him towards games and Computer Science. For his final Mathematics project, he designed an Android app that gamified teaching statistics. As part of his Psychology Masters degree, he investigated the potential benefits of MOBA games such as League of Legends with regard to visual attention. Currently, his extracurricular projects aim to explore video games’ effects on coping with trauma and on one’s perception of vulnerable groups, via commemorative gaming name choices or via in-game refugee storylines, respectively. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Game AI - Previous Next

< Back Dr Lina Gega University of York Supervisor Qualified both as a nurse and a psychological therapist, Lina is a senior member of the Mental Health and Addictions Research Group (MHARG) at the University of York, where she leads research under the Digital Mental Health Theme. She has published widely on computer-based therapies and virtual environments. Lina's work on technology-mediated interventions and training formed an impact case study was submitted to 2014 Research Excellence Framework as part of Psychology, Psychiatry and Neuroscience. Lina’s current work focuses on interventions to improve health and quality of life for children and young people with mental health problems. She has led the development and evaluation of a purposeful game to treat phobias in children, and of an innovative virtual environments system to assist psychological therapy and skills training. She co-leads the digital theme for the Closing the Gap (CTG) Network, funded by UK Research and Innovation (UKRI). The Network’s digital theme explores how technologies, including gaming, can be used to improve the physical health of people with severe mental illness, especially schizophrenia and bipolar affective disorder. An experienced University teacher, supervisor and examiner, Lina welcomes students with a design, engineering or behavioural sciences background who are interested in applied games research in the field of mental health, with a focus on: development and ‘proof-of-concept’ studies of purposeful games to improve mental health outcomes and social communication skills in children and young people. adaptation and evaluation of gamified applications to improve physical health outcomes with people whose motivation and information processing are affected by severe mental illness. Research themes: Game Design Games with a Purpose Player Experience Gamified Mental Health Interventions Email lina.gega@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Player Research - Previous Next

< Back Andrei Iacob University of Essex iGGi Alum Identifying Immersion in games using EEG and other measures (Industry placement at Sony SIE) The project aims to identify markers for immersion in player’s EEG signals. A few steps towards it include designing an experiment that reduces data noise and helps identify time frames for immersion during gameplay, recording EEG data among other “tests” to improve the accuracy of the state localization on a timeline. This research could prove useful for the games industry in a few ways: - it can provide tools for game testing (e.g. which parts of the game are immersive, which parts lack in that aspect) – thus making it easier to improve the game experience across the board; - it could also be used in making real-time adjustments to games (increase / decrease difficulty levels, pace, etc. to enhance the player’s immersion). Although the EEG data is the main focus of the project, it is not the only one. Correlations will be analyzed between different tests and in-game behaviors that should render even more information regarding the player’s state and mindset during gameplay. This information will be just as valuable and perhaps more readily available for widespread use in the near future. Andrei is a keen programmer and gamer. He graduated with a BSc (Hons) in Computer Science from the University of Essex. Andrei’s research interests are in the field of brain- computer interfaces and computer games. His hobbies include programming, gaming, guitar and skiing. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Player Research - Previous Next