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< Back Dr Tom Collins University of York Supervisor Tom runs the Music Computing and Psychology Lab in the Music Department at University of York, and so makes a good supervisor for game audio projects, but he has wider interests in media (e.g., podcasts) and sport (especially football), and in sport how AI can be leveraged to enhance analytics that lead to new insights into, and competitive advantages in, individual and team performance. Tom is internationally recognised for his work in automatic music generation, web systems for music, and information retrieval. His research has been featured by the BBC (BBC Click), The Times, and Financial Times among others. Tom is interested in supervising students who have a background in at least one of the following areas, and who are interested in acquiring knowledge of the others: Data science and machine learning (especially deep learning); One of music, podcasts, or sport; Software engineering (especially full-stack JavaScript development). tom.collins@york.ac.uk Email Mastodon https://tomcollinsresearch.net Other links Website LinkedIn BlueSky Github Themes Esports Game AI Game Audio Game Data Player Research - 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 Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - 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. me@morosanmihail.com Email Mastodon Other links Website https://uk.linkedin.com/in/morosanmihail LinkedIn BlueSky Github 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

< Back Dimitris Menexopoulos Queen Mary University of London iGGi PG Researcher Available for post-PhD position Dimitris Menexopoulos is a versatile music composer, sound designer, audio technologist, and multi-instrumentalist based in London, UK. With an academic background in Geoscience, Electronic Production, and Information Experience Design, he draws upon a broad knowledge base across Art, Science, and Technology to inform his work. He has released two solo albums (Perpetuum Mobile – 2017, Phenomena – 2014), three EPs (Siren’s Call – 2025, Modern Catwalk Music – 2022, 40 – 2020), and two soundtracks (Iolas Wonderland – 2021, The Village – 2019), and has performed internationally. Collaborative work includes projects with choreographer/dancer Akram Khan (Thikra: Night of Remembering – 2025), director Shekhar Kapur (Brides of the Well – 2018), and electronic musician Robert Rich (Vestiges– 2016), among others. As a designer, he has exhibited work at venues such as Christie’s London (Christie’s Lates – 2023, with Scarlett Yang), Somerset House (24 Hours in Uchronia with Helga Schmid – 2020), and the Barbican Centre (Nesta FutureFest – 2019, with Akvile Terminaite). Currently, his research focuses on graphics-driven procedural audio for interactive and linear experiences, as well as on innovative systems for music composition and performance. His original scientific publications and devices have been featured at prestigious events in Japan (AES 6th International Conference on Audio for Games – 2024), Spain (AES Europe – 2024), the UK (Iklectik – 2020), France (IRCAM – 2020, 2019), and the USA (Mass MoCA – 2019). Since 2025, he has served as a reviewer for Computer Music Journal, published by MIT Press—one of the leading academic journals in the field of computer music and digital sound technology. contact@menex.world Email http://www.linktr.ee/menex.world Mastodon http://www.menex.world Other links Website https://www.linkedin.com/in/dimitris-menexopoulos/ LinkedIn BlueSky Github Supervisors: Dr Josh Reiss Dr Tom Collins Featured Publication(s): Using texture maps to procedurally generate sound in virtual environments The State of the Art in Procedural Audio Themes Creative Computing Game Audio - Previous Next

< Back Dr Anna Bramwell-Dicks University of York Supervisor Anna Bramwell-Dicks has an interdisciplinary background which started in Electronics and Music Technology before taking a sideways move to the field of Human-Computer Interaction research. She likes to combine her underlying interest in sound and music with applied psychology and creativity. She is very interested in research involving multimodal interaction (e.g. using audio, haptics, smell and/or proprioception as well as visuals within interfaces) particularly where audio is used to affect user’s behaviour or experiences. She is also very interested in accessibility research and any research in the application area of mental health and mental illness. As a lecturer in Web Development and Interactive Media, based in TFTI, Anna is always interested in work that involves designing and evaluating novel and interesting user experiences, particularly where that leads to the option to create fun, engaging, accessible experiences. She likes to work across a range of application areas ranging from learning environments to e-commerce to escape rooms and cultural exhibits! Anna is keen to work with students who want to design and develop gamified systems to support people with disabilities, physical or mental illness. Or, those who are also interested in multimodal experiences. Research themes: Accessibility Multimodal and multisensory systems Research methods anna.bramwell-dicks@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/anna-bramwell-dicks-2b941a28/ LinkedIn BlueSky Github Themes Accessibility Applied Games Design & Development Game Audio Player Research - Previous Next

< Back Dr Mona Jaber Supervisor Mona Jaber is a lecturer in Internet of Things (IoT) who’s research is centred at the intersection of IoT and machine learning for sustainable development goals. In particular, she is interested in harnessing IoT data to model mobility trends in a digital twin platform that allows users to test future measures in a verisimilar virtual environment. Her research is grounded in privacy-preserving measures for capturing and analysing IoT data. She is the winner of a new investigator award research grant (DASMATE £500K) in which she examines distributed acoustic sensors systems and a privacy-preserving alternative data source to model active travel. She is interested in supervising students on the topic of serious game building that engages the public in shaping their neighbourhood through interventions in the virtual environment towards sustainable 15 minutes city goals. m.jaber@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/profiles/jabermona.html Other links Website https://www.linkedin.com/in/mona-jaber/ LinkedIn BlueSky Github Themes Accessibility Applied Games Game AI - Previous Next

< Back Dr James Goodman Queen Mary University of London iGGi Alum James has picked up degrees in Chemistry, History, Mathematics, Business Administration and Machine Learning. After a career in Consultancy and IT Project Management he is now finally doing the research he always wanted to. James is interested in opponent modelling, theory of mind and strategic communication in multi-player games, and how statistical forward planning can be used in modern tabletop board-games (or other turn-based environments). With a constrained budget, how much time should an agent spend thinking about it's own plan versus thinking about what other players might be doing to get in the way. How does this balance vary across different games? His secondary research interests are in using AI-playtesting as a tool for game-balancing and game-design. james.goodman@qmul.ac.uk Email Mastodon https://www.tabletopgames.ai/ Other links Website https://www.linkedin.com/in/james-goodman-b388791/ LinkedIn BlueSky Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): Seeding for Success: Skill and Stochasticity in Tabletop Games From Code to Play: Benchmarking Program Search for Games Using Large Language Models Skill Depth in Tabletop Board Games Measuring Randomness in Tabletop Games A case study in AI-assisted board game design Following the leader in multiplayer tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games MultiTree MCTS in Tabletop Games Visualizing Multiplayer Game Spaces TAG: Terraforming Mars Fingerprinting tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games AI and Wargaming Metagame Autobalancing for Competitive Multiplayer Games Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game Weighting NTBEA for game AI optimisation Re-determinizing MCTS in Hanabi Noise reduction and targeted exploration in imitation learning for abstract meaning representation parsing UCL+ Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound Themes Design & Development Game AI - Previous Next

< Back Dr Christian Guckelsberger Queen Mary University of London iGGi Alum + Supervisor Intrinsic Motivation in Computational Creativity with Applications to Games. (Industry placement at Splash Damage and Microsoft Research) This research investigates how we can engineer artificial systems that are creative in their own right. Christian addresses this challenge with computational models of intrinsic motivation (IM). Intrinsically motivated agents perform an activity for its inherent satisfaction rather than for some instrumental outcome. A classic example is to act in order to satisfy one’s curiosity. In both theoretical and applied studies, he demonstrates that models of IM can give rise to general, robust and adaptive creative systems. Christian has shown how models of IM can be used to create highly general non-player characters. Such characters can potentially be used in a wide range of games without previous knowledge of the game mechanics, reducing costs and effort in game development while increasing robustness and behavioural variety Christian’s ongoing research stretches beyond video games, investigating the role of computational models of IM for intentional agency, open-ended development and creativity in minimal lifeforms and artificial systems. Christian studied Computer Science, History of Art and Business in Germany and the UK and is now based in London, working towards a PhD in Artificial Intelligence. His work challenges the question how computers could ever become genuinely creative with a highly interdisciplinary approach based on Computing, Cognitive Science and Philosophy. Over the last few years, he published papers on a wide range of topics, held a tutorial on intrinsic motivation in video games, organised workshops on computational serendipity and spent three months at NYU’s Game Innovation Lab for a research collaboration. Christian has substantial industry experience, looking back at three years in the R&D department of SAP SE and a recent internship at Microsoft Research Cambridge. He enjoys working in an international environment with open-minded, passionate people. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/in/christianguckelsberger LinkedIn BlueSky Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Predicting game difficulty and engagement using AI players Embodiment and computational creativity Intrinsic Motivation in Computational Creativity Applied to Videogames. PhD Thesis. 306 pages. The Relationship of Future State Maximization and von Foerster's Ethical Imperative Through the Lens of Empowerment On the Machine Condition and its Creative Expression. Understanding and Strengthening the Computational Creativity Community: A Report From The Computational Creativity Task Force. Action Selection in the Creative Systems Framework Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (CORGIS) Generative design in Minecraft: Chronicle challenge Towards Mode Balancing of Generative Models via Diversity Weights Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Themes Game AI - Previous Next

< Back Remo Sasso Queen Mary University of London iGGi PG Researcher I hold a BSc and MSc in Artificial Intelligence at the University of Groningen (NL) and am currently a PhD student at the Queen Mary University of London under the supervision of Paulo Rauber. In addition to my academic work, I have worked as a Machine Learning engineer, and am currently the Head of AI at xDNA, an AI/Cybersecurity-based start-up from the Netherlands. Here I'm leading the initiative Project Aletheia, where we develop AI-driven tools to optimize the workflow of professional fact-checkers, with the overarching goal of ensuring information integrity in the world. Foundation World Models and Foundation Agents for Reinforcement Learning My research focuses on developing reinforcement learning algorithms that are both scalable and sample-efficient through Bayesian methods and model-based approaches, recently with a particular emphasis on Large Language Models (LLMs). My previous research focused on principled, efficient and scalable exploration algorithms for reinforcement learning, e.g. Poster Sampling for Deep Reinforcement Learning (ICML 2023), where we developed a reinforcement learning algorithm that can be considered state-of-the-art in Atari games. Currently I'm particularly interested in the integration of LLMs in the reinforcement learning framework, both as decision-making agents and simulators. My current research, called "Foundation World Models and Foundation Agents for Reinforcement Learning" investigates this integration in-depth and shows that large models show significant potential in various reinforcement learning tasks, ranging from decision-making in stochastic environments to serving as world models. r.sasso@qmul.ac.uk Email https://remosasso.github.io/ Mastodon Other links Website https://www.linkedin.com/in/remo-sasso-b9837a1ba/ LinkedIn BlueSky https://github.com/remosasso Github Supervisor: Dr Paulo Rauber Featured Publication(s): VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts Making Connections: Neurodevelopmental Changes in Brain Connectivity after Adverse Experiences in Early Adolescence Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Simultaneous multi-view object recognition and grasping in open-ended domains Posterior Sampling for Deep Reinforcement Learning Themes Game AI - Previous Next

< Back Prof. David Beer University of York Supervisor Professor Beer has been researching new and digital media since completing his PhD in 2006. This has included work on social media, mobile devices and algorithms. Over the last decade he has developed work exploring the social implications of data and metrics. His work has explored how automated decision making is impacting upon social connections and has looked at how the data that accumulates about us shaped the way individuals are understood and judged. He has recently conducted a study of the data analytics industry and produced a report into online targeting. His research areas for supervision include: The social power of algorithms Data analytics The power of data and metrics Critical analyses of data visualization The metricisation of everyday life Social media and social media data Online targeting Data harvesting and inequality Research themes: Game AI Game Analytics Game Design Games with a Purpose Computational Creativity Gaming data Algorithms in gaming Gamification and the social world david.beer@york.ac.uk Email Mastodon https://davidbeer.net/ Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing Game AI Game Data Player Research - Previous Next

< Back Peter York University of York iGGi Alum PhD student working in analytics and machine learning for esports broadcast and understanding. In particular working with Weavr on various projects related to broadcast and learning tools for Dota 2. Please note: Updating of profile text in progress Email Mastodon https://pete-york.github.io Other links Website LinkedIn BlueSky Github Featured Publication(s): Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports DAX: Data-Driven Audience Experiences in Esports A generalized framework for self-play training Themes Esports Game AI - Previous Next

< Back George Long Queen Mary University of London iGGi PG Researcher Available for placement George is an IGGI PhD student interested in AI assisted game design, particularly in how it can be used to assist in the creation and balancing of game mechanics. After graduating with a BSc in Computer Science at the University of Essex, he joined IGGI in 2021 to be able to research how Artificial Intelligence can be applied specifically to reduce the prevalence of Min-Maxing in Role-Playing Games. A description of George's research: My research focuses on the concepts of Min-Maxing and Meta in Role-Playing Games, and how we can use AI assisted game design to reduce their prevalence. Min-Maxing in Role-Playing Game refers to the idea of building a character in a Role-Playing Game by maximising their positive traits while minimising negative ones, often through exploiting game mechanics. This can cause optimal strategies to emerge which not only have the potential to upset the game balance, but when these strategies become prominent enough in the community to form a Meta, it can have wider consequences such as the shunning of players deemed not to be using optimal strategies, and loss of creative choice when building characters. There are two methods I am looking into to reduce the effectiveness of Min-Maxing. The first is using AI to discover these Min-Maxed strategies. Secondly, how AI can be used in the game balancing process to identify and modify the mechanics which enable these strategies. Currently, I am focusing on the first method, with my research looking into how we can measure the effectiveness of units in combat scenarios to identify which units could be considered unbalanced. g.e.m.long@qmul.ac.uk Email Mastodon http://www.longhouse.dev Other links Website https://www.linkedin.com/in/georgelonghouse/ LinkedIn BlueSky Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Themes Design & Development Game AI Game Data - Previous Next