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< 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 Dr Ryan Spick University of York iGGi Alum Deep Learning for Procedural Content Generation in Virtual Environments Ryan Spick is a PhD student with a computer science background, working on methods to improve how content (models, terrain, assets etc.) is created with an autonomous focus, with the main focus on generative deep learning to augment real-world data through a series of neural network layers to learn unlying properties of these data. Ryan has published a variety of papers around his main topic of generating content, such as terrain generation using generative adversarial networks and 3D voxel coloured model generation, to collaborations on other topics using deep learning, such as death prediction in a multiplayer online game and applying a recent map-elites algorithm. He has also worked with several leading industry researchers/games companies to further develop his research skill.If you have any ideas or collaboration opportunities please get in contact through any of the mediums below. Please note: Updating of profile text in progress ryan.spick@hotmail.co.uk Email Mastodon https://www.rjspick.com/ Other links Website https://www.linkedin.com/in/ryan-spick-505b63131/ LinkedIn BlueSky Github Featured Publication(s): System and Method for Point Cloud Generation System and method for training a machine learning model Robust Imitation Learning for Automated Game Testing Behavioural Cloning in VizDoom Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Utilising VIPER for Parameter Space Exploration in Agent Based Wealth Distribution Models Human Point Cloud Generation using Deep Learning Naive mesh-to-mesh coloured model generation using 3D GANs Realistic and textured terrain generation using GANs Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Time to die: Death prediction in dota 2 using deep learning Themes Game AI - Previous Next

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. Women in Games Jobs (WIGJ)

< Back What Factors Do Players Perceive as Methods of Retention in Battle Royale Games? Link Author(s) MJ Saiger, BDA Khaleque Abstract More info TBA Link

< Back Accessible player experiences (APX): The players Link Author(s) J Beeston, C Power, P Cairns, M Barlet Abstract More info TBA Link

< 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

< 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 Matt Bedder University of York iGGi Alum Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io) Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games. Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique. Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/pub/matthew-bedder/80/2a7/a51/ LinkedIn BlueSky Github Featured Publication(s): Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms Computational approaches for understanding the diagnosis and treatment of Parkinson's disease Automated motion analysis of adherent cells in monolayer culture Themes Game AI - Previous Next

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. NATS

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. Electronic Arts (EA)

< Back Dr Ahmed M. A. Sayed Queen Mary University of London Supervisor Ahmed Sayed is a Lecturer (Assistant Professor) of Big Data and Distributed Systems at the School of EECS, QMUL and leads the Scalable Adaptive Yet Efficient Distributed (SAYED) Systems Lab. He has a PhD in Computer Science and Engineering from the Hong Kong University of Science and Technology. His research interests lie in the intersection of distributed systems, computer networks and machine learning. He is an investigator on several UK and international grants totalling nearly USD$1 million in funding. His work appears in top-tier conferences and journals including NeurIPS, AAAI, MLSys, ACM EuroSys, IEEE INFOCOM, IEEE ICDCS, and IEEE/ACM Transactions on Networking. He is interested in supervising students with a background in game AI, machine learning, distributed systems, and/or creative computing, Ahmed is interested in working with students at the intersection of artificial intelligence, machine learning, and creative computing. He aims to leverage AI/ML methods, game data and player research to design intelligent game agents by creating systems that enable game agents to learn better gaming strategies, thus enhancing the gaming experience. He is open to any research proposals in that space and currently is keen on exploring solutions that are based on leveraging the emerging distributed privacy-preserving ML ecosystems on large-scale game data. If you are interested in working with him on this, please reach out to him. ahmed.sayed@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/~ahmed/ Other links Website https://www.linkedin.com/in/ahmedmabdelmoniem/ LinkedIn BlueSky https://github.com/ahmedcs Github Themes Creative Computing Design & Development Game AI Game Data Player Research - Previous Next

< Back Interactive machine learning for more expressive game interactions Link Author(s) C Gonzalez Diaz, P Perry, R Fiebrink Abstract More info TBA Link