< Back Dr Jozef Kulik University of York iGGi Alum Jozef’s first study has focused on developing a better understanding of the challenges and barriers to making accessible games. This identified a vast array of personal, organisational, and external factors which contribute to the difficulties that developers experience when seeking to make their games more accessible, and also identifies avenues which might be helpful. One key finding in this research was that one of the biggest challenges that developers experience relates to a lack of lived experience with disability, or knowledge of the player experience with disabilities. My most recent research is focused on how to effectively extract that knowledge from players with disabilities, then insert it into a large studio within the UK. This research takes a multi-pronged approach to assisting developers in making more accessible games. First by directly assisting a studio with knowledge about their games, second generating potentially transferable knowledge on accessibility issues and player experience for the rest of the industry, and exploring how research methods such as diary study methodology can be valuable in extracting data from natural play environments with people with disabilities. Email joe.kulik@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Paul Cairns Dr Jen Beeston Featured Publication(s): Understanding how we make accessible games: Perspectives from the games industry and players with disabilities A Qualitative Investigation of Real World Accessible Design Experiences within a Large Scale Commercial Game Development Studio Grounded theory of accessible game development What makes icons appealing? The role of processing fluency in predicting icon appeal in different task contexts Themes Accessibility Player Research - Previous Next

< Back Luke Farrar University of York iGGi Alum Luke Farrar is an iGGi PhD student at The University of York undertaking research in Flexible and Realistic Character Animations in Complex and Dynamic Environments. Luke's research focuses through his bachelor's and master's degrees were on applying machine learning to interesting and unique settings. In his bachelor's he focused on creating an application for individuals that suffered from cognitive impairments through the use of the "Microsoft HoloLens" and machine learning to allow those individuals to maintain a semblance of everyday life. In his postgraduate Luke focused on using machine learning to generalise high-fidelity scientific simulations to rapidly generate predictions for parameter combinations that had not yet been sampled in order to accelerate the production of new results. Luke revels in all things AI, knowing that there is always more to learn and seeks to continually deepen his understanding around AI. A description of Luke's research: Modern games have an increasing focus on hyper-realism and immersion to better capture the attention of players. One of the ways that games can break this immersion is by having animations that break the flow of movement or actions through the use of predefined animations. Motion matching is a solution for predicting the best next frame of an animation by looking at the pose and user trajectory. The downside however, is that when you increase the amount of possible animations in the database the runtime cost also increases. A solution was proposed known as 'learned motion matching' (Holden et al., 2020) which takes the positive properties of motion matching but also achieves the scalability of neural-network-based generative models. This project will explore and improve the learned motion matching method through implementation of memory layers to improve accuracy without the sacrifice of increasing runtime costs. A restructuring and adaptation of the existing machine learning neural network used could also improve the learned motion matching method as breaking down each step of the learned motion matching at each step could uncover optimisations that are not initially visible. Another way restructuring could improve the learned motion matching is through creating a more succinct all-in-one approach which may streamline the process. Email lukebfarrar@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Miles Hansard Dr Patrik Huber Dr James Walker Themes Immersive Technology - Previous Next

< Back Dr Oliver Withington Queen Mary University of London iGGi Alum Available for post-PhD position Oliver Withington is a AI and games researcher working on novel methods for evaluating content generation systems for games. Following a successful career in the healthcare technology industry he decided to combine his life long love of games and interest in AI research into a PhD with the iGGi CDT in 2020. He lives in London with his wife and two young daughters, and when he is not writing about, thinking about, or talking about games you can probably find him in either his local bouldering gym, or in the park either pursuing or being pursued by two small children. A description of Oliver's research: Oliver's primary motivation is to make the evaluation of novel content generators more standardised, robust and straightforward for both researchers and game designers. Currently his focus is on techniques for producing informative visualisations of the output spaces of content generators. His work has been published at many of the leading conferences in his field, and he has also taken his work and ideas to the game industry, most recently in the form of a talk at GDC 2025's AI Summit. Email owithington@hotmail.co.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Featured Publication(s): Designer Difficulties: Visualizing the Possibility Spaces of Dynamic Difficulty Adjustment Systems Exploring the Possibility Space of 1 Billion Spells Exploring Minecraft Settlement Generators with Generative Shift Analysis HarmonyMapper: Generating Emotionally Divers Chord Progressions for Games. The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods Visualising Generative Spaces Using Convolutional Neural Network Embeddings Compressing and Comparing the Generative Spaces of Procedural Content Generators Illuminating Super Mario Bros: quality-diversity within platformer level generation Themes Creative Computing Design & Development Game AI https://www.youtube.com/watch?v=4m1gYriq_pc Previous Next

< Back Dr Nathan Hughes University of York iGGi Alum Nathan Hughes is a player experience researcher who focuses on how player make choices within games. Specifically, the work explores open world games such as Skyrim and the Witcher 3, as these games allow players a vast amount of choice with little restrictions on how and when these are made. However, little research has considered these choices, so little is known about how players experience choice in open world games. Therefore, research questions for this work include; why do players choose not to pursue the main quest? What do players choose to do instead? When and how do they make this decision? His background is in psychology, and so asks these questions from a psychological perspective. The aim is to uncover how the process of choosing unfolds, and how this is influenced. In turn, this may allow reflections on how the decision-making process operates - by analysing choices within open world games, a more controlled (but still intrinsically motivating) setting can be studied. Email ngjhughes@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor Prof. Paul Cairns Featured Publication(s): Clinicians Risk Becoming "Liability Sinks" for Artificial Intelligence Understanding specific gaming experiences: the case of open world games The need for the human-centred explanation for ML-based clinical decision support systems Growing Together: An Analysis of Measurement Transparency Across 15 Years of Player Motivation Questionnaires Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units Opening the World of Contextually-Specific Player Experiences No Item Is an Island Entire of Itself: A Statistical Analysis of Individual Player Difference Questionnaires Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities Themes Player Research - Previous Next

< Back Dr Memo Akten Goldsmiths iGGi Alum Real-time, interactive, multi-modal media synthesis and continuous control using generative deep models for enhancing artistic expression Real-time, interactive, multi-modal media synthesis and continuous control using generative deep models for enhancing artistic expression. This research investigates how the latest developments in Deep Learning can be used to create intelligent systems that enhance artistic expression. These are systems that learn – both offline and online – and people interact with and gesturally ‘conduct’ to expressively produce and manipulate text, images and sounds. The desired relationship between human and machine is analogous to that between an Art Director and graphic designer, or film director and video editor – i.e. a visionary communicates their vision to a ‘doer’ who produces the output under the direction of the visionary, shaping the output with their own vision and skills. Crucially, the desired human-machine relationship here also draws inspirations from that between a pianist and piano, or a conductor and orchestra – i.e. again a visionary communicates their vision to a system which produces the output, but this communication is real-time, continuous and expressive; it’s an immediate response to everything that has been produced so far, creating a closed feedback loop. The key area that the research tackles is as follows: Given a large corpus (e.g. thousands or millions) of example data, we can train a generative deep model. That model will hopefully contain some kind of ‘knowledge’ about the data and its underlying structure. The questions are: i) How can we investigate what the model has learnt? ii) how can we do this interactively and in real-time, and expressively explore the knowledge that the model contains iii) how can we use this to steer the model to produce not just anything that resembles the training data, but what *we* want it to produce, *when* we want it to produce it, again in real-time and through expressive, continuous interaction and control. Memo Akten is an artist and researcher from Istanbul, Turkey. His work explores the collisions between nature, science, technology, ethics, ritual, tradition and religion. He studies and works with complex systems, behaviour, algorithms and software; and collaborates across many disciplines spanning video, sound, light, dance, software, online works, installations and performances. Akten received the Prix Ars Electronica Golden Nica in 2013 for his collaboration with Quayola, ‘Forms’. Exhibitions and performances include the Grand Palais, Paris; Victoria & Albert Museum, London; Royal Opera House, London; Garage Center for Contemporary Culture, Moscow; La Gaîté lyrique, Paris; Holon Design Museum, Israel and the EYE Film Institute, Amsterdam. Please note: Updating of profile text in progress Email memo@memo.tv Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Top-Rated LABS Abstracts 2021 Deep visual instruments: realtime continuous, meaningful human control over deep neural networks for creative expression Deep Meditations: Controlled navigation of latent space Learning to see: you are what you see Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Mixed-initiative creative interfaces Learning to see Real-time interactive sequence generation and control with Recurrent Neural Network ensembles Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks Deepdream is blowing my mind All watched over by machines of loving grace: Deepdream edition Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks Themes Game AI - Previous Next

< Back Dr Adam Katona University of York iGGi Alum Adam did his MSc in mechatronics at Budapest University of Technology and Economics. After graduation, he spent two years working on automated driving at Robert Bosch GmbH, during which he got exposed to both the classical and the machine learning approach of creating intelligent agents. Evolutionary computation continues to surprise us by producing creative and efficient designs. However despite our best efforts, artificial evolution had not produced anything ascomplex and interesting as natural evolution. As our hardware is becoming faster and number of cores in our chips increase, the lack of computational power is becoming less of an excuse. It is starting to become more and more obvious that some fundamental component of natural evolution is missing from our simulations. One possible candidate is the evolution of evolvability. Evolution seems to produce organisms which are well suited for further evolution. The goal of my research is to find mechanisms which allows evolution to increase evolvability, and incorporate these in the design of more efficient neuroevolution algorithms.This research is in the intersection of evolutionary computation, evolutionary developmental biology and neural networks. Email mail.adamkatona@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Complex computation from developmental priors Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring Growing 3d artefacts and functional machines with neural cellular automata Time to die: Death prediction in dota 2 using deep learning Themes Game AI - Previous Next

< Back Dr Claudio Guarnera University of York Supervisor You can get more out of your site elements by making them dynamic. To connect this element to content from your collection, select the element and click Connect to Data. Once connected, you can update it anytime without affecting your design or updating elements by hand. Add any type of content to your collection, such as rich text, images, videos and more, or upload it via CSV file. You can also collect and store information from your site visitors using input elements like custom forms and fields. Be sure to click Sync after making changes in a collection, so visitors can see your newest content on your live site. Email claudio.guarnera@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Creative Computing - Previous Next

< Back Amy Smith Queen Mary University of London iGGi PG Researcher Available for post-PhD position After completing a BA in Fine Art, at Bath School of Art and Design, Amy spent some years as a tattoo artist travelling and creating artworks. An interest in learning to code then led her to complete a conversion Masters degree in Computer Science at the University of Birmingham. Keen to preserve her interests in both a creative practice as well as a new interest in generative deep learning, Amy joined the IGGI program to explore these interests further under the guidance of Dr. Mike Cook, Dr. James Walker and Prof. Simon Colton. Amy's research is currently focused on the intersection between 'imaginative play', computational creativity and generative deep learning. This project explores whether the kind of novel text, image and video media produced by generative deep learning algorithms can be used to provoke and stimulate the imaginative, ideation and visualisation capabilities of the user as they interact with this cutting edge technology. To date, her work has been published in the International Conference on Computational Social Science, AAAI, ICCC, CHI, SIGGRAPH Asia, and EvoMusArt. Amy hopes to further encourage and explore the fruits of a close collaboration between human creativity and creative AI. Email amyelizabethsmith01@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Mike Cook Prof. Simon Colton Dr James Walker Featured Publication(s): Herding CATs: Making Sense of Creative Activity Traces Tracing Creativity: A Design Space For Creative Activity Traces in HCI Interpretive Cultures: Resonance, randomness, and negotiated meaning for AI-assisted tarot divination AI Séance: Recounts from designing artificial intelligence for transcendence, interpretive lenses and chance Clip-guided gan image generation: An artistic exploration Scaling Analysis of Creative Activity Traces via Fuzzy Linkography Fuzzy Linkography: Automatic Graphical Summarization of Creative Activity Traces AI-Generated Imagery: A New Era for the 'Readymade' The @artbhot Text-To-Image Twitter Bot. Trash to Treasure: Using text-to-image models to inform the design of physical artefacts Clip-guided gan image generation: An artistic explorationClip-guided gan image generation: An artistic exploration Art and the science of generative AI Generative Search Engines: Initial Experiments Themes Creative Computing 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

Partners (All) iGGi is a collaboration between Uni of York + Queen Mary Uni of London: the largest training programme worldwide for doing a PhD in digital games. iGGi Partners We are excited to be collaborating with a number of industry partners. iGGi works with industry in some of the following ways: Researcher Industry Knowledge Exchange - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the researcher and their industry partner. Researcher Sponsorship - for some of our researchers, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - iGGi industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! Check out our Industry Info page here to see these types of collaboration described in more detail. There are many ways for our industry partners to work with iGGi. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. 22 Cans AI and Games Autistica BT BetaJester Limited BiG BlitzGame Studios Bossa Studios British Broadcasting Corporation BBC British Games Institute (BGI) CBT Clinics COMIC Research Carnegie Mellon University Cooperative Innovations Creative AI Creative Assembly Die Gute Fabrik Digital Catapult Dubit Limited Durham University ESL UK Electronic Arts (EA) Enigmatic Studios Falmouth University Fluttermind LLC

< Back Dominik Jeurissen Queen Mary University of London iGGi PG Researcher Dominik holds an MSc in Artificial Intelligence from Maastricht University and a BSc in Computer Science with a focus on Applied Mathematics from RWTH Aachen. During his undergraduate studies, he worked for 3 years as a software engineer at INFORM GmbH, contributing to their supply management software, add*ONE. His long-term goal is to develop fully autonomous agents that can act in complex environments while continually improving themselves. As a result, he is familiar with many related fields, including reinforcement learning, continual learning, large language models (LLMs), and imitation learning. A description of Dominik's research: "My PhD is a collaboration with Creative Assembly , focusing on researching AI for complex strategy games, such as Total War. With the recent rise of Large Language Models (LLMs), I'm exploring their potential to enhance game-playing agents. LLMs can instantly recall knowledge on almost any topic, perform basic reasoning, and are easily configured for a wide range of text-based tasks. These abilities make them especially promising for game development, where machine learning agents often struggle due to constantly changing game environments. During an internship at Creative Assembly, I worked together with the Creative Assembly's R&D team and NVIDIA to develop a prototype for an Onboarding Assistant that aims to help players when they are stuck in the game. My current research aims to improve the assistant by exploring systems for retrieving dynamic gamestate information." Email d.jeurissen@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Diego Pérez-Liébana Dr Jeremy Gow Featured Publication(s): Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds Playing nethack with llms: Potential & limitations as zero-shot agents Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Generating Diverse and Competitive Play-Styles for Strategy Games Automatic Goal Discovery in Subgoal Monte Carlo Tree Search Game state and action abstracting monte carlo tree search for general strategy game-playing Portfolio search and optimization for general strategy game-playing The Design Of" Stratega": A General Strategy Games Framework Themes Design & Development Game AI Game Data - Previous Next

< Back Dr Mihail Morosan University of Essex iGGi Alum Computational Intelligence and Game Balance. (Industry placement at MindArk) Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. This process can, and should, be aided by automated systems, as machines have proven to be capable of finding innovative ways to complement human intuition and inventiveness. When man and machine cooperate, better products are created and the world has only to benefit. My research seeks to find, test and assess methods to apply computational intelligence to human-led game balance. Early research has proven that AI can successfully aid game designers in analysing the viability of various game rules and I intend to document this and polish the techniques that will result from my work. To achieve this, I am making use of cutting edge algorithms, powerful AI techniques and novel methods. Most of the current work done involves the use of evolutionary algorithms, as well as statistical analysis and evaluation of intelligent agents in various video games. Programmer (with a focus on optimisation and quick deliverables, mostly due to competitive experience), gamer (games are fun, relaxing and a great social experience), technology consumer (comes with the programmer bit) and all around happy guy stumbling through the world. Once ended up in a management internship at a bank thinking the application was for a programming position. And another time told an interviewer that "buying and eating a burger to solve hunger" is a legitimate problem-solving skill. Somehow received an invitation to the next interview stage. Email me@morosanmihail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Automating game-design and game-agent balancing through computational intelligence Lessons from testing an evolutionary automated game balancer in industry Genetic optimisation of BCI systems for identifying games related cognitive states Online-Trained Fitness Approximators for Real-World Game Balancing Evolving a designer-balanced neural network for Ms PacMan Speeding up genetic algorithm-based game balancing using fitness predictors Automated game balancing in Ms PacMan and StarCraft using evolutionary algorithms Themes Design & Development Game AI Player Research - Previous Next