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- Daniel Hernandez
< 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 Mastodon https://danielhp95.github.io Other links Website https://www.linkedin.com/in/dani-hernandez-perez-1106b2107 LinkedIn BlueSky https://github.com/Danielhp95 Github 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
- Prof David Beer
< 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
- Doruk Balci
< Back Doruk Balcı University of York iGGi PG Researcher Available for placement I am a game maker interested in the relationship between player creativity and game design. My work is centered around the transformative capabilities of players to invent their own metagames and play-practices, and how to support this through game design. My other interests include: drawing, literature, making zines and browser games, and playing with tools I don’t really understand. Designing for Appropriative Play How do we make games which we want to be messed with, changed fundamentally beyond our expectations in play? How do we make up rules that are intended to be bent, changed or broken? Why would we want that? Play practices that transform structures, subvert expectations and re-define their contexts are celebrated in many aspects of culture and can lead to personal and meaningful experiences. Yet research on this topic from a game design perspective has been scarce. In my project, I am exploring how we can design game systems that invite players to assume ownership of their play-practices through exploring alternative paradigms of game design. doruk.balci@york.ac.uk Email Mastodon https://fuzul.itch.io Other links Website https://www.linkedin.com/in/doruk-balc%C4%B1-19749a151 LinkedIn https://bsky.app/profile/dorukb.bsky.social BlueSky Github Supervisor: Dr Jo Iacovides Themes Design & Development Player Research - Previous Next
- Dr Zoe Handley
< Back Dr Zoe Handley University of York Supervisor Zoe Handley is a Senior Lecturer (Associate Professor) in Language Education. She is an interdisciplinary researcher, with a background in language technology, who recognizes the value of quantitative as well as qualitative work in this area. Her earlier work focused on the evaluation of speech synthesis for use in language learning and teaching. Since then she has carried out a systematic review of evidence for the use of technology to support English language learning in primary and secondary schools and supervised a number of theses evaluating applications of technology for language learning. These have typically explored the use of web 2.0 and Computer-Mediated Communication (CMC) technologies. Further to this she is interested in how learners autonomously use technology to support their learning in contexts such as study abroad. Zoe is currently particularly interested in teacher thinking in relation to the integration of technology to support language learning and developing and evaluating training to support teachers in making decisions about what technologies to integrate into their teaching, for what purposes and how. Zoe welcomes applications from PhD students interested in designing and evaluating educational activities that harness the affordances of digital technologies to create conditions and engage learners in processes that are known to support language learning. zoe.handley@york.ac.uk Email https://sites.google.com/york.ac.uk/pivotal-group/about Mastodon https://www.york.ac.uk/education/our-staff/academic/zhandley/ Other links Website https://www.linkedin.com/in/zoe-handley-a730b58/ LinkedIn BlueSky Github Themes - Previous Next
- Timea Farkas
< Back Dr Timea Farkas Goldsmiths iGGi Alum Timea is a researcher striving to understand how people engage with technologies—broadly defined—in their everyday lives, and how new technologies can enhance people's experiences of play, creative expression, and beyond. She has always been drawn to learning new things, with a background ranging from creative arts through games to science, which allows her to apply an interdisciplinary outlook towards research. She holds an MA in Sonic Arts from the University of Sheffield and has graduated with a First Class (Hons) degree in music composition and technology with a special award for outstanding achievement and collaboration. A description of Timea's research: This research project centres around understanding board game players' relationship with the immersive capabilities of hybrid board games - board games with a digital component - through finding novel interactions which strengthen the sensory elements of tabletop games. By focusing on physical board game pieces as alternative input devices to touch screens, the goal is to explore the design space of analogue-digital hybrids with a player-centric approach. farkasmarimba@gmail.com Email Mastodon Other links Website https://www.linkedin.com/in/timeafarkas/ LinkedIn BlueSky Github Featured Publication(s): Exploring the Design Space of Analogue-Digital Hybrid Boardgames Using a Player-Centric Approach How Boardgame Players Imagine Interacting With Technology The Effects of a Soundtrack on Board Game Player Experience A Grounded Analysis of Player-Described Board Game Immersion Themes Creative Computing Immersive Technology Player Research - Previous Next
- Dr William Smith
< 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 william.smith@york.ac.uk Email Mastodon https://www-users.cs.york.ac.uk/wsmith/ Other links Website https://www.linkedin.com/in/william-smith-b5421a70/ LinkedIn BlueSky https://github.com/waps101 Github Themes Creative Computing Design & Development Game AI Player Research - Previous Next
- Rob Homewood
< Back Rob Homewood Goldsmiths iGGi Alum Personalised Aesthetics for Games The worldwide games industry is a huge market and as the spectrum of people who spend time playing games increases, there is more and more competition to create games that capture the attentions of a wide audience. Whilst games have been traditionally designed with specific cultural demographics in mind, a game that could dynamically match the cultural values of a range of demographics would maximize its potential market. Robert’s research looks at developing techniques for procedurally generating dynamic game assets that can be viewed as being relevant at a ‘per player’ level. He aims to do this by actively profiling a player’s social networks and building up a picture of the cultural references with which they identify. This knowledge could then be used to create game assets that match an aesthetic the player would likely feel comfortable with, allowing a more flexible decoupling between game mechanics and aesthetic during the design process. Designers could then focus on creating interesting game mechanics that could work in a variety of settings and the system would fill in the aesthetic detail based on the requirements of the individual player at run-time. Having studied in five countries, Robert is currently undertaking a PhD at Goldsmiths, University of London where he is part of the EPSRC funded IGGI (Intelligent Games and Games Intelligence) program. He also holds a Bachelor’s degree in Game Design and Production Management from the University of Abertay Dundee which included a year of studies at the George Mason University Computer Game Design Program. He also spent a year studying Serious Games at Masters level at the University of Skövde in Sweden (which has the longest running Serious Games program in the world). Robert has an active interest in the media arts field and has exhibited his work in three countries. Please note: Updating of profile text in progress Email Mastodon Other links Website https://www.linkedin.com/in/robert-j-homewood-36906132/ LinkedIn BlueSky Github Themes Player Research - Previous Next
- Nathan Hughes
< Back Dr Nathan Hughes University of York iGGi Alum Nathan Hughes is a player experience researcher who focuses on how player make choices within games. Specifically, the work explores open world games such as Skyrim and the Witcher 3, as these games allow players a vast amount of choice with little restrictions on how and when these are made. However, little research has considered these choices, so little is known about how players experience choice in open world games. Therefore, research questions for this work include; why do players choose not to pursue the main quest? What do players choose to do instead? When and how do they make this decision? His background is in psychology, and so asks these questions from a psychological perspective. The aim is to uncover how the process of choosing unfolds, and how this is influenced. In turn, this may allow reflections on how the decision-making process operates - by analysing choices within open world games, a more controlled (but still intrinsically motivating) setting can be studied. ngjhughes@gmail.com Email Mastodon https://faethfulexplorations.wordpress.com Other links Website https://www.linkedin.com/in/nathan-hughes-1035b611b/ LinkedIn BlueSky Github Supervisor Prof. Paul Cairns Featured Publication(s): Clinicians Risk Becoming "Liability Sinks" for Artificial Intelligence Understanding specific gaming experiences: the case of open world games The need for the human-centred explanation for ML-based clinical decision support systems Growing Together: An Analysis of Measurement Transparency Across 15 Years of Player Motivation Questionnaires Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units Growing together: An analysis of measurement transparency across 15 years of player motivation questionnaires Opening the World of Contextually-Specific Player Experiences No Item Is an Island Entire of Itself: A Statistical Analysis of Individual Player Difference Questionnaires Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities Themes Player Research - Previous Next
- Philip Smith
< Back Philip Smith Queen Mary University of London iGGi PG Researcher Available for placement I was born and raised in Bermuda, a small island in the Atlantic Ocean with an approximate population of 65,000 people. I finished my undergraduate degree in Computer Science with a Specialist in Game Design at the University of Toronto. For my Master's degree, I studied Computer Games Technology at City, University of London. My goal is to help expand the use of video games from purely recreational activities to viable avenues for aiding in real world problems. A description of Philip's research: My research will be focusing on maximizing player engagement in gamified citizen science as a continuation of my Master's thesis. 'Citizen science' is the practice of employing volunteers from the general public for the collection and/or processing of data with respect to a scientific project. Gamified citizen science projects have relied upon prolonged engagement from volunteers, but the number of long-term participants have been unsatisfactory in current projects. This project attempts to address the lack of sufficient volunteer engagement in gamified citizen science projects. The aim is to build a framework meant to guide game designers in creating an engaging citizen science video game based on the values set by Self-Determination Theory (SDT). These values adhere to the theory’s concept of intrinsic and extrinsic motivators of engagement. Intrinsic motivation relies on the factors of player autonomy, competence, and relatedness during gameplay. Extrinsic motivation relies on external incentives to core gameplay such as in-game rewards. As part of my research, I am evaluating multiple game design frameworks focused on Applied Games and identifying the merits and flaws each have when applied to a citizen science context. The information I gather will formulate a prototype of the Framework that will be iterated upon through design workshops, development, and playtesting. p.c.smithii@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://pjsmith97.github.io/ Github Themes Applied Games Design & Development - Previous Next
- Charlie Ringer
< 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. charles.ringer@york.ac.uk Email Mastodon https://www.charlieringer.com Other links Website https://www.linkedin.com/in/charlie-ringer/ LinkedIn BlueSky https://www.github.com/charlieringer Github 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
- Dr Patrik Huber
< 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 patrik.huber@york.ac.uk Email Mastodon https://www.patrikhuber.ch/ Other links Website https://www.linkedin.com/in/patrik-huber/ LinkedIn BlueSky https://github.com/patrikhuber Github Themes Applied Games Esports Game Data Immersive Technology Player Research - Previous Next
- Nicole Levermore
< 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













