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< Back Dr Lina Gega University of York Supervisor Qualified both as a nurse and a psychological therapist, Lina is a senior member of the Mental Health and Addictions Research Group (MHARG) at the University of York, where she leads research under the Digital Mental Health Theme. She has published widely on computer-based therapies and virtual environments. Lina's work on technology-mediated interventions and training formed an impact case study was submitted to 2014 Research Excellence Framework as part of Psychology, Psychiatry and Neuroscience. Lina’s current work focuses on interventions to improve health and quality of life for children and young people with mental health problems. She has led the development and evaluation of a purposeful game to treat phobias in children, and of an innovative virtual environments system to assist psychological therapy and skills training. She co-leads the digital theme for the Closing the Gap (CTG) Network, funded by UK Research and Innovation (UKRI). The Network’s digital theme explores how technologies, including gaming, can be used to improve the physical health of people with severe mental illness, especially schizophrenia and bipolar affective disorder. An experienced University teacher, supervisor and examiner, Lina welcomes students with a design, engineering or behavioural sciences background who are interested in applied games research in the field of mental health, with a focus on: development and ‘proof-of-concept’ studies of purposeful games to improve mental health outcomes and social communication skills in children and young people. adaptation and evaluation of gamified applications to improve physical health outcomes with people whose motivation and information processing are affected by severe mental illness. Research themes: Game Design Games with a Purpose Player Experience Gamified Mental Health Interventions lina.gega@york.ac.uk Email Mastodon https://www.york.ac.uk/healthsciences/our-staff/lina-gega/ Other links Website LinkedIn BlueSky Github Themes Applied Games Player Research - Previous Next

< Back Dr Kevin Denamganaï University of York iGGi Alum Available for post-PhD position After graduating as an Engineer from the Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), France, with two double-degree diplomas, a MEng in Electrical Engineering and Information Science from the Osaka Prefecture University (OPU), Japan, and a MRes in Artificial Intelligence and Robotics from the Université de Cergy-Pontoise (UCP), France, Kevin Denamganaï spent a year accumulating experience as a Robotics & Machine Learning freelancer. He is now putting those skills at use in the IGGI PhD program, that, among other things, gives him the opportunity to reunite with video games. Indeed, it was thanks to a keen interest towards video game creation that he started learning programming around 12. His research interests are about everything psychology, neuroscience, AI, (deep) reinforcement/imitation learning, robotics, and natural/artificial language emergence and understanding as well as human-computer interfaces, challenging the question what are the necessary components of artificial agents to be able to converse with human-beings in an engaging manner and to be able to cooperate with them towards a pre-defined goal, e.g. clearing a level in a given video game. kevin.denamganai@york.ac.uk Email Mastodon https://kevindenamganai.netlify.app/ Other links Website LinkedIn BlueSky https://github.com/Near32/ Github Supervisor(s): Dr James Walker Featured Publication(s): ETHER: Aligning Emergent Communication for Hindsight Experience Replay Visual Referential Games Further the Emergence of Disentangled Representations Meta-Referential Games to Learn Compositional Learning Behaviours A comparison of self-play algorithms under a generalized framework On (Emergent) Systematic Generalisation and Compositionality in Visual Referential Games with Straight-Through Gumbel-Softmax Estimator ReferentialGym: A Nomenclature and Framework for Language Emergence & Grounding in (Visual) Referential Games A generalized framework for self-play training Coupled Kuramoto oscillator-based control laws for both formation and obstacle avoidance control of two-wheeled mobile robots Obstacle avoidance control law for two-wheeled mobile robots controlled by oscillators Themes Game AI - Previous Next

< Back Dr Evelyn Tan University of York iGGi Alum Evelyn holds a master’s degree in Industrial-Organisational Psychology from University College London (UCL) and has previously worked in the HR Technology industry where she undertook projects on game-based assessment, virtual team coaching and virtual reality (VR) hiring and training. She has published her work on developing trust in virtual work teams at CHI PLAY 2019, a premier conference for games research. On the Emergence and Development of Team Cohesion in Newly Formed Virtual Teams Evelyn specialises in teamwork and team dynamics. She is interested in uncovering how cohesion emerges and develops, and to identify its predictors. Her goal is to understand how to build high-performing teams that make people want to stay and remain united in the pursuit of their shared objectives. Under IGGI, she studies virtual teams in competitive esport games, specifically newly formed ad hoc teams. By applying theories and principles from psychology, her work can be extended to address the challenges faced by real-world teams with similar characteristics, for example emergency response teams and short-term project teams. By studying team cohesion – its emergence and development – her work addresses the broader challenges of building high-performing teams which retain their members. Please note: Updating of profile text in progress Email Mastodon https://sites.google.com/view/evelyntan Other links Website https://www.linkedin.com/in/evelyntisiantan/ LinkedIn BlueSky https://github.com/ett506 Github Featured Publication(s): Communication Sequences Indicate Team Cohesion: A Mixed-Methods Study of Ad Hoc League of Legends Teams Less is More: Analysing Communication in Teams of Strangers Trusted Teammates: Commercial Digital Games Can Be Effective Trust-Building Tools Themes Esports Player Research - Previous Next

< Back Amy Smith Queen Mary University of London iGGi PG Researcher Available for placement After completing a BA in Fine Art, at Bath School of Art and Design, Amy spent some years as a tattoo artist travelling and creating artworks. An interest in learning to code then led her to complete a conversion Masters degree in Computer Science at the University of Birmingham. Keen to preserve her interests in both a creative practice as well as a new interest in generative deep learning, Amy joined the IGGI program to explore these interests further under the guidance of Dr. Mike Cook, Dr. James Walker and Prof. Simon Colton. Amy's research is currently focused on the intersection between 'imaginative play', computational creativity and generative deep learning. This project explores whether the kind of novel text, image and video media produced by generative deep learning algorithms can be used to provoke and stimulate the imaginative, ideation and visualisation capabilities of the user as they interact with this cutting edge technology. To date, her work has been accepted to several conferences, including the International Conference on Computational Social Science, AAAI, the International Conference on Computational Creativity, and EvoMusArt. Amy hopes to further encourage and explore the fruits of a close collaboration between human creativity and creative AI. amyelizabethsmith01@gmail.com Email Mastodon http://aialchemy.media.mit.edu Other links Website https://www.linkedin.com/in/amy-smith-791784173 LinkedIn BlueSky Github Supervisors: Dr Mike Cook Prof. Simon Colton Dr James Walker Featured Publication(s): Scaling Analysis of Creative Activity Traces via Fuzzy Linkography Fuzzy Linkography: Automatic Graphical Summarization of Creative Activity Traces AI-Generated Imagery: A New Era for the 'Readymade' The @artbhot Text-To-Image Twitter Bot. Trash to Treasure: Using text-to-image models to inform the design of physical artefacts Clip-guided gan image generation: An artistic explorationClip-guided gan image generation: An artistic exploration Art and the science of generative AI Generative Search Engines: Initial Experiments Themes Creative Computing Player Research - Previous Next

< Back Dr Daniel Hernandez University of York iGGi Alum With the games industry as his target, Daniel Hernandez’s main research objective is to design and implement algorithms that, without any prior knowledge, generate strong gameplaying agents for a wide variety of games. To tackle this “from scratch” learning, he uses, and contributes to, the fields of Multiagent Reinforcement Learning, Game Theory and Deep learning. Self-play is the main object of study in his research. Self-play is a training scheme for multiagent systems in which AIs are trained by acting on an environment against themselves or previous versions of themselves. Such training scheme bypasses obstacles faced by many other training approaches which rely on existing datasets of expert moves or human / AI agents to train against. Daniel’s hope is that further development in Self-play will allow game studios of all sizes to generate strong AI agents for their games in an affordable manner. A storyteller by nature, Daniel has a strong track record of outreach through talks and workshops both in the UK and internationally. By sharing his journey, insights and discoveries he hopes to both inspire and instruct students, researchers and developers to realise the potential that Reinforcement Learning has to improve the games industry. His passionate work on Machine learning goes beyond crafting strong gameplaying agents. He sees the potential of using AI to simplify and automate a wide range of tasks in the games industry. He has led successful projects which used machine learning aimed at automating multiagent game balancing to alleviate the burden of manual game balancing. Daniel received an MEng in Computing: Games, Vision & Interaction from Imperial College London. Wanting to combine the power of AI and the creativity of videogames, Daniel began a PhD journey to explore the misty lands of Multi Agent Reinforcement Learning (MARL). Please note: Updating of profile text in progress Email Mastodon https://danielhp95.github.io Other links Website https://www.linkedin.com/in/dani-hernandez-perez-1106b2107 LinkedIn BlueSky https://github.com/Danielhp95 Github Featured Publication(s): A comparison of self-play algorithms under a generalized framework A generalized framework for self-play training Metagame Autobalancing for Competitive Multiplayer Games Themes Game AI Player Research - Previous Next

< Back Dr Lorenzo Jamone Queen Mary University of London Supervisor I am a Lecturer in Robotics and Director of the CRISP group (Cognitive Robotics and Intelligent Systems for the People) at the School of Electronic Engineering and Computer Science (EECS) of the Queen Mary University of London (QMUL). The CRISP group is part of ARQ (Advanced Robotics at Queen Mary). Since October 2018, I have been a Turing Fellow at The Alan Turing Institute. I am interested in understanding human (and animal) intelligence, by using computational techniques that include computer simulations and real robots. My research topics include: human creativity and creative problem solving, human perception, human-human non-verbal communication, object affordances, tool use, body schema, eye-hand coordination, dexterous manipulation and object exploration, human-robot interaction and collaboration, tactile and force sensing. I am interested in supervising students with an engineering, computer science or behavioural sciences background on the following topics: Creating computational models of human creativity Creating computational models of decisional agents l.jamone@qmul.ac.uk Email Mastodon https://lorejam.wixsite.com/crisp Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing - Previous Next

< Back Dr Tara Collingwoode-Williams Goldsmiths iGGi Alum Tara is an IGGI PhD student from Goldsmiths University taking her Mphil/PhD in Intelligent Games/Game intelligence with a focus on Avatar Embodiment and Interaction within Virtual Reality. Before this she graduated with a Bsc in Creative Computing. Over the years, her interdisciplinary profile has enabled her to work as a Technical Support and Researcher with many organisations in relation to her research, such as UCL, Great Ormond Street Hospital, George Mason Serious Games Institute in the United States where she also co-lectured a XR Games Module and, more recently as an Associate Lecturer in Goldsmiths University teaching Unity based XR experience development. Currently, she is contracting for USTech as an Assistant UX researcher at Facebook whilst completing her PhD program. With this rise in demand for Head Mounted Displays (HMDs), so is the need to create Embodied Shared Virtual Environments (ESVE) where users may experience authentic social interactions. Tara’s research presents an exploratory examination of Embodiment - meaning the subjective feeling of owning a virtual representation in VR, and specifically Consistency in Embodiment - relating to how we prioritize and syncronise objective attributes of embodiment (i.e avatar representation) in order to create ESVEs which supports more intuitive social interaction. The goal is to understand how different technical setups could have a psychological impact on participants' experiences in ESVE. This research hopes to inform development of successful social interaction in a variety of applications in VR, ranging from training to gaming. Tara presently holds a position as Lekturer in VR at Goldsmiths, Universtiy of London. tc.williams@gold.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/tara-collingwoode-williams-81141776/ LinkedIn BlueSky Github Featured Publication(s): Delivering Bad News: VR Embodiment of Self Evaluation in Medical Communication Training The impact of self-representation and consistency in collaborative virtual environments G487 (P) Is clinician gaze and body language associated with their ability to identify safeguarding cues? Evaluating virtual reality experiences through participant choices A discussion of the use of virtual reality for training healthcare practitioners to recognize child protection issues A study of professional awareness using immersive virtual reality: the responses of general practitioners to child safeguarding concerns The effect of lip and arm synchronization on embodiment: a pilot study Themes Applied Games Player Research - Previous Next

< Back Rory Davidson University of York iGGi PG Researcher Available for placement Learning and Strategy Acquisition in Digital Games Given the success and impact of games and the gaming industry, it is unsurprising that it has become the centre of a significant body of academic research and other literature. However, while the cognitive effects of gameplay have been extensively studied, this has typically been done from a “black-box” perspective – that is, looking at the effects of gameplay as a whole upon some other task or metric, such as ability to strategize or proclivity to violence – leaving the inner mechanisms of cognition during gameplay much less understood. In particular, while the idea of learning from games is an area of continued interest in educational psychology, very little literature exists on the subject of how learning in games actually occurs on a cognitive level. This study aims to fill this knowledge gap by examining the ways in which player learning and strategy acquisition occur within games. This examination will have two main hierarchical goals. In the first phase, the study will use experimental methods inspired by analysis of learning methods used in games as well as literature review of more general theories of learning and cognition, such as the dual-process account or the CLARION model, in order to form a model better specialized for the field of digital gaming. In the second phase, it will analyse how such a theory may be put to practical use to inform the design of games and game-like experiences. These two phases can be summed up in the following main research questions: Phase 1: How can strategy acquisition in digital games most effectively be explained as a cognitive process? Phase 2: How can this understanding be put into practice in the development of games with specific desirable characteristics? By linking a more complete understanding of cognition and learning during games with measurable or observable gameplay characteristics, this study will further research on gameplay experience, such as that on immersion. The first phase of research additionally has relevance to the field of AI, in which human responses to difficult and complex problems such as digital games may be mimicked or otherwise used to inform the design of new techniques, as well as to gamification, which attempts to elicit such responses in non-game contexts. rd553@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor: Prof. Paul Cairns Featured Publication(s): Automatic Game Tuning for Strategic Diversity Themes Applied Games Design & Development Player Research - 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 Daniel Berio Goldsmiths iGGi Alum AutoGraff: A Procedural Model of Graffiti Form. (Industry placement at Media Molecule) The purpose of this study is to investigate techniques for the procedural and interactive generation of synthetic instances of graffiti art. Considering graffiti as a special case of the calligraphic tradition, I propose a "movement centric" alternative to traditional curve generation techniques, in which a curve is defined through a physiologically plausible simulation of a (human) movement underlying its production rather than by an explicit definition of its geometry. In my thesis, I consider both single traces left by a brush (in a series of strokes) and the extension to 2D shapes (representing deformed letters in a large variety of artistic styles). I demonstrate how this approach is useful in a number of settings including computer aided design (CAD), procedural content generation for virtual environments in games and movies, computer animation as well as for the smooth control of robotic drawing devices. Daniel Berio is a researcher and artist from Florence, Italy. Since a young age Daniel was actively involved in the international graffiti art scene. In parallel he developed a professional career initially as a graphic designer and later as a graphics programmer in video games, multimedia and audio-visual software. In 2013 he obtained a Master degree from the Royal Academy of Art in The Hague (Netherlands), where he developed drawing machines and installations materializing graffiti-inspired procedural forms. Today Daniel is continuing his research in the procedural generation of graffiti within the IGGI (Intelligent Games and Game Intelligence) PhD program at Goldsmiths, University of London. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Optimality Principles in the Procedural Generation of Graffiti Style SURFACE: Xbox Controlled Hot-wire Foam Cutter The role of image characteristics and embodiment in the evaluation of graffiti Emergence in the Expressive Machine The CyberAnthill: A Computational Sculpture Sketch-Based Modeling of Parametric Shapes Artistic Sketching for Expressive Coding Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks AutoGraff: Towards a computational understanding of graffiti writing and related art forms Kinematics reconstruction of static calligraphic traces from curvilinear shape features Interactive generation of calligraphic trajectories from Gaussian mixtures Sketching and Layering Graffiti Primitives. Kinematic Reconstruction of Calligraphic Traces from Shape Features Expressive curve editing with the sigma lognormal model Dynamic graffiti stylisation with stochastic optimal control Computer aided design of handwriting trajectories with the kinematic theory of rapid human movements Generating calligraphic trajectories with model predictive control Learning dynamic graffiti strokes with a compliant robot Computational models for the analysis and synthesis of graffiti tag strokes Towards human-robot gesture recognition using point-based medialness Transhuman Expression Human-Machine Interaction as a Neutral Base for a New Artistic and Creative Practice Themes Game AI - Previous Next

< Back Prof. Marian Ursu Goldsmiths Supervisor Marian Ursu has a first degree in Computer Science, a PhD in Artificial Intelligence, and has worked over the past twenty years in the development of new forms of mediated expression and interaction, in the space of convergence of digital technology with creative practice. He worked at Goldsmiths, University of London, pioneering “creative computing”, a term denoting a fundamental link between digital technologies, the arts and media. At York, he led the development of the Interactive Media subject area in the department of Theatre Film Television and Interactive Media, inherently interdisciplinary, building on Computer Science, User Experience Design, Media Practice and Cultural Studies. He is a co-founder and the Director of the Digital Creativity (DC) Labs ( https://digitalcreativity.ac.uk ), a centre of excellence in impact-driven research in creativity for games, narrative media and the rich space of media convergence that lies in between, and Co-Director of XR Stories ( https://xrstories.co.uk ), a creative industries partnership working across film, TV, games, media arts, heritage, advertising and technology to champion a new future in storytelling, in which he leads on Research and Development. His personal research is situated in the area of narrative experiences in scree media – shared screens (film, TV), personal screens (games, social media), stories in VR, XR narrative experiences – drawing from and building on established narrative art-forms and media including film and TV, radio, theatre, and opera. One of his key research objective is to explore the creative process that emerges in dialogue between humans and machines (AI). On one hand, this is necessary for the authoring of more complex narrative experiences that truly exploit the affordances of interactive and immersive digital media technologies. On the other hand, this is a yet poorly untapped space of opportunities, potentially conducive to significant findings. He is particularly interested in supervising students interested in exploring creativity in dialogue with AI and/or the development of novel narrative experiences, in topics including: Conceptualising the space of interactive storytelling Developing authoring tools and techniques for interactive storytelling Creating new forms of narrative engagement Analysing the concept of creativity in interactive media which emerges in conversation with AI Research themes: Narrative Games; Narrative Experiences Storytelling with Convergent Media Object-Based Media Computational Creativity Live mediated experiences (performance, sports, esports) Entertainment media and mental health Games and Theatre marian.ursu@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/marianursu/?originalSubdomain=uk LinkedIn BlueSky Github Themes Creative Computing Player Research - Previous Next

< Back Dr Athen Ma Queen Mary University of London Supervisor Athen Ma is an innovator in interdisciplinary approaches to the study of communities and networked ecosystems. She is particularly interested in finding out how the structure and dynamics of communities evolve over time and what kind of mechanics that help underpin cohesion in communities. Her research has been published in world-leading journals, with recent works revealing the organisation of collaborative science in the UK (in PNAS highlight), uncovering how ecological networks rewire under drought (front cover of Nature Climate Change ), and how agricultural ecosystems are resilient to changes in farming management (in Nature Ecology and Evolution ). Online multiplayer games naturally form a platform for social relationships to develop, and deciphering the social structure and dynamics of the communities formed will provide insights into many aspects in games, ranging from users engagement and retention to team formation. For example, matchmaking enables users to find other players who share similar profiles, interests as well as skills and personality; has been seen as an important tool for establishing and maintaining a thriving gaming community. Athen is keen to explore novel ways to use advances in social network analysis to investigate player communities in games across multiple network scales, so as to better understand their formation and evolution. Findings from this research will help identify/predict the type of social interactions that will promote the level of engagement among players and community cohesion, paving the way for designing in-game activities that will foster long-time engagement and retention. athen.ma@qmul.ac.uk Email Mastodon https://sites.google.com/site/athenma2015/ Other links Website LinkedIn BlueSky Github Themes Game Data Player Research - Previous Next