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< Back Dr Agnieszka Lyons Queen Mary University of London Supervisor Agnieszka Lyons is a linguist and discourse analyst specialising in digitally mediated communication and multimodal communication, particularly across geographic distance. She explores the ways in which users of digital media construct their digitally mediated personae, particularly from the perspective of performance of the embodied selves, entering intersubjective spaces through verbal and non-verbal discourse and creating the feeling of physical and social presence across geographical distance. This can include multimedia sharing, avatar design, textual representation of nonverbal content, and others. She is particularly interested in supervising students with a communication, HCI, social and behavioural sciences background on the following topics: Player experience Player in-game interaction Construction of alternative personae Performance of player identities a.lyons@qmul.ac.uk Email Mastodon https://agnieszkalyons.wordpress.com/ Other links Website https://www.linkedin.com/in/agnieszka-lyons-3831592/ LinkedIn BlueSky Github Themes Player Research - Previous Next

< 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

< Back Dr Yizhao Jin Queen Mary University of London iGGi Alum Currently a student at Queen Mary University of London (QMUL), I have delved deep into the realms of artificial intelligence and game design. With a passion for understanding the complexities behind real-time strategy (RTS) games and their dynamic, unpredictable nature, I have committed myself to contribute novel insights to this domain. Research: My primary research area is Hierarchical Reinforcement Learning (HRL) for Real-Time Strategy (RTS) games. RTS games, known for their intricate mechanics and vast decision spaces, present a formidable challenge for traditional AI approaches. By employing HRL, I aim to develop agents that can not only understand the multi-layered tactics and strategies of these games but also learn to adapt to ever-changing game scenarios efficiently. The main objectives of my research are: Better Generalization: To create agents that can seamlessly transition between different RTS games or various maps within the same game without extensive retraining. This involves understanding common strategic threads across multiple game domains. Efficient Training: RTS games are inherently time-consuming due to their vast decision spaces and prolonged gameplay. My research seeks ways to optimize the training process, ensuring that AI agents can learn faster and with fewer computational resources. acw596@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://github.com/decatt Github Supervisors: Prof. Greg Slabaugh Prof. Simon Lucas Themes 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 Mastodon Other links Website LinkedIn BlueSky Github Themes Game Audio Player Research - Previous Next

< Back Peyman Hosseini Queen Mary University of London iGGi PG Researcher Peyman is interested in using his computer science knowledge to support society's well-being. Raised in a family where almost everyone’s work is somehow related to mathematics and its applications, he became passionate about algorithms and combinatorics from an early age. This prompted him to pursue an undergraduate degree in computer engineering with a focus on IT and AI. This background led him to start his PhD at IGGI on building more powerful yet efficient Natural Language Processing models for analysing textual data, a rich and abundant source of gaming feedback. A description of Peyman's research: Peyman's research focuses on advancing deep learning architectures for natural language processing and building tools on top of state-of-the-art models. To contribute to the fundamental understanding and practical application of deep learning in natural language processing, focusing on efficiency and effectiveness, he pursues two main objectives: Designing more efficient models that match or surpass state-of-the-art performance with fewer parameters. Systematically analyzing language models to develop solutions that enhance their effectiveness for end-users, such as game studios. His recent accomplishments towards these goals include: 1. Developing novel attention mechanisms: 1.1 Optimized Attention: 25% parameter reduction 1.2 Efficient Attention: 50% parameter reduction 1.3 Super Attention: 25% parameter reduction with significant performance improvements in language and vision tasks 1.4 All mechanisms demonstrate comparable or superior performance to standard attention across various inputs. 2. Designing and training Hummingbird , a proof-of-concept small language model using Efficient Attention, available on HuggingFace. 3. Conducting a study on large language models' limitations in analyzing lengthy reviews for basic NLP tasks. Proposed solutions offer substantial performance improvements while reducing API costs by more than 90%. s.hosseini@qmul.ac.uk Email Mastodon https://peymanhosseini.net/ Other links Website https://www.linkedin.com/in/peyman-hosseini1 LinkedIn BlueSky https://github.com/Speymanhs Github Supervisors: Dr Ignacio Castro Prof. Matthew Purver Featured Publication(s): Cost-Effective Attention Mechanisms for Low Resource Settings: Necessity & Sufficiency of Linear Transformations Efficient solutions for an intriguing failure of llms: Long context window does not mean LLMs can analyze long sequences flawlessly Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks You Need to Pay Better Attention: Rethinking the Mathematics of Attention Mechanism GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks Lon-ea at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction GRACER: Improving the Accuracy of RACER Classifier Using A Greedy Approach Themes Game AI Player Research - Previous Next

< Back Dr Maximilian Croissant University of York iGGi Alum I’m a psychology researcher, writer and game designer, exploring our emotional connection with games and creating games with purpose. Coming from a B.Sc. and M.Sc. in psychology and neuroscience, I’m now at the intersection of emotion research, design, and human-computer interaction and try to build design-oriented solutions for adapting game content to affective data. My project will include theoretical groundwork, investigating the emotional relationship between player and games and from there build an affective fear-focused VR horror game with specific and practical solutions in terms of emotion measurement, modelling, and adaptation. The ultimate goal is to help fill knowledge gaps that currently hold us back on making commercially viable affective games and provide tools to design games for a deep emotional impact. I’m also the Co-Founder of Vanilla Noir, a small studio working on applied games that aim to promote well-being and satisfying user experiences. For me, games are a great tool to explore psychological phenomena through interactions and the design and development of games based on applied psychology has great potential to help make the world a bit of a better place. mc2230@york.ac.uk Email http://www.maximilian-croissant.de/en Mastodon https://www.vanilla-noir.com Other links Website https://www.linkedin.com/in/maximilian-croissant LinkedIn BlueSky https://gitlab.com/MaximilianCroissant Github Supervisor(s) Dr Cade McCall Featured Publication(s): Advancing Methodological Approaches in Affect-Adaptive Video Game Design: Empirical Validation of Emotion-Driven Gameplay Modification Using Virtual Reality to Investigate the Influence of Sleep Deprivation on In-the-Moment Arousal During Exposure to Prolonged Threats Affective Systems: Progressing Emotional Human-Computer Interactivity with Adaptive and Intelligent Game Systems An appraisal-based chain-of-emotion architecture for affective language model game agents Emotion Design for Video Games: A Framework for Affective Interactivity Theories, methodologies, and effects of affect-adaptive games: A systematic review A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic Endocannabinoid concentrations in hair and mental health of unaccompanied refugee minors Progress in Adaptive Web Surveys: Comparing Three Standard Strategies and Selecting the Best Themes Design & Development Player Research - Previous Next

< Back Dr Sokol Murturi Goldsmiths iGGi Alum AI for game design: learning from designers For my PhD I am investigating how AI can help developers by learning to generate content in a similar fashion to the developers themselves. I envision a framework based on reinforcement learning, where an AI can learn a design policy for some content domain (e.g., FPS maps or platformer levels) by observing human designers. The AI would learn to take particular design actions in certain kinds of content states. Recent research into reinforcement learning has shown it is a powerful framework for developing complex agent behaviours and I believe there is a lot of potential to apply this work to game design. How would a human and artificial designer interact? Assume that an AI has learned to design a specific kind of content, such as a house, by observing human designers at work. A human designer could then partially develop some new content, and ask the AI to suggest some variations on it (see figure below), with both AI and human iterating on the design in a mixed-initiative interaction. The AI could learn from feedback from both the human designer and playtesting. As human feedback may not produce enough data for effective learning, the AI could perhaps extend this with data from simulated playtests. Game design decisions are often made with an expectation of how the player will react, and I could also look at how player models could be incorporated into the AI designer. In a reinforcement learning approach, the state could represent content+player, and the AI could learn to take design actions aimed a specific types of player. Developers could use this framework to develop content targeted at an individual player's style. Moreover, if the AI has learned something about how the human designer creates content, it can then be used live during the game to modify game elements in response to player interaction. Developers could set up modular levels, giving the AI the ability to adapt certain areas with content generated specifically to match the player. smurt001@gold.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Nathan John Queen Mary University of London iGGi Alum After graduating with a MEng in Computer Science from the University of Bristol, Nathan joined the games industry as a programmer, working for Climax Studios, Gaming Corps and Freejam, before moving to a career as a general software engineer, while still developing indie games on the side. His experiences across a range of industries sparked a passion for testing, and left him wondering if there were was to improve the automated testing in game development. Borne from an experiment Nathan had performed training AIs to play his indie game WarpBall, in which he found the agents solved for exploits in the authored AI rather than playing the game well, his research project proposes a novel method for improving the quality of behaviour of human authored agents by pitting them against trained agents and observing what bad behaviours/exploits the trained agents reveal. Authored agents refer to AI agents whose actions are explicitly designed by programmers using traditional techniques such as Utility functions, Behaviour Trees and state machines; trained agents refer to agents whose behaviour is learned by playing many games against the authored agents. n.m.john-mcdougall@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/vethan4/ LinkedIn BlueSky Github Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Themes Design & Development Game AI - Previous Next

< Back Dr David Gundry University of York iGGi Alum Using Applied Games to Motivate Speech Without Bias (Industry placement Lightspeed Research) Eliciting linguistic data faces several difficulties such as investment of researcher time and few available participants. Because of this, many language elicitation studies have to make do with few subjects and coarse sampling rates (measured in months). It would be ideal if a game could crowd-source relevant linguistic data with frequent, short game sessions. To this end, David’s research is looking into how games shape and elicit players’ linguistic behaviour. The established design patterns of gamification do not apply to a domain that lacks a ‘correct’ answer like language or personal beliefs and attitudes. David’s research shows how a player’s strategic goals will systematically bias data collection. It also shows how to design around this. The conclusion: The player’s choice of how to express a given datum must be strategically irrelevant in the game. David can remember the halcyon days when he had the free time to play games. Now he’s doing a PhD and has a one-year-old. He has an background in linguistics. He loves writing expressive code and designing clever little games. He wants to show that research games can be fun, not just effective. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Trading Accuracy for Enjoyment? Data Quality and Player Experience in Data Collection Games Designing Games to Collect Human-Subject Data Validity threats in quantitative data collection with games: A narrative survey Busy doing nothing? What do players do in idle games? Intrinsic elicitation: A model and design approach for games collecting human subject data Themes Applied Games - Previous Next

< Back Dr Fiona McNab University of York Supervisor During a postdoc at the Karolinksa Institute in Stockholm, Fiona investigated working memory and attention, providing empirical support for a role for the basal ganglia in the control of access to working memory and identification of changes in the dopamine system related to working memory training. At The Wellcome Trust Centre for Neuroimaging, UCL, with a Wellcome Trust Career Development Fellowship, she designed the working memory game in the large-scale smartphone study; “The Great Brain Experiment ”, leading to studies of different types of distraction in younger adults as well as in healthy ageing. In 2013 she moved to Birmingham University, where she conducted fMRI and behavioural studies of attention and working memory, and behavioural studies of the effects of competition on working memory. Fiona is now a lecturer in the Department of Psychology at the University of York. She is using fMRI and behavioural studies to investigate what limits working memory, how different types of distractors are successfully ignored and how working memory changes through development, with healthy aging, as well as in certain patient groups. Part of her work uses data from a new set of working memory games, which are currently available to play (York Memory Games, YORMEGA ). She is particularly interested in supervising students on the following topics: Understanding the limitations of working memory and the role of attention using games Understanding age-related changes in cognition using games, Cognitive training using games. Research themes: Game Design Games with a Purpose Player Experience Gamification Games for Cognition Research Games for Cognitive Training fiona.mcnab@york.ac.uk Email Mastodon https://www.york.ac.uk/psychology/staff/academicstaff/fm841/ Other links Website LinkedIn BlueSky Github Themes Applied Games - Previous Next

< Back Sarah Masters University of York iGGi PG Researcher Available for post-PhD position Sarah is an artist, game developer and researcher. They have an MA in Indie Game Development from Falmouth University (Distinction), where they created the city-building card game Eudaimonia. They are an active part of the games community taking part in game jams and setting up their own commercially focused studio. Sarah's work takes a research through design approach making and exploring games as an art form for change, collaborative design, speculative futures including 'ecopunk' and how we design games to meaningfully engage and entertain. Alongside a portfolio of games, their previous work includes running a workshop on Solarpunk vs Grimdark concepts. Their work also explores sustainable design and development practices to create emotional, engaging and meaningful experiences that can be a part of a greener industry and engage in climate change conversation. sarah.masters@york.ac.uk Email https://mastodon.gamedev.place/@sarah https://sarahdotgames.itch.io/ Mastodon https://sarah.games/ Other links Website https://www.linkedin.com/in/sarah-games/ LinkedIn BlueSky https://github.com/Impalpably Github Featured Publication(s): Radical Alternate Futurescoping: Solarpunk versus Grimdark Radical Alternate Futurescoping: Solarpunk versus Grimdark Better Dead than a Damsel: Gender Representation and Player Churn Themes Applied Games Design & Development Player Research Eudaimonia: A solarpunk city-building choice and consequence game - Save the world in eight years!: Fatalis - a witchy gardening game: Previous Next

< Back Dr Siamak Shahandashti University of York Supervisor Dr Siamak Shahandashti is a Lecturer (Assistant Professor) in Cyber Security Siamak has extensive experience in designing cryptographic solutions to enhance security and privacy for applications such as electronic voting, auctions, and biometric authentication systems. He has also worked on the security and privacy of password managers, IoT devices, mobile phone sensors, contactless payment, and paper fingerprinting. Siamak is interested in designing systems for improving security and privacy that are easy to use and accessible. He is working on designing usable password strength meters and human verifiable cryptographic codes. Siamak is a core member of the York Interdisciplinary Centre for Cyber Security and an expert fellow of the UK Network on Security, Privacy, Identity, and Trust in the Digital Economy (SPRITE+) He is a co-inventor on multiple patents including the first verifiable e-voting system trialled in the UK (patents US15582447, GB1607597) and paper fingerprinting (patent US15972922) with applications in banknote security. He led the design of the broadcast encryption deployed in millions of Thales’s Pay TV products worldwide. Siamak was part of teams who found vulnerabilities and fixed several systems, including the ISO/IEC11770-4 standard for password-based key exchange used in billions of devices, major mobile browser (Chrome, Firefox, Opera, Safari) sensor access policies, and Bitcoin's Payment Protocol used by 100k+ merchants. He is particularly interested in supervising students on the following topics: Using gamification to improve security and privacy in applications such as authentication and human verification Investigating and improving security and privacy in game environments Research themes: Gamification Games with a Purpose Game Security Game Privacy Game Analytics siamak.shahandashti@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/~siamak Other links Website https://au.linkedin.com/in/siamakfs LinkedIn BlueSky Github Themes Applied Games - Previous Next