< Back Dr Ozan Vardal University of York iGGi Alum Ozan studied undergraduate psychology at the University of Groningen, and holds a master's degree in Performance Psychology from the University of Edinburgh, where he wrote theses on the dynamics of psychological momentum in sport competition and the decision-making of expert applied psychologists respectively. He has long been fascinated with the psychological mechanisms underpinning complex skills, owing to his own background as a classically trained musician and his previous work as a performance psychology consultant with competitive athletes. His primary research interests involve the behavioural and neural factors surrounding human learning and skilled performance. A description of Ozan's research: Ozan views games as behaviourally rich environments for the study of complex skills and human learning. The competitive and immersive nature of games encourages millions of players to develop profound skill over hours, days, and even years of practice. Ozan’s work takes advantage of large data repositories generated by such players to study how different patterns of practice result in differences in learning outcomes. He also uses experimental methods in his work, and is currently using neuroimaging methods (MEG) and modelling techniques to identify how shifts between different behavioural and neural states affect performance as people play Tetris. By using games as a vehicle to study psychology, Ozan aims to develop scalable solutions to studying human learning. He hopes for a future where the science of learning is sufficiently advanced, such that (artificial) trainers can recommend optimised practice schedules for motivated learners, in any performance domain. Please note: Updating of profile text in progress Email ov525@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Mind the gap: Distributed practice enhances performance in a MOBA game Themes Design & Development Esports Game Data - 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 Dr Patrik Huber University of York Supervisor Patrik Huber is a researcher, developer and entrepreneur, working on 3D face reconstruction and face analysis in images and videos using 3D face models. He is a Lecturer (Assistant Professor) in Computer Vision in the Department of Computer Science of the University of York, UK, and he’s the Founder of 4dface.io, a small start-up specialising in 3D face models and realistic 3D face avatars for professional applications. His research is focused on computer vision, in particular, he is interested in the question of how to robustly obtain a metrically accurate, pose-invariant 3D representation of a face from 2D images and videos. He is interested in face tracking, 3D face modelling, analysis and synthesis, metrically accurate 3D face shape reconstruction, inverse rendering, and combining deep learning with 3D face models. Patrik is particularly interested in supervising students with a strong background and interest in computer vision, machine learning, computer graphics, and modern C++/Python, on topics related to creating 3D face avatars of players for immersive playing and social experiences , and using face analytics for professional e-sports . Research themes: 3D face avatars for games AR/VR Serious games and social interaction Immersive 3D player experiences Game Analytics Games with a Purpose E-Sports Email patrik.huber@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Esports Game Data Immersive Technology Player Research - Previous Next

< Back Dr Carlos Gonzalez Diaz University of York iGGi Alum Carlos is finishing his PhD at the University of York. He holds an MSc in Serious Games at the University of Skövde (Sweden) and a BSc in Software Engineering (Spain). He is been closely connected with industry throughout his PhD, having worked in the last years for Microsoft Research, Sony Interactive Entertainment R&D, Musemio Ltd R&D and Goldsmiths, UoL; as well as done consulting for tech companies such as Unity Technologies. A description of Carlos' research: The purpose of my PhD research is to advance game technologies by democratising the use of ML techniques among non-experts through innovative tools and plugins for game engines. I developed ML specific visual scritping languages and used mixed-methods research approaches to understand how to better support developers in creating VR interactions and the challenges behind human-AI interaction. I had several technical jobs throughout my PhD, as my expertise is highly applicable in both industry and academia. Thanks to the broad range of expertise that I gathered through many years of industrial work and academic study, I can tackle the challenges emerging from the inter-disciplinary nature of modern work: where user psychology, immersive technology and artificial intelligence intersect. Please refer to my website for completely up-to-date information regarding publications. Feel free to reach out if you want more information or want to chat about my/your work. I am looking for positions starting on February 2023 onwards. Email carlos.gonzalezdiaz@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Sebastian Deterding Featured Publication(s): Prototyping by Moving for Virtual Reality Embodied, in-medium design of VR game motion controls using interactive supervised learning Automatic Game Tuning for Strategic Diversity Programming by Moving: Interactive Machine Learning for Embodied Interaction Design InteractML: Node Based Tool to Empower Artists and Dancers in using Interactive Machine Learning for Designing Movement Interaction Movement interaction design for immersive media using interactive machine learning Using Machine Learning to Design Movement Interaction in Virtual Reality Interactive machine learning for more expressive game interactions Making Space for Social Time: Supporting Conversational Transitions Before, During, and After Video Meetings InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media Interactive Machine Learning for Embodied Interaction Design: A tool and methodology Bodystorming in SocialVR to Support Collaborative Embodied Ideation Themes Creative Computing Design & Development Game AI Immersive Technology https://www.youtube.com/watch?v=taVry9IQUjE https://www.youtube.com/watch?v=kkKU3MyBspM https://www.youtube.com/watch?v=WHiPav2l5gA 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 Andrew James Wood University of York Supervisor I am an interdisciplinary researcher at the University of York. My background is in Mathematical Physics but my interests are now in applying computational and mathematical techniques to interesting problems, mostly in Biology. This includes such topics as collective motion (particularly in interaction networks and the role of noise) and microbiology (particularly in metabolism, industrial biotechnology, spatial structure and plasmid dynamics) as well as modelling naval conflicts and glycosylation. I have a natural interest in games and am interested in the interface between games and science, be that in using games to do, or disseminate, science or in utilising mechanisms and insights from research to inspire games. Research themes: Game Analytics Game Design Games with a Purpose Gamification Email jamie.wood@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development Game Data - 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 Debbie Maxwell University of York iGGi Research Collaboration Coordinator Supervisor Debbie is a lecturer in User Experience Design and Interactive Media at the Department of Theatre, Film and Television. Her background spans computing, HCI and Design and she currently teaches user experience (UX) design and design methods and critical design on the BSc Interactive Media programme. Her research focuses on the roles of traditional storytelling and engagement in digital contexts. I’m interested in the ways that people interact with and reshape technology through stories, as both method and artefacts, and across media. She is particularly focuses on applying design and stories across health and wellbeing and environmental design drawing on speculative design processes and approaches. Debbie uses interdisciplinary approaches that draw on a range of fields including Human Computer Interaction (HCI), ethnography, interaction design, social anthropology, and service design. Her research always involves working with communities using participatory methods. She is particularly interested in supervising students with a design or HCI background on the following topics: design of applied games for environmental education or knowledge exchange design and application of serious games to mental health and wellbeing contexts design and application of serious games to outdoor spaces, particularly cultural heritage settings Research themes: Games with a Purpose User experience design Design methods and ethnography Speculative design Email debbie.maxwell@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development Player Research - Previous Next

< Back Dr Jen Beeston University of York iGGi Alum + Supervisor Jen is currently working as a Lecturer in HCI in the Department of Computer Science (University of York) whilst writing up her PhD. She has a multidisciplinary background, from studying subjects such as environmental science and media production and having worked in various jobs such as grassland research, flood risk management, and plasterboard quality. She feels extraordinarily fortunate to have been able to do research into her lifelong hobby of playing digital games. In particular, Jen feels it is important that everyone has the opportunity to enjoy gaming should they wish. As such, her research has been aimed at exploring the experiences of people with disabilities in playing games, beyond how various technologies can support play. Jen’s research is focused particularly on the social experiences of players with disabilities in-game and within the broader gaming community. She has worked alongside the charity AbleGamers with the aims of investigating these player’s experiences of gaming, what effects alternative controls have upon play, and what it’s like for these players in multiplayer or online games. Jen is broadly interested in HCI, user experience, player experience, inclusivity, social play, and game communities. Outside of her work, she enjoys walking, thinking, reading, crochet, art, and tabletop roleplaying games. Email jen.beeston@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Validation and Prioritization of Design Options for Accessible Player Experiences Social experiences of people with disabilities in playing (in) accessible digital games Enabled players: The value of accessible digital games Accessible player experiences (APX): The players Characteristics and motivations of players with disabilities in digital games Perceptions of Telepresence Robot Form Themes Accessibility - Previous Next

< Back Dr Elena Gordon-Petrovskaya University of York iGGi Alum Elena specialises in novel forms of 'predatory' monetisation in digital games and its effects on players and is particularly interested in the links of game design to gaming disorder. She uses her background in psychology and human-computer interaction to take a player-centric perspective: developing knowledge bottom-up and working directly with players as the primary stakeholder. Her work spans ethics, wellbeing, and the lived experience of technology and its use. In her work so far, Elena has conducted a qualitative study with 1000+ players to create a taxonomy of microtransactions that players perceive to be unfair, aggressive, or misleading, and carried out a prevalence assessment of these techniques across the most popular desktop and mobile games. Most recently, her work discovered several types of harms which emerge from player interaction with games perceived as 'designed to drive spending'. Additionally, Elena has contributed to government calls for evidence around game regulation, given talks at seminar series and conferences, and collaborated on a variety of related topics, such as loot box spending, esports betting, and changes to gameplay during COVID. Email elepetrovs@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Prof. Sebastian Deterding Dr David Zendle Featured Publication(s): Learnings from the case Maple Refugee: A dystopian story of free-to-play, probability, and gamer consumer activism. Four dilemmas for video game effects scholars: How digital trace data can improve the way we study games Adapting and Enhancing Evolutionary Art for Casual Creation. The many faces of monetisation: Understanding the diversity and extremity of player spending in mobile games via massive-scale transactional analysis The relationship between psycho-environmental characteristics and wellbeing in non-spending players of certain mobile games Why microtransactions may not necessarily be bad: a criticism of the consequentialist evaluation of video game monetisation The lived experience of Internet Gaming Disorder: core symptoms, antecedents and consequences as based on a qualitative analysis of Reddit posts. Prevalence and Salience of Problematic Microtransactions in Top-Grossing Mobile and PC Games: A Content Analysis of User Reviews Predatory Monetisation? A Categorisation of Unfair, Misleading and Aggressive Monetisation Techniques in Digital Games from the Player Perspective Designing Personas for Expressive Robots: Personality in the New Breed of Moving, Speaking, and Colorful Social Home Robots A large-scale study of changes to the quantity, quality, and distribution of video game play during the COVID-19 pandemic How do loot boxes make money? An analysis of a very large dataset of real Chinese CSGO loot box openings Defining the esports bettor: evidence from an online panel survey of emerging adults The Battle Pass: a Mixed-Methods Investigation into a Growing Type of Video Game Monetisation Casual Creators in the Wild: A Typology of Commercial Generative Creativity Support Tools Not all fun and games: The design and evaluation of a game to increase intrinsic motivation in learning programming Exploring the multiverse of analysis options for the Addiction Stroop "These People Had Taken Advantage of Me”: A Grounded Theory of Problematic Consequences of Player Interaction with Mobile Games Perceived as “Designed to Drive Spending" Themes Player Research - Previous Next

< Back Dr Valerio Bonometti University of York iGGi Alum Game analytics and player psychology: creating reliable models of player motivation Motivation can be loosely defined as a process of the brain and the mind, capable of driving and deeply shaping human behaviour. Motivational processes are embedded in many everyday life situations, exerting their effects via a wide range of incentive mechanisms and objects. Understanding this process in a videogame context, however, requires a more holistic approach considering not just the incentive properties of the game but also the player personal characteristics. My project aims to create reliable cross-games models of player motivation taking into account the contribution of natural inter individual variability. This will be accomplished linking in-game behavioural data and psychological models via a hybrid approach, where findings from small scale experimental studies (hypothesis-driven) will guide the realization of large scale (data-driven) applications for predicting players' characteristics, future behaviour and motivation evolution. Being able to model player motivation and predict the trajectories of its evolution could possibly lead to personalized and dynamic engagement strategies able to adapt accordingly to the player characteristics and in-game behaviour. Achieving a similar goal would be of pivotal importance in industrial and gamification contexts. I obtained my bachelor degree in Psychological Science and my master degree in Clinical Psychology at Padova University (Italy). During my academic path I acquired knowledge in general psychology, cognitive psychology, psychophysiology, neuroscience and research methodology. After my master degree I spent a considerable amount of time as a research trainee, both abroad and in my country, always investigating the reward process and its effects in various contexts. During this period I worked on various projects across different fields ranging from psychophysiology, player research and game analytics. In my free time I enjoy practicing indoor climbing and travelling, I like figurative art in general and more specifically I’m a huge cinema and graphic-novel enthusiast. Supervisors: Prof. Anders Drachen, Dr Sam Devlin Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): From Theory to Behaviour: Towards a General Model of Engagement Modelling early user-game interactions for joint estimation of survival time and churn probability Predicting skill learning in a large, longitudinal MOBA dataset Mind the gap: Distributed practice enhances performance in a MOBA game Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data: A Representation Learning Approach Based on Artificial Neural Networks Themes Player Research - Previous Next

< Back Dr Poonam Yadav University of York Supervisor Dr Yadav research is focused on making the Internet of Things (IoT) and edge computing-based distributed systems resilient, reliable, and robust. This is an interdisciplinary research area that requires expertise in system design and integration along with knowledge of sensor systems, wireless networking, and domain and contextual understanding. To achieve resilience and reliability in the area of resource constraints and distributed systems, I focus on coordination and collaboration using interactions among machines, humans and data entities. These interactions could be categorized as machine-to-machine (M2M), machine-to-human (M2H), and human-to-data (H2D), and involve many challenges such as collaborative trust, privacy, legibility and accountability. Dr Yadav is an active reviewer of many top-tier ACM/IEEE IoT and networking conferences and journals. Dr. Yadav leads ACM-W UK professional chapter and is featured as "People of ACM Europe" and among the top ten N2Women Rising Star in Computer networking and communications in 2020. Research themes: E-Sports Use of IoT in Games Gamifications Citizen Science Email poonam.yadav@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Design & Development Esports Game Data - Previous Next