< Back Tamsin Isaac University of York iGGi PG Researcher Tamsin has been a lifelong gamer ever since receiving her first Game Boy and has long been fascinated by how people engage with games emotionally, socially, and behaviourally. She joined the iGGi CDT in 2023 after completing a BSc and MSc in Psychology at the University of Plymouth, where she developed a growing interest in player motivation, disengagement, and live-service game design. Her PhD research focuses on limited-time events (LTEs) in digital games—temporary content designed to encourage engagement and re-engagement in live-service games. Through this work, she explores how LTEs shape player behaviour, routine, anticipation, disengagement, and return play across platforms and genres. Tamsin’s research combines large-scale content analysis with qualitative diary-and-interview methods to investigate both the structure and lived experience of LTEs. She is currently developing a cross-platform taxonomy of LTEs using data from over 2,600 Steam and Google Play games, alongside player-focused research exploring how individuals decide whether events are “worth” participating in during everyday play. Her work aims to support more ethical, sustainable, and player-friendly approaches to live-service game design by helping researchers and developers better understand how event structures influence player experience and long-term engagement. She is open to collaboration opportunities with game studios interested in live-service systems, player behaviour, engagement design, and event analysis using player data or design insights. When not researching or analysing games, Tamsin enjoys baking, reading, playing cosy indie games, and quietly grinding dailies. Email tamsin.isaac@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Prof. Paul Cairns Themes Applied Games Design & Development Player Research https://www.youtube.com/watch?v=n32ngtGYNQ8 Previous Next

< Back Dien Nguyen Queen Mary University of London iGGi PG Researcher Available for placement I graduated from the University of California, Irvine with a BSc in Computer Game Science where I became interested in the intersection of games and artificial intelligence – applying search, planning, evolutionary methods and knowledge representation to game playing and game design. My long-term goal is to work on the problem of formalizing game elements, representing game systems in a way that allows for automatic reasoning and inference. I also enjoy playing games where I can customize and theorycraft my playstyle to satisfy certain gameplay fantasies while beating the game. My current research is within the field of Automated Game Design Learning, an emerging field in AI research with the purpose of learning game design models through playing. The current strategy is to play out the full game in thousands of iterations, which can be impractical for complex games with large state space and computationally expensive forward models. My research will focus on applying Go-Explore—a recent exploration paradigm that outperforms many state-of-the-arts—to improve the efficiency of automated playtesting of tabletop games by using an archive of interesting game states to reduce the time needed for self-play. The research will be primarily conducted within the TAG framework and aim to be game-agnostic. On successful completion, this research will improve game development cycles, resulting in higher-quality games, and potentially give unique insights into the game design process. Email d.l.nguyen@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Diego Pérez-Liébana Featured Publication(s): JSON-Bag: A generic game trajectory representation Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis Themes Applied Games Creative Computing Design & Development Game AI - Previous Next

< Back Dr Remo Sasso Queen Mary University of London iGGi Alum 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. Email r.sasso@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Paulo Rauber Featured Publication(s): Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark 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 Gorm Lai Goldsmiths iGGi PG Researcher Inspired by the works of Karl Sims and William Latham as well games such as a Spore and No Man's Sky, Gorm's main work is focused on using artificial intelligence and machine learning to generate creatures for use in video games. Combining this with his background as a virtual reality pioneer, his full doctorate is looking into how mixed-initiative co-creative interfaces in vr can assist in creating procedural generated creatures for use in video games. As a stalwart of the game development community, Gorm ran the Danish chapter of the International Game Developer Association (IGDA) for 5 years, started the London Indie Game Developers meetup group which currently features almost 3000 members, co-founded the Nordic Game Jam, as well as the Global Game Jam. The Global Game Jam has been recorded into the Guinness Book of World Records, and has more participating countries than the Winter Olympics. Gorm is a games industry veteran who has worked on 17 commercial video games since 2004, and has spoken at numerous games industry conferences such as GDC, Nordic Game & Develop Brighton. Gorm is a student at Goldsmiths, University of London, where is he is supervised by William Latham and Frederic Fol Leymarie. Email lai.gorm@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. William Latham Featured Publication(s): Formal Constraints and Creativity: Connecting Game Jams, Dogma ’95, the Demo Scene, OuBaPo, and Renga poets What Is a Game Jam? The Dark Side of Game Jams On Mixed-Initiative Content Creation for Video Games Two decades of game jams Virtual Creature Morphology‐A Review Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry Introducing: the game jam license Trends in organizing philosophies of game jams and game hackathons The global game jam for teaching and learning Gplayer A compression method for spectral photon map rendering Themes Creative Computing Design & Development Game AI - Previous Next

< Back Igor Dall'Avanzi Goldsmiths iGGi Alum Creation of accessible tools for the use of procedural audio in video games The aim of this research is to investigate and provide new tools to developers for the use of procedural audio into video games. Procedural approaches could address different issues that commonly afflict game audio. In music, generative systems are not only less repetitive, but offer more adaptability as well. For what concerns sound design, they can provide not only variety, but stronger and more realistic support to the interaction with the game world; interaction that is becoming even deeper with the advent of VR Yet, these methods still need improvement on different sides. One is the level of quality that procedural audio needs to achieve to compete with the current aesthetic established by the use of rendered sounds and music in the media. Another is the additional amount of work required by the CPU to render the assets on runtime, and its variable cost). Finally, there is a general lack of user-friendly tools, to link common programming languages for audio to game engines. Software like MaxMsp, Pure Data or SuperCollider is used to design generative audio systems. A more accessible integration of these software could promote generative approaches among sound designers and composers in the field, that today have instead access to tools mainly designed to be used with rendered assets. My plan is to bring on research first by focusing on how a higher degree of quality could be addressed, exploring tools like the above mentioned MaxMsp, Pure Data, low level solutions, and machine learning algorithms. Primary research will be run to confront procedurally generated audio content with rendered one; to understand its impact on the player, and the level of quality needed to deliver a satisfactory experience. The creation of more accessible interfaces and tools dedicated to implement procedural audio in video games will be investigated and undertaken. I like to make noises of all sort and to play with them. For this reason I graduated in Music Production in 2016 and, at the moment of writing, I am finishing my final project for an MSc in Sound and Music for Interactive Games at Leeds Beckett University. Composer and sound designer, in the last year I have been focusing on audio implementation and programming, and I am currently exploring machine learning approaches for procedural audio. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Game Audio Player Research - Previous Next

< Back Susanne Binder Queen Mary University of London iGGi Manager iGGi Admin iGGi Manager @ QMUL ; alongside David Hull (iGGi Manager @ UoY) , and supported by Shopna Begum , Helen Tilbrook and Oliver Roughton, she's mostly in charge of making things run at iGGi with particular focus on iGGi-QMUL-specific admin iGGi-QMUL-specific student concerns PR, website and social media industry liaison Email s.binder@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes - Previous Next

< Back Dr Guifen Chen Queen Mary University of London Supervisor Dr Guifen Chen is a Lecturer in Neurobiology at QMUL. Her work focuses on the neuronal basis of multisensory integration, spatial cognition and memory. Her lab uses state-of-the-art techniques such as immersive virtual reality and in vivo electrophysiological/probe recording in mice. Her research is currently supported by funding from BBRSC and the Royal Society. Dr Chen completed her undergraduate studies in both biology and computer science at East China Normal University in China. She then pursued PhD in neuroscience, conducting research at both East China Normal University and Boston University in the USA. Following that, she undertook postdoctoral research at University College London in the UK. Her work has been published in high-impact journals such as Nature Communications, eLife, and Current biology. Email guifen.chen@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Design & Development Immersive Technology Player Research - Previous Next

< Back Dr Terence Broad Goldsmiths iGGi Alum Terence Broad is an artist and researcher working on developing new techniques and interfaces for the manipulation of generative models. His PhD focusses on how pre-trained generative neural networks can be repurposed and reconfigured for authoring novel multimedia content. He is completing his PhD at Goldsmiths, University of London and is also a visiting researcher at the UAL Creative Computing Institute. His research has been published in international conferences, workshops and journals such as SIGGRAPH, NeurIPS, Leonardo and xCoAx. He was acknowledged as an outstanding peer-reviewer by the journal Leonardo. Terence is a practicing artist and often uses the techniques he has developed in his research in the creation of his artworks. His art has been exhibited and screened internationally at venues such as The Whitney Museum of American Art, Ars Electronica, The Barbican and The Whitechapel Gallery. He won the Grand Prize in the ICCV 2019 Computer Vision Art Gallery. Email t.broad@gold.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Co-Designing Fashion with AI: A Small-Data Approach to Generative Garment Design Expanding the Generative Space: Data-Free Techniques for Active Divergence with Generative Neural Networks XAIxArts Manifesto: Explainable AI for the Arts Using Generative AI as an Artistic Material: A Hacker's Guide Is computational creativity flourishing on the dead internet? Interactive Machine Learning for Generative Models Envisioning Distant Worlds: Fine-Tuning a Latent Diffusion Model with NASA's Exoplanet Data Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Network Bending: Expressive Manipulation of Generative Models in Multiple Domains Active Divergence with Generative Deep Learning--A Survey and Taxonomy Network Bending: Expressive Manipulation of Deep Generative Models Amplifying The Uncanny Transforming the output of GANs by fine-tuning them with features from different datasets Searching for an (un) stable equilibrium: experiments in training generative models without data Autoencoding Blade Runner: Reconstructing Films with Artificial Neural Networks Light field completion using focal stack propagation Autoencoding video frames IoT and Machine Learning for Next Generation Traffic Systems Themes Creative Computing Design & Development - 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): 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 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. Email tw1700@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes - Previous Next

< Back Prof. Greg Slabaugh Queen Mary University of London Supervisor Gregory G. Slabaugh is Professor of Computer Vision and AI and Director of the Digital Environment Research Institute (DERI) at Queen Mary University of London. He is also a Turing Fellow at the Alan Turing Institute. His research work spans computer vision and computer graphics including geometric modelling and image/video-based understanding. He is interested in deep learning approaches including generative techniques like normalizing flow an generative adversarial networks. He previously worked in the games industry as a 3D graphics programmer and his PhD thesis focussed on how to model 3D objects from a collection of images. He is interested in how to create engaging content and interaction from images as well as procedural methods to reduce the effort of 3D modelling. Email g.slabaugh@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Creative Computing Immersive Technology - Previous Next

< Back Prof. Nick Pears University of York Supervisor Nick Pears is a Professor of Computer Vision in York’s Vision, Graphics and Learning (VGL) research group. He works on statistical modelling of 3D shapes, with an emphasis on the human face and head. The Liverpool-York Head Model and the associated Headspace training set has been downloaded by over 100 research groups internationally, with the Universal Head Model being downloaded by 50 research groups. His most recent work with his PhD students has focused on semantic disentanglement of 3D images and how to make autonomous vehicles safer and more trustworthy when using computer vision systems. He is assessor for many PhDs including construction of generative models for novel video content using adversarial deep learning techniques. Email nick.pears@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game AI - Previous Next