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< Back Dr Chris Madge Queen Mary University of London iGGi Alum Turning Difficult Scientific Problems into Easy Games: Crowdsourcing Solutions via Gamification The aim of the research is to exploit, on a large scale, the idea introducing game elements in a non-game context (gamification) and make use of a large population of non-expert users to solve scientific problems (crowdsourcing). The proposed research follows the increasingly popular concept of splitting a large, complex task into small easily digestible tasks that lend themselves to division, distribution and game representation. This research will begin by taking advantage of the University of Essex’s expertise in the field of Natural Language Engineering. Multiple games will be created to attempt to encourage people to participate in training natural language models. This will be achieved by splitting these tasks into smaller problems that can be represented as games, and easily solved by players that could not easily be solved computationally. Alongside this, the success of different gamification methods and game design choices will be evaluated to determine their effect on the information gathered and the accuracy achieved. This evaluation will be used to guide the development of future games in the research with a view to producing better quality models for solving natural language problems, and improving gamification. Prior to starting my PhD with IGGI I completed a BSc in Computer Science and MSc in Advanced Computer Science. During both of those I took multiple computer game and AI courses in addition to text analytics and natural language engineering courses. During my BSc I was fortunate to work at Signal Media as an intern on text analytics related problems. Before starting my BSc I worked as a software developer for 5 years, primarily in web application development. I’ve had a passion for games from a very young age and continue to play on PC, mobile and consoles today. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Gamifying language resource acquisition Progression in a language annotation game with a purpose Incremental game mechanics applied to text annotation Making text annotation fun with a clicker game The design of a clicker game for text labelling Crowdsourcing and aggregating nested markable annotations Testing TileAttack with Three Key Audiences Experiment-driven development of a gwap for marking segments in text Metrics of games-with-a-purpose for NLP applications Testing game mechanics in games with a purpose for NLP applications TileAttack Novel Incentives for Phrase Detectives Themes Player Research - Previous Next

< Back Dr Alan Pedrassoli Chitayat University of York iGGi Alum Available for post-PhD position Alan is a researcher that focuses on audience experience within esport broadcast. His Machine Learning background allows him to extract complex patterns from game and game related data in order to derive meaningful insights that can be utilised in broadcast. Having worked in the esport industry, both as a software engineer as well as researcher, Alan has experience with both technical and research problems. His research aims to explore the factors that improve the audience experience within esports. This is catered to esport broadcast of all levels, from highly produced professional tournaments to regular streams by content creators and it could be in the form of: Measuring and representing different forms of audience engagement. Exploring the different ways to visualise and utilise Machine Learning to enhance and integrate existing broadcast pipelines. Investigating how community-led narratives can be generated through data. alan.pchitayat@york.ac.uk Email https://linktr.ee/alanpchitayat Mastodon https://alanpchitayat.com/ Other links Website https://www.linkedin.com/in/alan-pchitayat/ LinkedIn BlueSky Github Supervisors: Dr James Walker Prof. Anders Drachen Featured Publication(s): How Could They Win? An Exploration of Win Condition for Esports Narratives Applying and Visualising Complex Models in Esport Broadcast Coverage From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports What are you looking at? Team fight prediction through player camera Echo Suite of Software (Showcase Brochure) Automatic Generation of Text for Match Recaps using Esport Caster Commentaries WARDS: Modelling the Worth of Vision in MOBA's DAX: Data-Driven Audience Experiences in Esports Themes Design & Development Esports Game Data - Previous Next

< Back Sunny Thaicharoen Queen Mary University of London iGGi PG Researcher Available for post-PhD position Sunny is a passionate esports enthusiast, with a love of MOBA games. His background is in engineering and entrepreneurship, with a Master of Technology Entrepreneurship degree from University College London. He is the creator of YGOscope, a statistical game data platform for a competitive card game, Yu-Gi-Oh. Sunny is an avid player of competitive Dota in his spare time, and is also a keen theme park enthusiast. He is interested in modelling metagames of MOBAs through game data and player research, particularly how players adopt the most effective strategies when changes to the stable gameplay state occurs. A description of Sunny's research: The project focuses on how the META - most effective tactics available - of MOBA games shift during disruption (usually through gameplay updates) between states of ignorance and stability within the player space of these games, to deepen our understanding of how players adapt to the changes that these gameplay updates cause, and why. There is a large degree of variability of how new METAs develops, and currently there is little research on the meta and metagame front. Available research so far has been based on defining the phenomena and resulting effects of gameplay updates, but little modelling has been done to attempt bring these fragmented pieces of knowledge together and attempt to structure them. The study and structuring of this phenomena can be an ideal starting point in understanding how effective strategies develop not only in MOBAs or video games, but any other competitive games such as chess, trading card games or sports. t.thaicharoen@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/thaicharoens/ LinkedIn BlueSky https://github.com/thaicharoens Github Supervisors: Prof. Anders Drachen Dr Jeremy Gow Featured Publication(s): An ecosystem framework for the meta in esport games Themes Esports Game Data Player Research - Previous Next

< Back Dr Adrián Barahona-Ríos University of York iGGi Alum From 2018 and in collaboration with Sony Interactive Entertainment Europe, Adrián is researching strategies to increase the efficiency in the creation of procedural audio models for video games by using DSP and machine learning approaches. His main research interests, applied to the synthesis of sound effects, are generative deep learning (GANs, RNNs and VAEs) to synthesise raw audio and machine learning to find out the best parameters for a synthesiser to generate a target sound. Adrián has been enthusiastic about sound and more specifically about game audio since he began his studies. By the time he completed an HND in Creative Media Production in Madrid, he started working in the industry as a recording engineer in an ADR studio for the Spanish localisation of video games (such as Fallout 4, Until Dawn or Just Cause 3). He moved from Spain to the UK in 2015 to take a BA (top-up) in Music Production at the Southampton Solent University and an MSc in Sound Design at the University of Edinburgh immediately after. During that journey, he focused his career in procedural audio and explored ways to create models for interactive applications by using different techniques. adrian.barahona.rios@gmail.com Email Mastodon https://www.adrianbarahonarios.com/ Other links Website https://www.linkedin.com/in/adrianbarahona LinkedIn BlueSky https://github.com/adrianbarahona Github Supervisor Dr Tom Collins Featured Publication(s): Deep Learning for the Synthesis of Sound Effects NoiseBandNet: controllable time-varying neural synthesis of sound effects using filterbanks Sonifying energy consumption using SpecSinGAN SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs Synthesising Knocking Sound Effects Using Conditional WaveGAN Perception of emotions in knocking sounds: An evaluation study Perceptual Evaluation of Modal Synthesis for Impact-Based Sounds Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Creative Computing Game Audio - Previous Next

< Back Dr Yul HR Kang Queen Mary University of London Supervisor Yul Kang, MD, PhD is a computational cognitive neuroscientist studying how natural & artificial neural networks handle unavoidable uncertainty in sequential decision-making, such as wayfinding during navigation. He uses Bayesian approaches and probabilistic neural representation models, with applications to games, fundamental science, and healthcare. He received his MD in Seoul National University (South Korea), PhD in Columbia University (USA), and did postdoctoral research at the University of Cambridge (UK), where he was elected and served as a Junior Research Fellow. His work was published in top-tier journals such as Current Biology and eLife, and was presented as a talk in leading computational neuroscience conferences such as Cosyne and Bernstein Conference. His work was featured in news outlets such as The Independent. His research addresses how the brain handles unavoidable uncertainty (e.g., from ambiguous visual scene) during sequential decision-making (e.g., wayfinding). It helps understand players’ behaviour and predict their uncertainty given a map (and hence difficulty). Since neurological patients often show specific impairments in such tasks, it may help earlier and more specific diagnosis of diseases. Yul is interested in predicting players’ behaviour, procedural generation of levels by predicting subjective uncertainty and fun, and using games for diagnosis of psychiatric and neurological diseases. yul.kang@qmul.ac.uk Email Mastodon https://www.yulkang.net/ Other links Website https://www.linkedin.com/in/yul-kang-9b11522b/ LinkedIn BlueSky https://github.com/yulkang Github Themes Creative Computing Game AI Immersive Technology Player Research - Previous Next

< Back Guilherme Matos de Faria University of York iGGi Alum I am a Portuguese student with a background in Artificial Intelligence. In 2016 I started attending video game tournaments and learned to analyse my matches and improve from it. When I did my masters in AI, I noticed that I could join my professional skills and my hobbies together to create something relevant to AI and competitive gaming. A description of James' research: I am looking to better understand which actions and decisions have the biggest impact on the outcome of a game. Currently, I am particularly focused on competitive turn based card games. What are the best players doing to win? How can players adapt to improve their chances of success? These are the questions I am hoping to help answer, giving players a better understanding of the game and how to improve. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Anna Bramwell-Dicks University of York Supervisor Anna Bramwell-Dicks has an interdisciplinary background which started in Electronics and Music Technology before taking a sideways move to the field of Human-Computer Interaction research. She likes to combine her underlying interest in sound and music with applied psychology and creativity. She is very interested in research involving multimodal interaction (e.g. using audio, haptics, smell and/or proprioception as well as visuals within interfaces) particularly where audio is used to affect user’s behaviour or experiences. She is also very interested in accessibility research and any research in the application area of mental health and mental illness. As a lecturer in Web Development and Interactive Media, based in TFTI, Anna is always interested in work that involves designing and evaluating novel and interesting user experiences, particularly where that leads to the option to create fun, engaging, accessible experiences. She likes to work across a range of application areas ranging from learning environments to e-commerce to escape rooms and cultural exhibits! Anna is keen to work with students who want to design and develop gamified systems to support people with disabilities, physical or mental illness. Or, those who are also interested in multimodal experiences. Research themes: Accessibility Multimodal and multisensory systems Research methods anna.bramwell-dicks@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/anna-bramwell-dicks-2b941a28/ LinkedIn BlueSky Github Themes Accessibility Applied Games Design & Development Game Audio Player Research - Previous Next

< Back Dr Anne Hsu Queen Mary University of London Supervisor Anne Hsu’s research includes machine learning, artificial agents, natural language processing and learning, human decision making, interaction design, and well-being technology. Her interests include developing interactive systems that use machine learning and understanding of human psychology to improve human behaviour. She is particularly interested in supervising students with a machine learning, design, HCI, or behavioural sciences background on the following topics: understanding and designing for curiosity in games design for behaviour change motivational/educational games Research themes: Game AI Game Design Games with a Purpose Player Experience Gamification anne.hsu@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/anne-showen-hsu LinkedIn BlueSky Github Themes Applied Games Design & Development Esports 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 Dr Martin Balla Queen Mary University of London iGGi Alum Before starting his PhD Martin studied Computer Science at the University of Essex. His main interest is artificial intelligence and its application to all sort of problems ranging from computer vision to game AI. He likes spending his spare time with various activities which mainly involves reading, playing video games and skateboarding. Martin's PhD thesis focuses on Reinforcement Learning agents that can adapt to changes in the reward function and/or changes in the environment. His work investigates how agents can transfer their knowledge to changes in the environment, such as new rewards, levels or visuals. Outside of his main research direction, Martin is involved with the Tabletop games framework (TAG), which is a collection of various tabletop games implemented with a common API with a focus on various game-playing agents (including RL). TAG brings various challenges to RL agents compared to search-based agents, such as complex action spaces, unique observation spaces (various embeddings), multi-agent dynamics with competitive and collaborative aspects, and lots of hidden information and stochasticity. m.balla@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/martinballa LinkedIn BlueSky https://martinballa.github.io Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Illuminating Game Space Using MAP-Elites for Assisting Video Game Design PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games TAG: Pandemic Competition Task Relabelling for Multi-task Transfer using Successor Features TAG: A tabletop games framework Design and implementation of TAG: a tabletop games framework Evaluating generalisation in general video game playing Evaluating Generalization in General Video Game Playing Analysis of statistical forward planning methods in Pommerman Themes Game AI - Previous Next

< Back Helen Tilbrook University of York iGGi Administrator iGGi Admin iGGi Administrator at York helen.tilbrook@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Dr Claudio Guarnera University of York Supervisor You can get more out of your site elements by making them dynamic. To connect this element to content from your collection, select the element and click Connect to Data. Once connected, you can update it anytime without affecting your design or updating elements by hand. Add any type of content to your collection, such as rich text, images, videos and more, or upload it via CSV file. You can also collect and store information from your site visitors using input elements like custom forms and fields. Be sure to click Sync after making changes in a collection, so visitors can see your newest content on your live site. claudio.guarnera@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/cvpr/member/claudio/ Other links Website https://www.linkedin.com/in/giuseppe-claudio-guarnera LinkedIn BlueSky Github Themes Applied Games Creative Computing - Previous Next