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< Back Pilar Zhang Qiu Queen Mary University of London iGGi Alum Pilar is a researcher with a background in Design Engineering. She has a keen interest in user experience and interaction, wearables and the use of cyber-physical systems in the medical field. Her PhD centres around the creation of play assessments for neuromotor conditions in children with cerebral palsy. This gravitates around the idea that better and more objective clinical data can be obtained through gamification of common assessments. Please note: Updating of profile text in progress Email Mastodon https://www.pilarzhangqiu.com/ Other links Website https://www.linkedin.com/in/pilar-zhang-qiu/ LinkedIn BlueSky https://github.com/pili-zhangqiu Github Themes Applied Games - Previous Next

< Back Prof. Peter Cowling Queen Mary University of London iGGi Director Supervisor Peter Cowling has led teams that have won £45 million for research into games and digital creativity. After decades of experience in novel models and algorithms for AI decision-making, his research is now targeted on finding and promoting promising research directions in AI, games and digital creative technology, to benefit people and wider society. Playful ideas, curiosity and games have a central role! As Principal Investigator, he led the teams which won the grants for IGGI (2014 and 2019) and Digital Creativity Labs (2015). He is a member of the Programme Advisory Board which informs strategy in the Digital Economy area of UK research council funding. He has sat on several research council grant funding prioritisation panels, chairing two. He has presented ideas for the use of games as a tool to influence and understand the human condition at a number of venues, including TEDx and 10 Downing Street. He has published over 100 papers, winning 2 best paper awards at AIIDE. His research technology has over 5 million installs in commercial games – he was invited to talk at GDC about that. He would be interested to supervise students whose research uses games as a tool to gather opinion or promote understanding: to identify research directions and harness the future potential of games, creativity and AI to benefit people and society. He is particularly interested in how games and other curious, creative things can help us to understand a world of complex interacting agents, each living a world created by their own thought (!). Research themes: Research visions for games and AI Game design/development to influence, inform and understand people and society Game AI peter.cowling@qmul.ac.uk Email Mastodon https://www.petercowling.com/ Other links Website https://uk.linkedin.com/in/peter-cowling-3590962 LinkedIn BlueSky Github Themes Applied Games Design & Development 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 https://www.youtube.com/watch?v=3yVD-7mRlk4 Previous Next

< Back Sahar Mirhadi University of York iGGi PG Researcher Available for post-PhD position Sahar Mirhadi is a final-year PhD researcher investigating how video games support during challenging times. Her contributions have been published in the Proceedings of the ACM Conference on Human-Computer Interaction, and she has presented at Devcom on transforming the complexity of turn-based games into a strategic advantage. She is also a passionate Magic: The Gathering player, collaborating with competitive Magic team Worldly Counsel to convert tournament insights into a deeper understanding of player motivations and team dynamics. Sahar is also a Safe In Our World Ambassador, a recipient of the Magic: The Gathering New Perspectives Grant for Marginalised Players, and a member of the Birds of Paradise collective. A description of Sahar's research: Sahar's PhD research project investigates the specific aspects of games that facilitate coping for players during difficult life experiences. Building on earlier work that mapped broad links between game aspects and coping strategies, Sahar’s first study showed that games can support a variety of coping strategies, including emotion-focused, avoidance, and meaning-focused coping. However, questions remained about how these effects occur across different gaming contexts. To address this, her second study employed in-depth interviews and a grounded theory approach with players of Disco Elysium, Darkest Dungeon and Stardew Valley. The findings led to the development of the Games as Dynamic Coping Systems theory, which posits that specific aspects of video games scaffold a diverse range of coping strategies for players facing personal difficulties. The model highlights the dynamic interplay between what the player brings (e.g., prior experiences, needs, skills) and what the game provides (such as Narrative, Game Environment and Character Interactions). Through this interaction, players develop coping strategies, and the outcomes from coping feed back into their ongoing gaming and life experiences. While the grounded theory offered a deeper understanding of how specific game aspects support various coping strategies, it also revealed a gap: the temporal dynamics of coping. Sahar’s ongoing work aims to explore how players transition between coping strategies over time and what factors shape these transitions. Her overall aim is to provide a deeper understanding of specific aspects within games that support coping, shedding light on the potential benefits and limitations of video games during times of difficulty. sm2904@york.ac.uk Email https://linktr.ee/saharmirhadi Mastodon Other links Website https://www.linkedin.com/in/saharmirhadi/ LinkedIn https://bsky.app/profile/saharmirhadi.bsky.social BlueSky Github Supervisors: Dr Alena Denisova Dr Jo Iacovides Themes Player Research https://www.youtube.com/watch?v=0nTTCR25O0Y 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 Mastodon Other links Website LinkedIn BlueSky Github Themes Player Research - Previous Next

< Back Filip Sroka Queen Mary University of London iGGi PG Researcher Filip is a Computer Science researcher specialising in Game AI. He acquired an Integrated Masters in Computer Science from Queen Mary University of London and is pursuing a PhD in Game AI with iGGi. With a passion for algorithms and problem-solving, he constantly seeks new challenges to enhance his skills. As an avid LEGO collector and investor, he brings a unique blend of technical and creative abilities. He is excited about the potential of the Metaverse and is driven by the role of technology in shaping its future. His research explores the integration of Dynamic Difficulty Adjustment (DDA) into VR rhythm games such as Beat Saber, with the goal of enhancing player skill development and motivation through the application of learning theories. By addressing difficulty spikes, the project creates personalised learning experiences using human-made maps designed to accelerate the learning process. Key components include player evaluation, map segmentation, and procedural generation. The broader aim is to extend these findings to other rhythm games, offering benefits to players, game developers, and the health and fitness industry. f.sroka@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/filip-sroka-134954197/ LinkedIn BlueSky https://github.com/FilipSroka Github Supervisor: Dr Laurissa Tokarchuk Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Dr Sokol Murturi Goldsmiths iGGi Alum AI for game design: learning from designers For my PhD I am investigating how AI can help developers by learning to generate content in a similar fashion to the developers themselves. I envision a framework based on reinforcement learning, where an AI can learn a design policy for some content domain (e.g., FPS maps or platformer levels) by observing human designers. The AI would learn to take particular design actions in certain kinds of content states. Recent research into reinforcement learning has shown it is a powerful framework for developing complex agent behaviours and I believe there is a lot of potential to apply this work to game design. How would a human and artificial designer interact? Assume that an AI has learned to design a specific kind of content, such as a house, by observing human designers at work. A human designer could then partially develop some new content, and ask the AI to suggest some variations on it (see figure below), with both AI and human iterating on the design in a mixed-initiative interaction. The AI could learn from feedback from both the human designer and playtesting. As human feedback may not produce enough data for effective learning, the AI could perhaps extend this with data from simulated playtests. Game design decisions are often made with an expectation of how the player will react, and I could also look at how player models could be incorporated into the AI designer. In a reinforcement learning approach, the state could represent content+player, and the AI could learn to take design actions aimed a specific types of player. Developers could use this framework to develop content targeted at an individual player's style. Moreover, if the AI has learned something about how the human designer creates content, it can then be used live during the game to modify game elements in response to player interaction. Developers could set up modular levels, giving the AI the ability to adapt certain areas with content generated specifically to match the player. smurt001@gold.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

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

< Back Lizzie Vialls University of York iGGi Alum Discrete Models and Algorithms to create a more satisfying and strategic opponents For many 4x and Grand Strategy computer games (e.g. Civilisation, Europa Universalis), the player will be playing against one or more AI opponents. For many games, the AI is not clever enough to stand up to a player without being given the ability to "cheat" - ability to spawn in resources, see what the player is doing, etc. This creates an unsatisfactory opponent for a player, as it gives them opponents that fight through "cheating" over strategy or out-manoeuvring the player. The aim for my PhD is to look into the potential uses of SAT and similar to create a more satisfying and strategic opponent for players to play against in these styles of computer games. To this end, I’ll be identifying potential for improvement regarding my proposal, and once I’ve narrowed down the specifics - be it related to improving how SAT solvers can handle problems, or how better to encode AI into SAT - I will be working on ways to improve AI for turn based strategic games. Lizzie Vialls is a recent Computer Science graduate of University of Leicester, having graduated with a 2:1 and a prize for best third year project, which was the project that fueled her interest in SAT. When not searching for an errant semicolon in her code she can be found working with various online gaming communities, hunched over many a tabletop game, or attempting to make friends with the local feline populace. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Charles Ringer University of York iGGi Alum Charlie Ringer is a researcher interested in applied Machine Learning with a focus on the ways in which we can use Deep Learning to model various facets of video games streams (e.g. stream highlights, emotional moments, in-game events, various streamer behaviours etc.). As such, his work spans many Machine Learning fields, such as Computer Vision, Affect Computing, and Natural Language Processing. His research has three motivating factors. Firstly, the challenge of how to fuse multi-view stream data (e.g. audio, web-cam footage, game footage, chat) into a single model, especially when considering the challenges of ‘in-the-wild’ data. Secondly, the untapped and bountiful data source that livestreaming represents, especially regarding the way in which streamers play games and interact with their audience. Thirdly, the exciting and emerging field of self-supervised learning which has the potential to utilise this abundance of livestream data. Charlie initially worked in the video games industry working mainly on the Magic: The Gathering - Duels of the Planeswalkers series of games before studying a BSc in Computer Science at Goldsmiths, University of London. After his BSc he joined IGGI, firstly at Goldsmiths and then at York. He was recognised as a finalist for the Twitch Research Fellowship 2019 for his research on livestream data. charles.ringer@york.ac.uk Email Mastodon https://www.charlieringer.com Other links Website https://www.linkedin.com/in/charlie-ringer/ LinkedIn BlueSky https://www.github.com/charlieringer Github Featured Publication(s): Machine Learning with Applications From Theory to Behaviour: Towards a General Model of Engagement Modelling early user-game interactions for joint estimation of survival time and churn probability Time to die 2: Improved in-game death prediction in dota 2 Autohighlight: Highlight Detection in League of Legends Esports Broadcasts via Crowd-Sourced Data Multi-Modal Livestream Highlight Detection from Audio, Visual, and Language Data Twitchchat: A dataset for exploring livestream chat Multimodal joint emotion and game context recognition in league of legends livestreams Streaming Behaviour: Livestreaming as a Paradigm for Analysis of Emotional and Social Signals Deep unsupervised multi-view detection of video game stream highlights Streaming behaviour: Live streaming as a paradigm for multi-view analysis of emotional and social signals Rolling Horizon Co-evolution in Two-player General Video Game Playing Themes Esports Game AI Game Data - Previous Next

< Back Tom Wells University of York iGGi PG Researcher Available for placement Tom has an interest in niche alternative and indie games which evoke strong emotions and are narratively immersive. He studied Experimental Psychology as an undergraduate in Oxford, specialising in conscious brightness perception in specific optical pigments. His Masters was in Computational Neuroscience, Cognition and AI from Nottingham, and focused on Computer Vision (specifically facial recognition) and Visual Attention. He enjoys heavy metal, strength sports and literature. A description of Tom's research: With the rise of digital art, Uncanny Valley has emerged from an esoteric robotics concept into an infectious memetic phenomenon, with specific memes such as 'Uncanny/Canny Mr. Incredible', or more generally uncanny faces being used as reaction images for humor. Critics and players will now refer to specific media being 'Uncanny' rather than using more general words as 'off-putting', demonstrating uncanniness cementing itself in the public consciousness as examples increasingly abound; ergo digital artists should be aware of evoking the uncanny even with modern rendering technology, as audiences become increasingly discerning of the Uncanny. This is most pertinent in videogames, where rendering is performed in real-time, meaning rendering constraints must be implemented. This potentially confines characters to the Uncanny Valley, as it may not be possible to increase graphical fidelity, thus artists may be left to either accept the uncanny or demaster their work (both undesirable options). This project aims to learn about the Uncanny Valley pertaining to modern skin rendering techniques, using artificial intelligence (specifically GANs) to directly map skin rendering parameters onto user assessments of uncanniness and realism. This can then be reverse engineered to provide automated tools for generatively rendering realistic non-uncanny skin, and predicting audience responses to skin realism, expediting QA testing. The primary experimental stage is to generate a face database with photorealistic skin to be assessed using psychometrics by participants. This is additionally one of few studies looking into the novel phenomena of training AI's to generate human-oriented psychologically salient content. tw1700@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Prof. Mark Sandler Queen Mary University of London Supervisor Queen Mary University of London, Centre for Digital Music, School of Electronic Engineering and Computer Science. Mark Sandler has been doing research in audio and music – with a little computer vision as a side line – for over 40 years. He founded the world-leading Centre for Digital Music and has been its Director since 2003 (with a 4 year gap 2010-14). The Centre is now one of the largest such research groupings in the world, with around 80 PhDs, PDRAs and academics. In his early career he invented the Digital Power Amplifier, researched Drum Analysis and Synthesis for Simmons Electronics Ltd, moving into Fractal and Chaos analysis and synthesis, Ambisonic modelling and Fine Grain Audio Compression before becoming one of the pioneers of Music Information Retrieval around 2000. He is a Fellow of the Royal Academy of Engineering, of the IEEE, the IET and the AES. He is also a Fellow of the Alan Turing Institute. He has supervised over 40 PhD candidates successfully through their studies and is currently involved in four other CDTs at QM - in AI and Music (Co-Investigator, Impact, in Data Centric Engineering (Co-Director, Partnerships), in Data-centric Engineering (Director) and Media & Arts Technology (of which he was founding Director 2009-16). Research interests are: Digital Signal Processing, Digital Audio, Digital Music Technology, Music Informatics, Semantic Audio, Music Data Science, Semantic Music Metadata, Auditory User Interaction, Immersive Audio. He is particularly interested in supervising students with a background in Acoustics, Signal Processing, Audio, Machine/Deep Learning in: Virtual acoustics for games Games engines for virtual and augmented reality music experiences Research themes: Games Engines for non-gaming interactive experiences Game Audio and Music mark.sandler@qmul.ac.uk Email Mastodon https://www.linkedin.com/in/mark-sandler-a689b4/ Other links Website LinkedIn BlueSky Github Themes Game Audio - Previous Next