< 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). Email n.penaperez@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link 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 Ross Fifield University of York iGGi PG Researcher Available for post-PhD position I am a user-centred games designer and researcher working across the practical and theoretical dimensions of play. I hold both a BA and MA in Games Design from Falmouth University and have experience teaching further and higher education courses in games development. My work explores the relationship between design innovation, player psychology, and emerging technologies, with a particular focus on how players discover, evaluate, and sustain meaningful play experiences. Currently undertaking a PhD through the iGGi programme, my research investigates matchmaking beyond conventional skill-based or algorithmic systems. I am interested in how social context, player motivation, behavioural patterns, and lived experience influence whether a game feels “right” for a player at a given moment. Through this work, I aim to develop more nuanced approaches to player matching and game discovery that better account for the emotional, social, and contextual dimensions of play. My practice draws on speculative and disruptive design methodologies to challenge established, top-down models of game development. I am particularly interested in how live and contextual player data can support more adaptive, empathetic, and sustainable systems for player engagement, retention, and community building. Alongside this, I maintain professional interests in affective psychology and digital heritage. As a player, I take an agnostic approach to genre, though I have a particular affinity for First Person Shooters, MMOs, sandbox games, and live-action roleplay. I welcome opportunities to discuss research collaboration, applied matchmaking systems, and future-facing approaches to player experience design. I am seeking placement opportunities with studios and organisations that are open to collaboration on live, data-driven projects focused on social play, player engagement, matchmaking and game discovery. My goal is to contribute meaningfully to real-world game development while refining methodologies that support more empathetic, inclusive, and dynamic player experiences. Email ross.fifield@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Joe Cutting Prof. Paul Cairns Themes Player Research https://www.youtube.com/watch?v=3yVD-7mRlk4 Previous Next

< Back Alex Fletcher Queen Mary University of London iGGi Alum Alex Fletcher is a freelance audio engineer and junior game developer working on understanding the perceived flow and player experiences in mobile rhythm games and how a dynamic difficulty adjustment system would improve these experiences. The function of EEG and other biosensors as an additional measurement of player experience is of particular interest as further research in its use as an adaptive system. Other areas of research interest include game-based learning and games with a purpose. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game Audio Player Research - Previous Next

< 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 Email s.riis@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game AI Game Data - Previous Next

< Back Michael Aichmüller Queen Mary University of London iGGi Alum My background lies in physics and statistical mathematics with a later specialization in optimization in the fields of Reinforcement Learning (RL) and Causal Inference. My first encounters with RL occurred during my Masters when studying how to create strong policies in perfect information games using algorithms, such as MinMax, MCTS, DQN, and later AlphaZero variants. My favorite game application remains the board game ‘Stratego’. In the meantime I investigated the estimation of causal parents influencing a target variable from interventional datasets for my Master’s thesis. Specifically, how well Deep Learning estimations could replace exponentially scaling graph search methods with approximations requiring only polynomial runtime. A description of Michael's research: My research focuses on the state-of-the-art in game-playing solutions for imperfect information games (think games like Poker, Stratego, Liar’s Dice etc.). I am particularly interested in the application of No-Regret (and related) methods which seek to learn those actions that provided the most benefit (or least regret) compared to the benefit all possible actions provided on average. These methods learn such via iterative play to find a Nash-Equilibrium (NE), a game-theoretic concept comparable to an optimal policy known from Single-Agent RL, but for all partaking players at once. Particularly, variants of Counterfactual Regret Minimization (CFR) remain the state-of-the-art algorithms for computing NEs in 2-player zero-sum games due to their success in tabular form so far. Yet, prohibitive complexity and memory scaling bars them from large-scale applications. Hence, research of recent years seeks to couple CFR (and other No-Regret methods) with function approximation, such as Deep Learning, to scale up the size of applicable games with already notable successes (Deepstack, Libratus, Pluribus, DeepNash). My research seeks to contribute to this endeavour by first analyzing the specifics of established methods and finding ways to introduce Hierarchical RL concepts to No-Regret learning. Please note: Updating of profile text in progress Email m.f.aichmueller@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Simon Lucas Dr Raluca Gaina Themes Applied Games Game AI - Previous Next

< Back Dr David Gundry University of York iGGi Alum Using Applied Games to Motivate Speech Without Bias (Industry placement Lightspeed Research) Eliciting linguistic data faces several difficulties such as investment of researcher time and few available participants. Because of this, many language elicitation studies have to make do with few subjects and coarse sampling rates (measured in months). It would be ideal if a game could crowd-source relevant linguistic data with frequent, short game sessions. To this end, David’s research is looking into how games shape and elicit players’ linguistic behaviour. The established design patterns of gamification do not apply to a domain that lacks a ‘correct’ answer like language or personal beliefs and attitudes. David’s research shows how a player’s strategic goals will systematically bias data collection. It also shows how to design around this. The conclusion: The player’s choice of how to express a given datum must be strategically irrelevant in the game. David can remember the halcyon days when he had the free time to play games. Now he’s doing a PhD and has a one-year-old. He has an background in linguistics. He loves writing expressive code and designing clever little games. He wants to show that research games can be fun, not just effective. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Trading Accuracy for Enjoyment? Data Quality and Player Experience in Data Collection Games Designing Games to Collect Human-Subject Data Validity threats in quantitative data collection with games: A narrative survey Busy doing nothing? What do players do in idle games? Intrinsic elicitation: A model and design approach for games collecting human subject data Themes Applied Games - 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 Website LinkedIn Mastodon BlueSky GitHub Other Link 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 Cade McCall University of York Supervisor Cade McCall is an experimental psychologist. He uses games and virtual environments to study emotion, cognition, and behaviour during threatening experiences. His work explores how threat unfolds over time as revealed by dynamics in motion tracking data, psychophysiological measures, and experience-sampling. McCall is interested in supervising projects with a psychological focus, including: ● human interactions with autonomous systems ● the use of games to manipulate emotions ● social interactions within games Research themes: Games with a purpose Player experience Game analytics Email cade.mccall@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game Data Player Research - Previous Next

< Back Philip Smith Queen Mary University of London iGGi PG Researcher Available for placement I was born and raised in Bermuda, a small island in the Atlantic Ocean with an approximate population of 65,000 people. I finished my undergraduate degree in Computer Science with a Specialist in Game Design at the University of Toronto. For my Master's degree, I studied Computer Games Technology at City, University of London. My goal is to help expand the use of video games from purely recreational activities to viable avenues for aiding in real world problems. A description of Philip's research: My research will be focusing on maximizing player engagement in gamified citizen science as a continuation of my Master's thesis. 'Citizen science' is the practice of employing volunteers from the general public for the collection and/or processing of data with respect to a scientific project. Gamified citizen science projects have relied upon prolonged engagement from volunteers, but the number of long-term participants have been unsatisfactory in current projects. This project attempts to address the lack of sufficient volunteer engagement in gamified citizen science projects. The aim is to build a framework meant to guide game designers in creating an engaging citizen science video game based on the values set by Self-Determination Theory (SDT). These values adhere to the theory’s concept of intrinsic and extrinsic motivators of engagement. Intrinsic motivation relies on the factors of player autonomy, competence, and relatedness during gameplay. Extrinsic motivation relies on external incentives to core gameplay such as in-game rewards. As part of my research, I am evaluating multiple game design frameworks focused on Applied Games and identifying the merits and flaws each have when applied to a citizen science context. The information I gather will formulate a prototype of the Framework that will be iterated upon through design workshops, development, and playtesting. Email p.c.smithii@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Design & Development - Previous Next

< Back Dr Marko Tot Queen Mary University of London iGGi Alum Hello! I'm Marko, and welcome to my page! As a part of the IGGI programme and Game AI research group, I'm working on adapting Statistical Forward Planning methods for complex environments. Statistical Forward Planning methods have proven to be effective in some simpler domains and, without requiring any prior learning, they provide a good out of the box AI algorithm. However, while these algorithms shine in certain games, they struggle to perform well in cases where the reward received from the game is sparse. In games where it takes a series of optimal actions to reach the goal, without any significant feedback from the environment in between, their performance drops significantly. My research is centered on solving this problem through automatic sub-goal generation and utilisation of local learned forward models. Creation of the sub-goals could be used to simulate the feedback from the environment and give regular rewards to the agent even in sparse and complex environments. I started my journey in video games when I got my first PC at the age of six, and at that point it was decided that I'm going to make a career out of it. So here I am, ~20 years later, a PhD. student at Queen Mary University of London, trying to make AI agents that can play games, and regularly spending too much time playing games under the excuse that it's all for 'research purpose'. Email m.tot@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): Adapting a world model for trajectory following in a 3d game Bootstrap Your Own Teacher: Online Policy Distillation for Multi-Game Reinforcement Learning Statistical Forward Planning Algorithms World and human action models towards gameplay ideation Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search Making Something Out of Nothing: Monte Carlo Graph Search in Sparse Reward Environments What are you looking at? Team fight prediction through player camera Themes Game AI - 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 Email poonam.yadav@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Design & Development Esports Game Data - Previous Next

< Back Joshua Kritz Queen Mary University of London iGGi PG Researcher Available for placement Graduated in Applied Mathematics in computer science, however my love for games pushed me to dedicate myself for studying them. This led me to brave many areas of knowledge, such as: psychology, design, education, production and entrepreneurship. My work as a teacher allowed me develop many of these skills in practice, besides invoking a new perspective about the world. On a personal level, I love new experiences that can teach me new knowledge and, most important, I am very open minded and easy to talk to! I believe discussion leads to enlightenment. A description of Joshua's research: Card games, in particular Trading Card Games (TCGs) thrive on using the synergy between the cards to create emergent and interesting gameplay. However, these games usually have hundreds of different cards to create such rich experience, with some older TCGs featuring thousands of different cards. With such a huge amount of different cards playtesting these games present a big challenge. In example a new set of Magic the Gathering takes over 3 years of development to be fully designed. But even considering simpler exemplars like Dominion or Assencion can be difficult to balance, and both games are known to need a few expansions of experience to indeed provide a well balanced experience. One way to make this task faster and easier is to use automated agents to playtest the game exhaustively and provide much needed data. Whilst this would assist card game development, it is not used in practice, the playtesting of card games is still completely done by players. Even with systematic playtesting there is a limit of how much of the possibilities humans can test. However, implementing playtesting of card games have two big challenges, which are the main reason it has not been implemented in practice yet. First: Automated agents are not great when playing a game with too many variables (different cards) Second: The possible combinations of cards used in a deck or set of a single game is huge. My research aim to address the second issue by using a theory of synergy between cards to reduce the search space necessary to properly evaluate a card game. Email j.s.kritz@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Raluca Gaina Featured Publication(s): A Conceptual Model for the Analysis of Investigation Elements in Games A FAIR catalog of ontology-driven conceptual models O Que São Jogos A FAIR catalog of ontology-driven conceptual models A Conceptual Model for the Analysis of Investigation Elements in Games A Vocabulary of Board Game Dynamics Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis When 1+ 1 does not equal 2: Synergy in games Towards an Ontology of Wargame Design Themes Applied Games Design & Development Game AI https://www.youtube.com/watch?v=StVXD9EbXzQ Previous Next