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< Back Dr Athanasios Vasileios Kokkinakis University of York iGGi Alum Videogame Correlates of Real-Life Cognitive Traits Video-games have been increasingly gaining momentum and popularity, both with the public but also with the scientific world who has seen their usefulness in multiple areas. Researchers have been making bold claims of Videogames increasing Intelligence monopolizing the public’s attention and taking it away from what Videogames are excellent at; serving as diagnostic tools examining constructs such Reaction Times, Memory and fluid Intelligence. The sharp decline of the aforementioned concepts has been linked to multiple diseases such as the prodrome of Schizophrenia, Alzheimer’s and Dementia. Moreover, their measurement has been linked to important life outcomes such as Academic Achievement, Time in Unemployment, Unwanted Pregnancies and Mathematical Achievement among others. In my doctoral thesis I have correlated these constructs with the massively played video-game League of Legends. By cross-validating Psychometric measurements with Video-game metrics we can possibly identify at risk populations and stage Health Interventions or even identify “gifted” children or children that lag behind at an early age and place them in appropriate training curricula. He acquired his BSc in Psychology from the University of Bangor and he then went to complete his MSc in Cognitive Neuroscience at the University of York. In his first experiment he attempted to see whether “expert video-gamers” would show less Attentional Resources when compared to a control group of non-gamers and whether a short training session of approximately a week had any effects on the non-gamer group. His MSc, although not related to gaming, gave him valuable experience with EEG and MEG which he hopes to incorporate into his future experiments. In his most recent experiments he correlated psychometric Intelligence with Videogame Scores, more specifically League of Legends Tiers. He believes that these scores may give us insight on multiple developmental trajectories for instance healthy aging. athanasios dot kokkinakis *at* z)!gmail*com Email https://www.researchgate.net/profile/Athanasios-Kokkinakis Mastodon Other links Website https://www.linkedin.com/in/athanasios-kokkinakis-8b79101a4 LinkedIn BlueSky Github Prof. Alex Wade Prof. Peter Cowling Featured Publication(s): Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports Videogame Correlates of Real Life Traits and Characteristics. Exploring the relationship between video game expertise and fluid intelligence Temporal and spatial localization of prediction-error signals in the visual brain What's in a name? Ages and names predict the valence of social interactions in a massive online game MEG adaptation resolves the spatiotemporal characteristics of face-sensitive brain responses Predicting skill learning in a large, longitudinal MOBA dataset 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 Time to die 2: Improved in-game death prediction in dota 2 Themes Esports Game AI Player Research Research Gate Google Scholar 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 Karl Clarke Queen Mary University of London iGGi PG Researcher Available for placement Karl Clarke is a PhD researcher focused on how virtual environments influence social interaction. He was born in England, grew up in the Middle East, and returned to the UK for university. He holds a Bachelor's and a Master's degree in Audio Technology. During the COVID-19 lockdown, he began exploring virtual reality after getting access to a headset, which led to a shift in focus toward social VR. He is now part of the Intelligent Games and Game Intelligence (iGGi) doctoral programme, where his research looks at how spatial layouts and group behavior are shaped by virtual environments in free-standing social settings. Outside of his research, Karl runs SONAR, a music group hosted in VRChat that uses social VR for live performance and shared listening experiences. Through this project, he has independently learned game development skills in 3D modelling, scripting, and a small amount of graphics programming. He is currently looking to collaborate with VR studios or social platforms working on immersive and social experiences. karl.clarke@qmul.ac.uk Email Mastodon https://linktr.ee/llamahat Other links Website https://www.linkedin.com/in/karl-clarke-york/ LinkedIn https://bsky.app/profile/llamahat.bsky.social BlueSky Github Supervisors: Themes Design & Development Immersive Technology 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 Dien Nguyen Queen Mary University of London iGGi PG Researcher Available for placement I graduated from the University of California, Irvine with a BSc in Computer Game Science and a Minor in Statistics. My undergraduate thesis focused on augmenting Monte Carlo tree search with a value network trained through a self-play framework similar to AlphaZero. During my undergraduate degree, I became interested in the intersection of games and artificial intelligence—applying methods of reinforcement learning, graphical models, and knowledge representation to game playing and game design. My long-term goal is to work on the problem of formalizing game elements, representing game systems in a way that allows for automatic reasoning and inference. I also enjoy playing games where I can customize and theorycraft my playstyle to satisfy certain gameplay fantasies while beating the game. My current research is within the field of Automated Game Design Learning, an emerging field in AI research with the purpose of learning game design models through playing. The current strategy is to play out the full game in thousands of iterations, which can be impractical for complex games with large state space and computationally expensive forward models. My research will focus on applying Go-Explore—a recent exploration paradigm that outperforms many state-of-the-arts—to improve the efficiency of automated playtesting of tabletop games by using an archive of interesting game states to reduce the time needed for self-play. The research will be primarily conducted within the TAG framework and aim to be game-agnostic. On successful completion, this research will improve game development cycles, resulting in higher-quality games, and potentially give unique insights into the game design process. d.l.nguyen@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor: Dr Diego Pérez-Liébana Featured Publication(s): Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis Themes Applied Games Creative Computing Design & Development Game AI - Previous Next

< Back Dr Myat Aung University of York iGGi Alum Immersion is a state in which players are engaged to a degree of total absorption that inhibits the ability to correctly report one’s surroundings or time. Present theory on immersion has developed a coherent model that provides sufficient evidence to distinguish itself from other cognitive concepts such as presence, attention, selective attention, absorption and flow. However, immersion research thus far has been hindered by difficulties with taking in-vivo measurements of cognition and physiological responses during videogame play. This presents an ideal opportunity for implementations of neuroimaging methods to carry out such real time measurements of attention, as well as other cognitive processes and their roles in videogame immersion. Using various combinations of neural and physiological methods such as skin conductance, eye tracking, electroencephalography and even functional magnetic resonance imaging, it is now possible to obtain richer data in immersion research. The goal of this project is to apply such methods in order to better define and measure videogame immersion, identify the cognitive processes and hierarchical models that are involved in immersion and ultimately contribute to the literature in videogame immersion. Though neuroimaging is limited by statistical sensitivity, challenging experimental logistics and non-ideal lab environments, they are still presently the best tools available to obtain fine-grain data of attention and the many other cognitive components of immersion. Such knowledge would contribute significantly to a better understanding of effective development of videogames, as well as educational tools. I am an MPsych Psychology graduate from the University of York, having studied Psychology, Cognitive Neuroscience & Neuroimaging for four years. My Master’s research was primarily in vision, attempting to manipulate and record parahippocampal responses to visual stimuli selected parametrically by computer algorithms. During my degree I also spent much of my time researching videogames, studying the literature on the effects of videogame play on sleep, and working with a IGGI PhD student as a lab assistant. Between my degree and my PhD, I have also been working as a data analyst at Digital Creativity Labs researching skill learning in large gaming populations from Riot Games’ League of Legends. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Different rules for binocular combination of luminance flicker in cortical and subcortical pathways Investigating the non-disruptive measurement of immersive player experience The trails of just cause 2: spatio-temporal player profiling in open-world games Predicting skill learning in a large, longitudinal MOBA dataset Themes Game AI - Previous Next

< Back Dr Dino Ratcliffe Queen Mary University of London iGGi Alum Teaching AI agents transferable skills for game playing My research focuses on the ability of an AI agent to be able to evaluate the various skills it would need to master a game, such as in an FPS (first person shooter) like doom. If the agent can learn to cluster actions that may split into strategies such as attacking enemies, gathering ammo/health and avoiding enemy fire this information could then be used in similar games. This information would also provide a base for being to evaluate players on a skill level, giving a much more granular view of their strengths and weaknesses in any of these games. This could then be used for better matchmaking in team games, placing players into teams whose skill sets complement each other. Other applications include being able to guide the player into situations that give them more experience in the areas they are weakest. Dino started a MSci in computer science at the University of Essex in 2011. During the next 4 years, he focused on modules that involved improving technical skills and Artificial Intelligence. He was the winner of the K.F Bowden Memorial prize in two separate years. Dino worked at the London startup Signal Media during the summer of 2014 and continued to work for them part time during my masters year. He graduated with a 1st class degree. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Cross-lingual style transfer with conditional prior VAE and style loss Author's declaration Win or learn fast proximal policy optimisation Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games Clyde: A deep reinforcement learning doom playing agent Themes Game AI - Previous Next

< Back Dr Raluca Gaina Queen Mary University of London iGGi Outreach Coordinator iGGi Alum + Supervisor Dr Raluca D. Gaina is currently a Lecturer in Game AI at Queen Mary University of London, where she obtained her Ph.D. in Intelligent Games and Games Intelligence in May 2021 (in the area of rolling horizon evolution in general video game playing). She completed a B.Sc. and M.Sc. in Computer Games at the University of Essex in 2015 and 2016, respectively. In 2018, she did a 3-month internship at Microsoft Research Cambridge, working on the Multi-Agent Reinforcement Learning in Malmo Competition (MARLO). She was the track organiser of the Two-Player General Video Game AI Competition (GVGAI) 2016-2019 and was the Vice-Chair for Conferences of the IEEE CIS Games Technical Committee in 2020. Her research interests include general video game playing AI, evolutionary algorithms, and tabletop games. r.d.gaina@qmul.ac.uk Email Mastodon https://rdgain.github.io/ Other links Website https://www.linkedin.com/in/raluca-gaina-347518114/ LinkedIn BlueSky https://www.github.com/rdgain Github Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games The n-tuple bandit evolutionary algorithm for automatic game improvement Population seeding techniques for rolling horizon evolution in general video game playing Automatic Game Tuning for Strategic Diversity Analysis of vanilla rolling horizon evolution parameters in general video game playing General video game for 2 players: Framework and competition General Video Game Artificial Intelligence Playing with evolution Rolling horizon evolutionary algorithms for general video game playing Self-adaptive rolling horizon evolutionary algorithms for general video game playing Rolling Horizon NEAT for General Video Game Playing Frontiers of GVGAI Planning Planning in GVGAI Efficient heuristic policy optimisation for a challenging strategic card game General video game artificial intelligence Optimising level generators for general video game AI 'Did you hear that?' Learning to play video games from audio cues Project Thyia: A forever gameplayer Tackling sparse rewards in real-time games with statistical forward planning methods General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms The Multi-Agent Reinforcement Learning in Malm\" O (MARL\" O) Competition VERTIGØ: visualisation of rolling horizon evolutionary algorithms in GVGAI General win prediction from agent experience League of Legends: A Study of Early Game Impact Self-adaptive MCTS for General Video Game Playing The 2016 two-player gvgai competition Introducing real world physics and macro-actions to general video game AI Rolling horizon evolution enhancements in general video game playing Learning local forward models on unforgiving games Themes Game AI - 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 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 Poonam Yadav University of York Supervisor Dr Yadav research is focused on making the Internet of Things (IoT) and edge computing-based distributed systems resilient, reliable, and robust. This is an interdisciplinary research area that requires expertise in system design and integration along with knowledge of sensor systems, wireless networking, and domain and contextual understanding. To achieve resilience and reliability in the area of resource constraints and distributed systems, I focus on coordination and collaboration using interactions among machines, humans and data entities. These interactions could be categorized as machine-to-machine (M2M), machine-to-human (M2H), and human-to-data (H2D), and involve many challenges such as collaborative trust, privacy, legibility and accountability. Dr Yadav is an active reviewer of many top-tier ACM/IEEE IoT and networking conferences and journals. Dr. Yadav leads ACM-W UK professional chapter and is featured as "People of ACM Europe" and among the top ten N2Women Rising Star in Computer networking and communications in 2020. Research themes: E-Sports Use of IoT in Games Gamifications Citizen Science poonam.yadav@york.ac.uk Email Mastodon https://poonamyadav.net Other links Website https://www.linkedin.com/in/pyadav/ LinkedIn BlueSky https://github.com/pooyadav Github Themes Design & Development Esports Game Data - Previous Next

< Back No Item Is an Island Entire of Itself: A Statistical Analysis of Individual Player Difference Questionnaires Link Author(s) N Hughes, P Cairns Abstract More info TBA Link