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< Back Dr Jo Iacovides University of York Supervisor Jo Iacovides, is a Lecturer in Computer Science at the University of York, UK. Her research interests lie in Human Computer Interaction with a particular focus on understanding the role of learning within the player experience, and on investigating complex emotional experiences in the context of digital play. In addition, she is interested in exploring how games and playful technologies can created for a range of purposes, such as education, citizen science, or wellbeing. She is an active member of the HCI and games community and serves on the Steering Committee for the annual CHI PLAY conference. She has received awards for a work on examining reflection and gaming (best paper, CHI PLAY 2018), evaluating serious experience in games (honourable mention, CHI 2015) and for the game Resilience Challenge, which encourages healthcare practitioners to consider how they adapt safely under pressure (first prize, 2017 Annual Resilience Healthcare Network symposium). She is interested supervising students that have a mix of qualitative, mixed method or design experience that they wish to apply to the study of digital games and playful technologies. Possible topics include exploring the effects of negative emotion in the context of playful approaches to persuasion; or examining how games can support wellbeing (particularly in relation to challenging life experiences). Research themes: Game Design Games with a Purpose Player Experience jo.iacovides@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/people/?group=Academic%20and%20Teaching%20Staff&username=ii Other links Website https://uk.linkedin.com/in/joiacovides LinkedIn BlueSky Github Themes Applied Games Design & Development Player Research - Previous Next

< Back Shopna Begum Queen Mary University of London iGGi Administrator iGGi Admin iGGi Administrator at QMUL Shopna is part of the iGGi Admin Team which is responsible for the smooth running of iGGi. In her role as iGGi QMUL Administator she provides administrative services and pastoral care to PhD students and assists the iGGi QMUL Manager in key aspects of the Centre's management. shopna.begum@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Gorm Lai Goldsmiths iGGi PG Researcher Inspired by the works of Karl Sims and William Latham as well games such as a Spore and No Man's Sky, Gorm's main work is focused on using artificial intelligence and machine learning to generate creatures for use in video games. Combining this with his background as a virtual reality pioneer, his full doctorate is looking into how mixed-initiative co-creative interfaces in vr can assist in creating procedural generated creatures for use in video games. As a stalwart of the game development community, Gorm ran the Danish chapter of the International Game Developer Association (IGDA) for 5 years, started the London Indie Game Developers meetup group which currently features almost 3000 members, co-founded the Nordic Game Jam, as well as the Global Game Jam. The Global Game Jam has been recorded into the Guinness Book of World Records, and has more participating countries than the Winter Olympics. Gorm is a games industry veteran who has worked on 17 commercial video games since 2004, and has spoken at numerous games industry conferences such as GDC, Nordic Game & Develop Brighton. Gorm is a student at Goldsmiths, University of London, where is he is supervised by William Latham and Frederic Fol Leymarie. lai.gorm@gmail.com Email Mastodon https://gormlai.github.io Other links Website https://www.linkedin.com/in/gormlai/ LinkedIn BlueSky https://github.com/gormlai Github Supervisor(s): Prof. William Latham Featured Publication(s): Formal Constraints and Creativity: Connecting Game Jams, Dogma ’95, the Demo Scene, OuBaPo, and Renga poets What Is a Game Jam? The Dark Side of Game Jams On Mixed-Initiative Content Creation for Video Games Two decades of game jams Virtual Creature Morphology‐A Review Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry Introducing: the game jam license Trends in organizing philosophies of game jams and game hackathons The global game jam for teaching and learning Gplayer A compression method for spectral photon map rendering Themes Creative Computing Design & Development Game AI - Previous Next

< Back Oliver Roughton University of York iGGi Administrator iGGi Admin Based in York alongside Tracy and Helen I act as a Point of contact for iGGi PGRs and provide administrative support in the implementation of iGGi procedures. iGGi PGRs are most likely to hear from me in relation to conference/kit funding and travel bookings for the taught modules and other iGGi events. As well as my admin work I am a part-time PhD student (not with iGGi) and spend much of my free time knitting. oliver.roughton@york.ac.uk Email https://www.instagram.com/klaus.the.magnificent/ Mastodon Other links Website LinkedIn BlueSky Github Themes Previous Next

< Back Ruizhe "Jay" Yu Xia Queen Mary University of London iGGi PG Researcher Available for placement Ruizhe has bachelor degrees in Mathematics and Physics and a master's degree in Artificial Intelligence. After a short time as a consultant he decided to pursue research into what got him into AI in the first place: game agents. He enjoys games of all kinds, but strategy and RPG games occupy a sizeable portion of his collection. AI agents that perform with superhuman skill in increasingly complex games have appeared in recent years, but these agents are not always useful to game developers. Players within a game exhibit significant variance in their skill levels and play styles. Therefore, game agents with similar variance would better represent the player base. The research Ruizhe proposes will focus on three areas: measuring skill and play styles, developing game agents that mimic a range of human play styles and skill levels, and making these agents human-like. Upon successful completion, this research will improve the game development process via automated playtesting and will enable the development of AI agents that are more engaging and interactive. r.yuxia@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/ruizheyuxia/ LinkedIn BlueSky Github Supervisor: Prof. Simon Lucas Dr Jeremy Gow Themes Game AI Game Data - 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. lukebfarrar@gmail.com Email Mastodon Other links Website https://www.linkedin.com/in/luke-farrar-3967b3243/ LinkedIn BlueSky Github Supervisors: Dr Miles Hansard Dr Patrik Huber Dr James Walker Themes Immersive Technology - Previous Next

< Back Stefan Stoican University of Essex iGGi Alum Understanding human crowd behaviour via virtual environments: feedback loop between games & research This project uses computer game experiments to explore decision-making in a virtual evacuation simulation. Can one be “saved by the gaze”? Currently, Stefan is investigating how innate social cognition components such as gaze-cuing might inform one’s egress. Do “Us versus Them” scenarios occur? He is also testing how one’s feelings of social identification with the surrounding crowd might modulate one’s risk-taking. Does hoarding prevent herding? Lastly, the project is looking at how cultural differences might affect egress time, when one insists to save personal possessions. More broadly, Stefan’s research concentrates on two key open questions in human crowd behavioural research. Firstly, how do social groups (that the player observes or is a member of) within the simulated crowd of agents affect both individual decision-making and the emergent behaviour of the crowd? Secondly, both empirical and virtual experiments of human crowds have not fully explored the effect of agent or player interactions with underlying landscape features (e.g. layout, signage, debris, large objects and other obstacles, etc). The outcomes of the experimental studies using real human participants will subsequently be used to develop more realistic decision-making and behavioural response algorithms and hence improve the behaviour of simulated agents in follow-on computer games. Stefan’s academic background may lie in Mathematics and Psychology, but his interdisciplinary mindset has constantly pushed him towards games and Computer Science. For his final Mathematics project, he designed an Android app that gamified teaching statistics. As part of his Psychology Masters degree, he investigated the potential benefits of MOBA games such as League of Legends with regard to visual attention. Currently, his extracurricular projects aim to explore video games’ effects on coping with trauma and on one’s perception of vulnerable groups, via commemorative gaming name choices or via in-game refugee storylines, respectively. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Sebastian Berns Queen Mary University of London iGGi Alum Sebastian is a designer and researcher working on use-inspired fundamental research in generative machine learning for creative and artistic applications. Sebastian holds a master’s degree in artificial intelligence and has a background in visual communications. He has worked several years as an independent graphic and type designer with a specialisation in web development. His design work has been awarded national and international design prizes. A description of Sebastian's research: "Generative machine learning methods are trained on raw data, modelling the primary patterns that constitute typical examples. They enable the production of high-quality artefacts in very complex domains and provide useful models for generative systems, in particular in the visual arts and video games. However, modelling a training data distribution perfectly is less valuable for applications in art production and video games. In particular, our analysis of the use of generative models in visual art practices motivates the need to increase the output diversity of generative models. In my dissertation, I focus on diversity in generative machine learning for visual arts and video games. Our findings benefit the application of generative models in generative systems, quality diversity search, art production and video games. Rather than a ‘ground truth’ that needs to be modelled perfectly, we argue that training datasets are merely a limited snapshot of a complex world with inherent biases. To be useful for applications in visual arts and video games, generative models require higher output diversity. Relatedly, higher generative diversity benefits efforts of equity, diversity and inclusion by reducing harmful biases in generative models." s.berns@qmul.ac.uk Email Mastodon http://www.sebastianberns.com/ Other links Website LinkedIn BlueSky https://github.com/sebastianberns Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Towards Mode Balancing of Generative Models via Diversity Weights Increasing the Diversity of Deep Generative Models Active Divergence with Generative Deep Learning--A Survey and Taxonomy Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search First experiments in the automatic generation of pseudo-profound pseudo-bullshit image titles Generative Search Engines: Initial Experiments Adapting and Enhancing Evolutionary Art for Casual Creation. Creativity Theatre for Demonstrable Computational Creativity Bridging Generative Deep Learning and Computational Creativity NEST 2.18. 0 Active Divergence with Generative Deep Learning--A Survey and Taxonomy Themes Creative Computing - 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. carlos.gonzalezdiaz@york.ac.uk Email https://masto.ai/@carlotes247 Mastodon https://carlotes247.github.io Other links Website https://uk.linkedin.com/in/carlosglesdiaz LinkedIn BlueSky https:// https://github.com/carlotes247 Github Supervisor(s): Prof. Sebastian Deterding Featured Publication(s): 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 Previous Next

< Back Dr Søren Riis Queen Mary University of London Supervisor Søren Riis has more than 15 years of experience in teaching computability, complexity and the art of creating fast efficient algorithms. He has a strong interest in reinforcement learning and generative adversarial networks (GANs) related to strategy games. Riis has been actively involved in computer chess, and is listed on the wiki of influential people in chess programming https://www.chessprogramming.org/ Søren Riis is a strong player of strategy games including Chess, Shogi, Go and Bridge at an internal level. He has worked as a consultant for an AI company and is involved in applying deep learning for the card game of bridge. For the last 5 years he has been working on technical projects related to machine learning and reinforcement learning. He has practical experience and interest in scientific computing on super computers, and in creating C and C++ libraries to run from within python. Søren Riis is particularly interested in supervising students with a strong technical and/or maths background. Aptitude for strategy games with an interest in one the following ares is an advantage. Games requiring inductive reasoning combined with exploration. Hidden identity games (Werewolf, Resistance/Avalon, Mafia etc) Using GANs to sample realistic scenarios during gameplay Deep Reinforcement Learning in multi-agent strategy games Building and analysing games for investigating evolution of communication. Research themes: Game AI Game Design Game Creativity Games and mathematics s.riis@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/profiles/riissoren.html Other links Website https://www.linkedin.com/soren-riis-13602117/ LinkedIn BlueSky Github Themes Creative Computing Game AI Game Data - 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 Cristina Dobre Goldsmiths iGGi Alum Cristina Dobre has a background in Mathematics and Computing receiving distinction in her undergraduate degree in Computer Science. My current focus is on the nonverbal cues that influence and shape the social interaction in immersive VR environments. More broadly, I'm investigating autonomous agents (or virtual humans) in social settings in terms of non-verbal interactions with users. I'm interested in the underlying mechanics of social interaction that help developing an emphatic and engaging virtual human. At the moment, I'm working on ML models based on multimodal datasets to detect various social cues (such as gaze) or various human-defined social attitudes (such as engagement) in social interactions in VR. I'm also interested in generating more complex behaviour for virtual characters (NPCs) that will improve the user's experience with the NPCs in a social VR setting. Designing communication and other social interactions in immersive VR can be a challenging task, and aspects on this are addressed in my research. The findings from these studies can help game designers and game developers determine the appropriate non-player character's non-verbal (and verbal) behaviour in games, especially in VR games. Along with its applications in the games industry, the findings would be useful for other applications such as designing multi-modal human-machine interactions and other systems for medical purposes, for social anxiety disorders therapy, simulations, training or learning. cristina.dobre@uni-a.de Email https://hci.social/@ShesCristina Mastodon Other links Website https://linkedin.com/shesCristina LinkedIn BlueSky https://www.github.com/shesCristina Github Featured Publication(s): Social Interactions in Immersive Virtual Environments: People, Agents, and Avatars Rolling Horizon Co-evolution in Two-player General Video Game Playing Using machine learning to generate engaging behaviours in immersive virtual environments More than buttons on controllers: engaging social interactions in narrative VR games through social attitudes detection Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings Immersive Machine Learning for Social Attitude Detection in Virtual Reality Narrative Games Direct Gaze Triggers Higher Frequency of Gaze Change: An Automatic Analysis of Dyads in Unstructured Conversation Themes Game AI Immersive Technology - Previous Next