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< 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 Connor Watts Queen Mary University of London iGGi PG Researcher I am a machine learning research engineer and software developer with commercial experience deploying and maintaining models for start-ups and larger organizations. I have experience researching and developing novel algorithms, as well as designing custom environments for application in domains such as combinatorial optimization, finance and games. c.watts@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/connor-watts-363354232/ LinkedIn BlueSky https://ConnorWatts.github.io Github Supervisor: Dr Paulo Rauber Themes Game AI - Previous Next

< Back Prof. Massimo Poesio Queen Mary University of London Supervisor Massimo Poesio is a cognitive scientist whose primary field is Computational Linguistics / Natural Language Processing. He is interested in the interdisciplinary study of language processing using evidence from computational modelling, corpora, psychological studies, and neuroscience; specific interests include computational models of anaphora resolution (coreference); the study of disagreement on language interpretation through the creation of large corpora containing multiple judgments (an area in which he pioneered the use of games-with-a-purpose with the development of Phrase Detectives, http://www.phrasedetectives.org ); the interpretation of verbal and non-verbal communication in interaction; and the study of conceptual knowledge using a combination of methods from human language technology and neuroscience. He has also been involved in a number of projects applying NLP methods to real life problems, such as detecting deception online, or identifying human rights violations reports in social media. He holds a European Research Council grant on identifying disagreements in language through Games-With-A-Purpose, DALI and is a co-founder of the open access journal Dialogue and Discourse . Using conversational agents in games Applying games to label data for AI Research themes: Game AI Game Design Games with a Purpose m.poesio@qmul.ac.uk Email Mastodon https://sites.google.com/view/massimo-poesio/ Other links Website LinkedIn BlueSky https://github.com/dali-ambiguity Github Themes Applied Games Game AI - 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 Dr Nuria Peña Pérez Queen Mary University of London iGGi Alum Nuria got her bachelor’s in biomedical engineering in Spain before moving to London. After studying an MSc in Neurotechnology and working in robotic neurorehabilitation at Imperial College London, she discovered the enormous potential of serious games in the field of human-robot interaction. She joined IGGI in 2018. Her PhD research involves studying human motor control and learning during bimanual tasks to investigate how the dynamics of the interaction can serve to develop better training systems. This is done through the development of interactive gaming environments that are compatible with rehabilitation robotic devices. The modelling of the recorded human neuromuscular data allows to explore how to better help patients to restore their motor function. Her work is a collaboration between the Advanced Robotics group at Queen Mary University of London and the Human Robotics group at Imperial College London. As part of her PhD she has worked for the company GripAble, developing games for the assessment and training of hand function (February 2020-August-2020). n.penaperez@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor(s): Dr Ildar Farkhatdinov Featured Publication(s): Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks Dissociating haptic feedback from physical assistance does not improve motor performance Bimanual interaction in virtually and mechanically coupled tasks The impact of stiffness in bimanual versus dyadic interactions requiring force exchange How virtual and mechanical coupling impact bimanual tracking Lateralization of impedance control in dynamic versus static bimanual tasks Is a robot needed to modify human effort in bimanual tracking? Exploring user motor behaviour in bimanual interactive video games Quartz Crystal Resonator for Real-Time Characterization of Nanoscale Phenomena Relevant for Biomedical Applications Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Applied Games - Previous Next

< Back Prof. Paul Cairns University of York iGGi Chair Supervisor Paul Cairns is a professor interested in Human-Computer Interaction (HCI) generally and specifically on how games work to produce the experiences that players really value. He has looked extensively at immersion and engagement in games but is also developing new ideas on players experiences of challenge and uncertainty. He has been teaching HCI for over twenty years and is particularly interested in the rigorous application of research methods having co-edited the first book on research methods for HCI and written another about doing better statistics in HCI. He strongly believes in self-explanatory book titles. He is also Scholar-in-Residence at The AbleGamers Charity, based in the USA, through which he is working with players and game developers to inform and advance the development of accessible games. With his colleagues there, he produced the Accessible Player Experiences (APX) design patterns and card deck. He is particularly interested in supervising students with a HCI, behavioural sciences, media or computer science background on the following topics: Understanding player experiences Developing new measures of player experience whether based on self-report, physiological or other instruments Accessible player experiences Using games to understand and inform people’s experiences with other interactive systems Research themes: Accessible Games Games with a Purpose Player Experience paul.cairns@york.ac.uk Email Mastodon https://www-users.cs.york.ac.uk/~pcairns Other links Website https://www.linkedin.com/in/paul-cairns-99a1b32/ LinkedIn BlueSky Github Themes Accessibility Applied Games Game Data Player Research - Previous Next

< Back Dr Ildar Farkhatdinov Queen Mary University of London Supervisor Dr Ildar Farkhatdinov is a Lecturer in Robotics at QMUL since 11/2016 and a Turing Institute Fellow. He is an internationally leading expert in assistive robotics and human-machine interaction. He is a principle investigator of several projects on wearable robotics, mobility assistance and haptic interfaces (including funding from the UK government on supernumerary robotic limbs and assistive wheelchairs, £500k+). Several of his research works were recognised as the best paper or finalists for best paper awards at leading robotics conferences. Before joining QMUL, he was a postdoctoral research associate at the Human Robotics group of the Department of Bioengineering, Imperial College London (2013-16). He earned Ph.D. in Robotics in 2013 (Sorbonne University, UPMC, France), M.Sc. in Mechanical Engineering in 2008 (KoreaTech, South Korea) and B.Sc. in Automation and Control in 2006 (Moscow University, Russia). He has actively collaborated on a number of large-scale research projects: EPSRC NCNR to create novel robotic solutions for the nuclear industry; EU FP7 BALANCE to develop balance and robotic walking assistance for the elderly; EU FP7 SYMBITRON to develop exoskeleton control for people with spinal cord injury. My research interest relevant to CDT IGGI include serious games for medical applications, as well as using game theory to investigate human-machine interaction. Research themes: Game Design Serious games Virtual reality Game theory i.farkhatdinov@qmul.ac.uk Email Mastodon https://hair-robotics.qmul.ac.uk Other links Website https://www.linkedin.com/in/ildar-farkhatdinov-33075016 LinkedIn BlueSky Github Themes Design & Development Game AI Immersive Technology Player Research - Previous Next

< 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

< Back Dr Diego Pérez-Liébana Queen Mary University of London iGGi Industry Liaison Supervisor Born in Madrid (Spain) and living in London (United Kingdom), I am a Senior Lecturer in Computer Games and Artificial Intelligence at Queen Mary University of London. I hold a PhD in Computer Science from the University of Essex (2015) and a Master degree in Computer Science from University Carlos III (Madrid, Spain; 2007). My research is centered in the application of Artificial Intelligence to games, Tree Search and Evolutionary Computation. At the moment, I am especially interested on General Video Game Playing and Strategy games, which involves the creation of content and agents that play any real-time game that is given to it, and research in Abstract Forward Models. I have recently been awarded with an EPSRC grant on Abstract Forward Models for Modern Games. I am author of more than 100 papers in the field of Game AI, published in the main conferences of the field of Computational Intelligence in Games and Evolutionary Computation. I have publications in highly respected journals such as IEEE TOG and TEVC. I have also organised international competitions for the Game AI research community, such as the Physical Travelling Salesman Competition, and the General Video Game AI Competition, held in IEEE (WCCI, CIG) and ACM (GECCO) International Conferences. I also experience in the videogames industry as a game programmer (Revistronic; Madrid, Spain), with titles published for both PC and consoles. I worked as a software engineer (Game Brains; Dublin, Ireland), where I oversaw the development of AI tools that can be applied to the latest industry videogames. I am particularly interested in supervising students with background on applications of Tree Search or Evolutionary Algorithms for strategy games. Research Themes: Game AI Rolling Horizon Evolutionary Algorithms. Monte Carlo Tree Search Statistical Forward Planning methods. Strategy Games. diego.perez@qmul.ac.uk Email Mastodon https://diego-perez.net Other links Website https://www.linkedin.com/in/diegoperezliebana/ LinkedIn BlueSky https://github.com/diegopliebana Github Themes Game AI Game Data - Previous Next

< Back Dr Ben Kirman University of York iGGi Training Coordinator Supervisor Available to supervise non-iGGi students for 2024 intake Ben is a Senior Lecturer (Associate Prof) in Interactive Media at the University of York, who has over 20 years' experience as a creative technologist. Since his first programming job fixing Y2K bugs (you're welcome), he has worked with dozens of organisations, large and small, in design and prototyping playful experiences. His research uses game design and playful design as a way to explore the complex effects of emerging technologies through novel and unexpected interactions and experiences. Most often, this is through the design and development of games, digital/physical prototypes, and design fictions. Ben has applied this in topics ranging from immersive theatre, dog technology, non-league football, radical cycle delivery, and time travelling robots, to educational games, esports, new situationism and magic. The unifying theme is play – as a topic of study, a way of working, for research insight, and as expression or output in games or playful experiences. This work, especially the more bizarre stuff, has often been covered by traditional media, including the BBC, New Scientist, Wired, The Guardian, TIME, Metro, the New York Times, and Your Cat magazine. Ben is keen on supervising students with strong creative drives, with an interest in making, design, experimentation, and a broad perspective on games and play. This might be a project about playful props in immersive theatre, or a project about context in locative and site-specific games, or any other project that looks to explore new possibilities and new implications of emerging technology through the lens of play. Research themes include: Game Design Applied Games Computational Creativity Sports with an E and without an E Player Experience ben.kirman@york.ac.uk Email Mastodon https://ben.kirman.org/ Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing Design & Development Esports Player Research - Previous Next

< Back Tom Wells University of York iGGi PG Researcher Available for placement Tom has an interest in niche alternative and indie games which evoke strong emotions and are narratively immersive. He studied Experimental Psychology as an undergraduate in Oxford, specialising in conscious brightness perception in specific optical pigments. His Masters was in Computational Neuroscience, Cognition and AI from Nottingham, and focused on Computer Vision (specifically facial recognition) and Visual Attention. He enjoys heavy metal, strength sports and literature. A description of Tom's research: With the rise of digital art, Uncanny Valley has emerged from an esoteric robotics concept into an infectious memetic phenomenon, with specific memes such as 'Uncanny/Canny Mr. Incredible', or more generally uncanny faces being used as reaction images for humor. Critics and players will now refer to specific media being 'Uncanny' rather than using more general words as 'off-putting', demonstrating uncanniness cementing itself in the public consciousness as examples increasingly abound; ergo digital artists should be aware of evoking the uncanny even with modern rendering technology, as audiences become increasingly discerning of the Uncanny. This is most pertinent in videogames, where rendering is performed in real-time, meaning rendering constraints must be implemented. This potentially confines characters to the Uncanny Valley, as it may not be possible to increase graphical fidelity, thus artists may be left to either accept the uncanny or demaster their work (both undesirable options). This project aims to learn about the Uncanny Valley pertaining to modern skin rendering techniques, using artificial intelligence (specifically GANs) to directly map skin rendering parameters onto user assessments of uncanniness and realism. This can then be reverse engineered to provide automated tools for generatively rendering realistic non-uncanny skin, and predicting audience responses to skin realism, expediting QA testing. The primary experimental stage is to generate a face database with photorealistic skin to be assessed using psychometrics by participants. This is additionally one of few studies looking into the novel phenomena of training AI's to generate human-oriented psychologically salient content. tw1700@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Dr Andrew James Wood University of York Supervisor I am an interdisciplinary researcher at the University of York. My background is in Mathematical Physics but my interests are now in applying computational and mathematical techniques to interesting problems, mostly in Biology. This includes such topics as collective motion (particularly in interaction networks and the role of noise) and microbiology (particularly in metabolism, industrial biotechnology, spatial structure and plasmid dynamics) as well as modelling naval conflicts and glycosylation. I have a natural interest in games and am interested in the interface between games and science, be that in using games to do, or disseminate, science or in utilising mechanisms and insights from research to inspire games. Research themes: Game Analytics Game Design Games with a Purpose Gamification jamie.wood@york.ac.uk Email Mastodon https://ajamiewood.weebly.com/ Other links Website https://www.linkedin.com/in/jamie-wood-82460055/ LinkedIn BlueSky Github Themes Applied Games Design & Development Game Data - Previous Next