< Back Dr William Smith University of York Supervisor William Smith is a Reader in the Computer Vision and Pattern Recognition research group in the Department of Computer Science at the University of York. He is currently a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow and an Associate Editor of the journal Pattern Recognition. His research interests span vision, graphics and ML. Specifically, physics-based and 3D computer vision, shape and appearance modelling and the application of statistics and machine learning to these areas. The application areas in which he most commonly works are face/body analysis and synthesis, surveying and mapping, object capture and inverse rendering. A wide variety of tools and areas of maths are often useful in his research such as: convex optimisation, nonlinear optimisation, manifold learning, learning/optimisation on manifolds, computational geometry and low level computer vision (e.g. features and correspondence). He leads a team of five PhD students and one postdoc and has published over 100 papers, many in the top conferences and journals in the field. He was General Chair for the ACM SIGGRAPH European Conference on Visual Media Production in 2019 and is Program Chair for the British Machine Vision Conference in 2020. Research themes: Game AI Game Design Computational Creativity Graphics and rendering Content creation Email william.smith@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Design & Development Game AI Player Research - Previous Next

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

< Back Dr Daniel Hernandez University of York iGGi Alum With the games industry as his target, Daniel Hernandez’s main research objective is to design and implement algorithms that, without any prior knowledge, generate strong gameplaying agents for a wide variety of games. To tackle this “from scratch” learning, he uses, and contributes to, the fields of Multiagent Reinforcement Learning, Game Theory and Deep learning. Self-play is the main object of study in his research. Self-play is a training scheme for multiagent systems in which AIs are trained by acting on an environment against themselves or previous versions of themselves. Such training scheme bypasses obstacles faced by many other training approaches which rely on existing datasets of expert moves or human / AI agents to train against. Daniel’s hope is that further development in Self-play will allow game studios of all sizes to generate strong AI agents for their games in an affordable manner. A storyteller by nature, Daniel has a strong track record of outreach through talks and workshops both in the UK and internationally. By sharing his journey, insights and discoveries he hopes to both inspire and instruct students, researchers and developers to realise the potential that Reinforcement Learning has to improve the games industry. His passionate work on Machine learning goes beyond crafting strong gameplaying agents. He sees the potential of using AI to simplify and automate a wide range of tasks in the games industry. He has led successful projects which used machine learning aimed at automating multiagent game balancing to alleviate the burden of manual game balancing. Daniel received an MEng in Computing: Games, Vision & Interaction from Imperial College London. Wanting to combine the power of AI and the creativity of videogames, Daniel began a PhD journey to explore the misty lands of Multi Agent Reinforcement Learning (MARL). Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): A comparison of self-play algorithms under a generalized framework A generalized framework for self-play training Metagame Autobalancing for Competitive Multiplayer Games Themes Game AI Player Research - Previous Next

< Back Prof. Anders Drachen Supervisor Anders Drachen, PhD, (born 1976) is a Professor at the Department of Computer Science, with Digital Creativity Labs and Weavr at the University of York (UK). His work in games research is focused on user behavior, user experience and audience engagement and the application of data science, information systems modelling, business intelligence, design and Human-Computer Interaction in these domains. His research and professional work are carried out in collaboration with companies across the Creative Industries, from big publishers to indies. He is recognized as one of the most influential people in his domains of work and have authored over a hundred publications with international colleagues across industry and academia. Having lived and worked on four different continents, Anders Drachen has had the mixed pleasure of fending off three shark attacks in Africa and Australia. He is also the youngest Dane in history to publish a cooking book – dedicated to ice cream. Research themes: Data Science, Analytics, Machine Learning in Interactive Media Big Data, behavior- and social media analytics in the Creative Industries Data Mining and Business Informatics in the Creative Industries Data-Driven Storytelling and Audience Engagement Games User Research and User Experience in Games Data-Driven Design and Development Human-Computer Interaction Esports and Sports Analytics Behavioral/Market Analytics and Business Intelligence Entrepreneurship in the Creative Industries Blockchain and Cryptocurrencies Email anders.drachen@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Design & Development Esports Game Data Player Research - Previous Next

< 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). Email tom.collins@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Esports Game AI Game Audio Game Data Player Research - Previous Next

< Back Dr Martin Balla Queen Mary University of London iGGi Alum Before starting his PhD Martin studied Computer Science at the University of Essex. His main interest is artificial intelligence and its application to all sort of problems ranging from computer vision to game AI. He likes spending his spare time with various activities which mainly involves reading, playing video games and skateboarding. Martin's PhD thesis focuses on Reinforcement Learning agents that can adapt to changes in the reward function and/or changes in the environment. His work investigates how agents can transfer their knowledge to changes in the environment, such as new rewards, levels or visuals. Outside of his main research direction, Martin is involved with the Tabletop games framework (TAG), which is a collection of various tabletop games implemented with a common API with a focus on various game-playing agents (including RL). TAG brings various challenges to RL agents compared to search-based agents, such as complex action spaces, unique observation spaces (various embeddings), multi-agent dynamics with competitive and collaborative aspects, and lots of hidden information and stochasticity. Email m.balla@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): Multi-task Reinforcement Learning for Adaptive Agents Program Committee and Subreviewers Tiny Moves: Game-based Hypothesis Refinement PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games TAG: Pandemic Competition Task Relabelling for Multi-task Transfer using Successor Features TAG: A tabletop games framework Design and implementation of TAG: a tabletop games framework Evaluating generalisation in general video game playing Evaluating Generalization in General Video Game Playing Analysis of statistical forward planning methods in Pommerman Themes Game AI - 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. Email daniel.gomme@yahoo.co.uk Website LinkedIn Mastodon BlueSky GitHub Other Link 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 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 Filip Sroka Queen Mary University of London iGGi PG Researcher Available for placement Filip is a Computer Science researcher specialising in Game AI. He acquired an Integrated Masters in Computer Science from Queen Mary University of London and is pursuing a PhD in Game AI with iGGi. An avid LEGO collector and investor, Filip brings a unique blend of technical and creative abilities to his work. He is excited about the potential of the Metaverse and is driven by the role of technology in shaping its future. A description of Filip's research: His research explores the integration of Dynamic Difficulty Adjustment (DDA) and Procedural Content Generation (PCG) into VR rhythm games to optimise motor learning and skill acquisition. By leveraging learning theories, the project creates personalised, adaptive training environments. Beyond commercial gaming, this framework demonstrates how adaptive systems can be used to maximise engagement and training efficiency, offering high-value insights for game developers, immersive tech creators, and the digital health and fitness industry. Email f.sroka@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Alena Denisova Dr Laurissa Tokarchuk Dr Jeremy Gow Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Karl Clarke Queen Mary University of London iGGi PG Researcher Available for placement Karl Clarke is a PhD researcher focused on how virtual environments influence social interaction. He was born in England, grew up in the Middle East, and returned to the UK for university. He holds a Bachelor's and a Master's degree in Audio Technology. During the COVID-19 lockdown, he began exploring virtual reality after getting access to a headset, which led to a shift in focus toward social VR. He is now part of the Intelligent Games and Game Intelligence (iGGi) doctoral programme, where his research looks at how spatial layouts and group behavior are shaped by virtual environments in free-standing social settings. Outside of his research, Karl runs SONAR, a music group hosted in VRChat that uses social VR for live performance and shared listening experiences. Through this project, he has independently learned game development skills in 3D modelling, scripting, and a small amount of graphics programming. He is currently looking to collaborate with VR studios or social platforms working on immersive and social experiences. Email karl.clarke@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Themes Design & Development Immersive Technology Player Research https://www.youtube.com/watch?v=Ldehhn_LdhA Previous Next

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

< Back Zoë O’Shea Goldsmiths iGGi PG Researcher Zoë O’Shea is an Irish freelance games designer and artist, working on her thesis in game design and player psychology. Her previous qualifications include 3D Generalism, and an MA in Digital Game Design and Theory. She is endlessly curious about the meaning and value that technology can bring to the world, exploring the human experience as a core principle of her work. She firmly believes in the importance of creating a more joyful and inclusive world. Zoë has previously worked with a range of clients and companies including A Brave Plan, Surgent Studios, Transport for London (TfL) and LEGO. In 2019, Zoë was awarded a Digital Fellowship from the Royal Shakespeare Company (RSC) in collaboration with Magic Leap. Zoë worked with other creatives for a year to explore the future of theatre and Spatial Computing (Mixed Reality). The programme completed in Feb 2020, through the generous support of Magic Leap, the RSC, their technologists, industry partners, i2 Media Research and the University of Portsmouth. Currently, Zoë is working on completing her thesis while offering consultancy services for games and start-ups looking to expand their knowledge in soft aesthetics, tend & befriend game design and immersive technology. A description of Zoë's research: Tend & Befriend: A New Perspective on Player Psychology in Digital Games Many are familiar with the term "fight-or-flight" - a stress response activated when animals come into conflict with a stressor or threat. Less commonly known is "tend & befriend", an alternative theory of stress response which suggests that engaging with tending and affiliative behaviours under duress can soothe and protect us. This thesis investigates this phenomenon in digital games, with a focus on empirical data and design. Results demonstrate a consistent niche in the games industry for "tend & befriend" centric design and the value in synthesising previous design frameworks to create a intentional and polished experience for players. Email z.oshea@gold.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Richard Bartle Featured Publication(s): The impact of self-representation and consistency in collaborative virtual environments Themes Design & Development Immersive Technology Player Research - Previous Next