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< Back Dr Josh Reiss Queen Mary University of London Supervisor Josh Reiss investigates transformative technologies focused around audio production and sound design. He has published more than 200 scientific papers (including over 50 in premier journals and 5 best paper awards), and co-authored two books. His research has been featured in dozens of original articles and interviews on TV, radio and in the press. He is a Fellow and former Governor of the Audio Engineering Society. He co-founded the highly successful spin-out company, LandR, and recently formed a second start-up, FXive. He maintains a popular blog, YouTube channel and twitter feed for scientific education and dissemination of research activities. Prof. Reiss has a strong interest in games research, especially procedural audio content generation. Procedural content generation supports creation of rich and varied games, maps, levels, characters and narrative elements. But sound design has not kept pace with such innovation. Often the visual aspects of every object in the scene may be procedurally rendered, yet sound designers still rely on huge libraries of pre-recorded samples. This approach is inflexible, limited and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, computational, or tailored only to a narrow class of sounds. Machine learning from the sample libraries, to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. He welcomes the opportunity to supervise students interested in this or related topics. Research themes: Procedural Content Generation Game Audio and Music Game AI Game Design Computational Creativity Player Experience joshua.reiss@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/~josh/index.htm Other links Website https://www.linkedin.com/in/reissjoshua/ LinkedIn BlueSky Github Themes Creative Computing Game AI Game Audio - 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. t.broad@gold.ac.uk Email Mastodon https://terencebroad.com Other links Website https://www.linkedin.com/in/terence-broad-81350668/ LinkedIn BlueSky https://github.com/terrybroad Github Featured Publication(s): 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 Active Divergence with Generative Deep Learning--A Survey and Taxonomy 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 Nicole Levermore University of York iGGi PG Researcher Available for placement Nicole's academic background is within Neuroscience, having achieved BSc Neuroscience and Psychology, MSc Translational Neuroscience and an MPhil in Auditory Neuroscience. Outside of her research interests, she enjoys playing video games, hiking and playing the cello. A description of Nicole's research: Video games have enormous potential for research on cognition and mental health. In my project, I will use video games to perform basic research into a common psychiatric disorder (ADHD), paving the way for improved diagnosis, monitoring and therapy. ADHD is typically diagnosed in childhood and is characterised by failures of attentional state maintenance. This project involves using cutting-edge neuroimaging techniques to investigate how subjects with and without ADHD switch between attentional states (for example, ‘engagement’ and ‘flow’) while playing a cognitively engaging video game. The ultimate goal is to use video games to understand how mental health impacts people’s ability to focus on cognitively demanding tasks and, potentially, to develop therapeutic intervention. iggi-admin@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/nicole-levermore-b14245283 LinkedIn BlueSky Github Supervisor: Prof. Alex Wade Themes Accessibility Design & Development Immersive Technology Player Research Previous Next

< Back Dr Daniel Gomme University of Essex iGGi Alum Players have underlying expectations of the opponents they play against in strategy games: don't break the rules, provide a sense of tension, be able to communicate plans... AI doesn't always fulfil these. Dan's focus is on finding ways to better fulfil those expectations - and even to overtly change them - in order to improve player experience. With qualitative tools and in-game testing, he's found several concrete design mechanisms that can further that goal. daniel.gomme@yahoo.co.uk Email Mastodon Other links Website https://www.linkedin.com/in/daniel-gomme/ LinkedIn BlueSky https://github.com/OctarineSourcerer Github Supervisor Prof. Richard Bartle Featured Publication(s): Player Expectations of Strategy Game AI Playing with Dezgo: Adapting Human-AI Interaction to the Context of Play Strategy Games: The Components of A Worthy Opponent Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Tools To Adjust Tension And Suspense In Strategy Games: An Investigation Themes Design & Development Game AI Player Research - Previous Next

< Back Matt Bedder University of York iGGi Alum Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io) Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games. Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique. Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/pub/matthew-bedder/80/2a7/a51/ LinkedIn BlueSky Github Featured Publication(s): Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms Computational approaches for understanding the diagnosis and treatment of Parkinson's disease Automated motion analysis of adherent cells in monolayer culture Themes Game AI - Previous Next

< Back Francesca Foffano University of York iGGi PG Researcher Available for post-PhD position Francesca's work represents her fascination with how players elaborate and understand complex situations in video games. She likes to use mixed methods (both qualitative and quantitative) to understand high-level player perception in video games using her competencies in HCI (MSc at the University of Trento) and Psychology (BSc at the University of Padua). Prior to joining the PhD, she developed international experience in industry and research. She worked as Research Fellow on AI and ethics for the European project AI4EU at ECLT (Ca' Foscari University of Venice) and on players' perception of adaptive videogames at Reykjavik University. She also was involved as UX Strategist in creative content for MediaMonks headquarter (Amsterdam). A description of Francesca's research: Players will tell you exactly when they got stuck playing a game, but how we define stuck in the first place is still open to discussion. This PhD research aims to identify how and when this happens to help in predicting when players need support. The goal is to smoothen the player experience by reducing the need for external support (such as online guides, walkthroughs, and online forums) that might affect player immersion. The current stage of the research uses in-depth interviews to understand what players have in common, no matter what task they are doing or game they are playing. So why rely on user tests that consider singular test cases instead of understanding where they originate? ff716@york.ac.uk Email Mastodon https://ffoffano.wordpress.com/about/ Other links Website https://www.linkedin.com/in/foffanofrancesca/ LinkedIn https://bsky.app/profile/francescafoffano.bsky.social BlueSky Github Supervisor: Prof. Paul Cairns Featured Publication(s): A Survey on AI and Ethics: Key factors in building AI trust and awareness across European citizens. When Games Become Inaccessible: A Constructive Grounded Theory on Stuckness in Videogames Artificial intelligence across europe: A study on awareness, attitude and trust When Games Become Inaccessible: A Constructive Grounded Theory on Stuckness in Videogames Investing in AI for social good: an analysis of European national strategies European Strategy on AI: Are we truly fostering social good? Changes of user experience in an adaptive game: a study of an AI manager Themes Player Research - 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 and a Minor in Statistics. My undergraduate thesis focused on augmenting Monte Carlo tree search with a value network trained through a self-play framework similar to AlphaZero. During my undergraduate degree, I became interested in the intersection of games and artificial intelligence—applying methods of reinforcement learning, graphical models, 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. d.l.nguyen@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor: Dr Diego Pérez-Liébana Featured Publication(s): 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 Pengcheng Liu Queen Mary University of London Supervisor Dr Pengcheng Liu is a Lecturer (Assistant Professor) at the Department of Computer Science, University of York, UK. He is an internationally leading expert in robotics, Artificial Intelligence and human-machine interaction. He has been leading and involving in several research projects, including EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, NSFC, etc. Several of his research works were published on top-tier journals and leading conferences in the fields of robotics and AI. Before joining York, he has held several academic positions including a Senior Lecturer at Cardiff School of Technologies, Cardiff Metropolitan University, UK, a joint Research Fellowship at Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute of Agri-Food Technology (LIAT), University of Lincoln, UK, a Research Assistant and a Teaching Assistant at Bournemouth University, UK. I also held academic positions as a Visiting Fellow at Institute of Automation, Chinese Academy of Sciences, China and Shanghai Jiao Tong University, China. Dr Liu is a Member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Systems, Man and Cybernetics Society (SMC), IEEE Control Systems Society (CSS) and IFAC. He is member of IEEE Technical Committees (TC) on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. He has published over 60 journal and conference papers. Dr Liu serves as an Associate Editor for IEEE Access and PeerJ Computer Science. He received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He was selected as regular Fundings/Grants reviewer for EPSRC, NIHR and NSFC. Dr Liu’s research interest relevant to CDT IGGI include applied games for healthcare and rehabilitation applications, as well as using mixed reality and machine learning for human-machine interactions. He is particularly interested in supervising students with a design, HCI, computer science or behavioural sciences background on the following topics: applied games for healthcare and rehabilitation design for adaptive mixed reality system for physical therapy and neurological rehabilitation design for physical and cognitive behaviour change learning for human intention prediction analysis of mixed reality rehabilitation system with biological signals (EEG, sEMG) pengcheng.liu@york.ac.uk Email Mastodon https://sites.google.com/view/pliu Other links Website https://www.linkedin.com/in/pengcheng-liu-12703288/ LinkedIn BlueSky Github Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Prof. Richard Bartle University of Essex iGGi Co-Investigator Supervisor Richard Bartle is a renowned pioneer in game design and research. He co-wrote the first virtual world, MUD ("Multi-User Dungeon") in 1978, and has thus been at the forefront of the online games industry from its very inception. He is an influential writer on all aspects of virtual world design, development, and management. As an independent consultant, he has worked with many of the major online game companies in the U.K. and the U.S. over the past 30 years. His 2003 book, Designing Virtual Worlds , has established itself as a foundation text for researchers and developers of virtual worlds alike. His Player Type theory is taught in game design programmes worldwide (he appears in examination questions!). His interests are directed mainly virtual worlds, particularly Massively Multiplayer Online Role-Playing Games (MMORPGs, or MMOs), but cover all aspects of game design. He is keen to see AI used for non-player characters in MMOs (his PhD is in AI), and his current work considers the long-term moral and ethical implications of this. They’re maybe not what you might think they were at first glance… rabartle@essex.ac.uk Email Mastodon https://mud.co.uk/richard/ Other links Website https://www.linkedin.com/in/richardbartle/ LinkedIn BlueSky Github Themes Design & Development Game AI Player Research - Previous Next

< Back James Gardner University of York iGGi PG Researcher I am a third-year PhD student at The University of York, specialising in computer vision and machine learning for 3D scene understanding. Supervised by Dr William Smith, my research focuses on neural-based vision and language priors in inverse rendering and scene representation learning. I'm particularly interested in neural fields, generative models, 3D computer vision, differentiable rendering, geometric deep learning, multi-modal models, and 3D scene understanding in general. My research has been recognised with publications at prestigious conferences including NeurIPS and ECCV. Currently, I am working as a research fellow on the ALL.VP project, funded by BridgeAI and Dock10, developing relightable green screen performance capture using deep learning and inverse rendering techniques. This work aims to provide greater creative control to film and TV productions without requiring expensive LED volumes or post-production. I hold an MEng in Electronic Engineering from The University of York, for which I was awarded the IET Prize for outstanding performance and the Malden Owen Award for the best-graduating student on an MEng programme. A description of James' research: My research lies at the intersection of computer vision, machine learning, and 3D scene understanding, with a particular focus on neural-based approaches and the integration of vision and language priors. My work spans a range of topics including neural fields, generative models, differentiable rendering, and geometric deep learning. A key theme in my research is the use of 3D inductive biases for inverse rendering, addressing challenges such as illumination estimation, albedo/geometry disentanglement, and shadow handling in complex outdoor scenes. I've made contributions in creating a rotation-equivariant neural illumination model and spherical neural models for sky visibility estimation in outdoor inverse rendering. Additionally, my work extends to learning rotation-equivariant latent representations of the world from 360-degree videos, aimed at advancing the field of 3D scene understanding and developing models with an understanding of core physical principles such as object permanence. Through my research, I aim to build computer systems capable of deeply comprehending the 3D world, utilising self-supervised, generative, and non-generative approaches to push the boundaries of what's possible in computer vision and scene representation learning. james.gardner@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/jadgardner/ LinkedIn BlueSky https://jadgardner.github.io/ Github Featured Publication(s): The Sky's the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and Outside-In Visibility 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. claudio.guarnera@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/cvpr/member/claudio/ Other links Website https://www.linkedin.com/in/giuseppe-claudio-guarnera LinkedIn BlueSky Github Themes Applied Games Creative Computing - 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