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< Back Dr Martin Balla Queen Mary University of London iGGi Alum Before starting his PhD Martin studied Computer Science at the University of Essex. His main interest is artificial intelligence and its application to all sort of problems ranging from computer vision to game AI. He likes spending his spare time with various activities which mainly involves reading, playing video games and skateboarding. Martin's PhD thesis focuses on Reinforcement Learning agents that can adapt to changes in the reward function and/or changes in the environment. His work investigates how agents can transfer their knowledge to changes in the environment, such as new rewards, levels or visuals. Outside of his main research direction, Martin is involved with the Tabletop games framework (TAG), which is a collection of various tabletop games implemented with a common API with a focus on various game-playing agents (including RL). TAG brings various challenges to RL agents compared to search-based agents, such as complex action spaces, unique observation spaces (various embeddings), multi-agent dynamics with competitive and collaborative aspects, and lots of hidden information and stochasticity. m.balla@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/martinballa LinkedIn BlueSky https://martinballa.github.io Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Illuminating Game Space Using MAP-Elites for Assisting Video Game Design PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games TAG: Pandemic Competition Task Relabelling for Multi-task Transfer using Successor Features TAG: A tabletop games framework Design and implementation of TAG: a tabletop games framework Evaluating generalisation in general video game playing Evaluating Generalization in General Video Game Playing Analysis of statistical forward planning methods in Pommerman Themes Game AI - Previous Next

< Back Callum Deery University of York iGGi Alum Callum is a researcher and game developer investigating how real-time player experience measurement can be used to drive adaptive games. Aiming to embed player experience questionnaires into games in a way that doesn’t break immersion and presence, his PhD is focussed on leveraging the wide range of existing player experience questionnaires to improve games ability to adapt to players. This will involve exploring the states of immersion and presence: What is necessary to maintain them? What experiences can players reflect on without breaking immersion? How do we embed a questionnaire into an in-development game without disrupting the player experience? callum.deery@gmail.com Email Mastodon https://cfdj.itch.io/ Other links Website LinkedIn BlueSky Github Supervisors: Dr James Walker Dr Anna Bramwell-Dicks Themes Accessibility Design & Development Player Research - Previous Next

< Back Sahar Mirhadi University of York iGGi PG Researcher Available for post-PhD position Sahar Mirhadi is a final-year PhD researcher investigating how video games support during challenging times. Her contributions have been published in the Proceedings of the ACM Conference on Human-Computer Interaction, and she has presented at Devcom on transforming the complexity of turn-based games into a strategic advantage. She is also a passionate Magic: The Gathering player, collaborating with competitive Magic team Worldly Counsel to convert tournament insights into a deeper understanding of player motivations and team dynamics. Sahar is also a Safe In Our World Ambassador, a recipient of the Magic: The Gathering New Perspectives Grant for Marginalised Players, and a member of the Birds of Paradise collective. A description of Sahar's research: Sahar's PhD research project investigates the specific aspects of games that facilitate coping for players during difficult life experiences. Building on earlier work that mapped broad links between game aspects and coping strategies, Sahar’s first study showed that games can support a variety of coping strategies, including emotion-focused, avoidance, and meaning-focused coping. However, questions remained about how these effects occur across different gaming contexts. To address this, her second study employed in-depth interviews and a grounded theory approach with players of Disco Elysium, Darkest Dungeon and Stardew Valley. The findings led to the development of the Games as Dynamic Coping Systems theory, which posits that specific aspects of video games scaffold a diverse range of coping strategies for players facing personal difficulties. The model highlights the dynamic interplay between what the player brings (e.g., prior experiences, needs, skills) and what the game provides (such as Narrative, Game Environment and Character Interactions). Through this interaction, players develop coping strategies, and the outcomes from coping feed back into their ongoing gaming and life experiences. While the grounded theory offered a deeper understanding of how specific game aspects support various coping strategies, it also revealed a gap: the temporal dynamics of coping. Sahar’s ongoing work aims to explore how players transition between coping strategies over time and what factors shape these transitions. Her overall aim is to provide a deeper understanding of specific aspects within games that support coping, shedding light on the potential benefits and limitations of video games during times of difficulty. sm2904@york.ac.uk Email https://linktr.ee/saharmirhadi Mastodon Other links Website https://www.linkedin.com/in/saharmirhadi/ LinkedIn https://bsky.app/profile/saharmirhadi.bsky.social BlueSky Github Supervisors: Dr Alena Denisova Dr Jo Iacovides Themes Player Research https://www.youtube.com/watch?v=0nTTCR25O0Y Previous Next

< Back Tamsin Isaac University of York iGGi PG Researcher Available for placement Tamsin has been a lifelong gamer ever since receiving her first Game Boy and has always been fascinated by how people engage with games—both emotionally and behaviourally. She joined the iGGi CDT in 2023 after completing a BSc and MSc in Psychology at the University of Plymouth, where she developed a growing interest in how psychological principles such as motivation and disengagement apply not just to players, but to the systems they interact with. Her PhD research focuses on limited-time events (LTEs) in digital games—temporary content used to drive engagement and re-engagement. By exploring how LTEs influence player engagement, disengagement, and return play in live-service games, her work aims to bring clarity to this rapidly evolving area of game design. She is currently developing a cross-platform taxonomy of LTEs through large-scale content analysis of over 1,000 top-charting mobile and PC games. Alongside this, she is conducting an ongoing diary-plus-interview study to explore how players experience these events in everyday play. Tamsin’s research investigates how different LTE formats affect sustained engagement, disengagement, and re-engagement, with the goal of informing more ethical and effective event design for both players and developers. She is open to Knowledge Exchange opportunities with game studios interested in analysing live-service events, player behaviour, and re-engagement strategies using live data or design insights. When not writing about or analysing games, Tamsin enjoys baking, reading, playing cosy indie games, and quietly grinding dailies. tamsin.isaac@york.ac.uk Email Mastodon http://www.tamsinisaac.com Other links Website http://www.linkedin.com/in/tamsinisaac LinkedIn https://bsky.app/profile/tamsinisaac.bsky.social BlueSky Github Supervisor: Prof. Paul Cairns Themes Applied Games Design & Development Player Research https://www.youtube.com/watch?v=n32ngtGYNQ8 Previous Next

< Back Dr Laurissa Tokarchuk Queen Mary University of London iGGi Research Collaboration Coordinator Supervisor Laurissa Tokarchuk is a senior lecturer and researcher working on playful ways of exploring and integrating virtual and real world space. Her primary focus is looking at engaging ways of creating and interacting with AR content in games and incorporating physical sensors for increasing playability in mobile games. Her interests also include merging AI with mobile and social sensing to detect events and behaviours in crowds and games, and the use of technology to promote learning/well-being. Her research has resulted in the widely used SensingKit framework, best poster awards, media appearances in the Guardian and BBC (Royal Institution Christmas Lectures). She is particularly interested in supervising students on the following topics: AR/VR games for learning and cognition design for promoting behaviour change understanding and designing for player behaviour and curiosity in games Research themes: Game AI Games with a Purpose Computational Creativity Player Experience laurissa.tokarchuk@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/~laurissa/ Other links Website https://www.linkedin.com/in/laurissa-tokarchuk-27aa3214/ LinkedIn BlueSky Github Themes Applied Games Creative Computing Game AI Immersive Technology Player Research - Previous Next

< Back Ruizhe "Jay" Yu Xia Queen Mary University of London iGGi PG Researcher Available for placement Ruizhe has bachelor degrees in Mathematics and Physics and a master's degree in Artificial Intelligence. After a short time as a consultant he decided to pursue research into what got him into AI in the first place: game agents. He enjoys games of all kinds, but strategy and RPG games occupy a sizeable portion of his collection. AI agents that perform with superhuman skill in increasingly complex games have appeared in recent years, but these agents are not always useful to game developers. Players within a game exhibit significant variance in their skill levels and play styles. Therefore, game agents with similar variance would better represent the player base. The research Ruizhe proposes will focus on three areas: measuring skill and play styles, developing game agents that mimic a range of human play styles and skill levels, and making these agents human-like. Upon successful completion, this research will improve the game development process via automated playtesting and will enable the development of AI agents that are more engaging and interactive. r.yuxia@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/ruizheyuxia/ LinkedIn BlueSky Github Supervisor: Prof. Simon Lucas Dr Jeremy Gow Themes Game AI Game Data - 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 Yu-Jhen Hsu Queen Mary University of London iGGi Alum I have always been interested in automation specifically within strategy games, starting from civilization 5. I have a background in Artificial Intelligence with a Master of Science degree from Queen Mary, University of London, with a focus on Game AI, Computer Vision and Machine Learning/Deep Learning. My research interests involve Game AI improvement in real-time turned-based games with the help of data science techniques. A description of Yu-Jhen's research: This project has two goals. Firstly, to improve the performance of MCTS (Monte Carlo Search Tree) implementation. Secondly, the goal is focused on building an AI agent that is able to win the game but also provide feedback information/data about it’s decisions to the players and designers. In order to achieve the goal, the plan of the project is to use different data science skills to enable the game AI agent to understand the utility of different actions and decrease the time needed for making decisions. The data collected can also help the game AI agent explain it’s behaviors, hence provided useful information/data for its users and designers. y.hsu@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/yujhenhsu/ LinkedIn BlueSky Github Supervisors: Dr Diego Pérez-Liébana Dr Raluca Gaina Featured Publication(s): Why Choose You?-Exploring Attitudes Towards Starter Pokémon Tribes: a new turn-based strategy game for AI research MCTS Pruning in Turn-Based Strategy Games. Themes Game AI Game Data - Previous Next

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

< Back Prof. Alex Wade University of York Supervisor Alex Wade is a psychologist working in the field of human cognitive neuroscience. He uses a combination of structural and functional brain imaging, electrophysiology, psychophysics and big data analysis to ask how we see, solve problems and make decisions. His most recent work in the domain of video games focuses on what we can learn about global cognitive health and player personality from the analysis of large MOBA datasets in collaboration with Riot games (League of Legends). He is particularly interested in supervising students with a psychology or neuroscience background in the areas of: Using commercial video games to measure cognition and personality How the brain responds to solo- and group gameplay Can we use video games to monitor and modify real-world cognition, behaviour and mental health Research themes: Game Analytics Games with a Purpose Computational Creativity E-Sports Player Experience The neuroscience of gaming alex.wade@york.ac.uk Email Mastodon https://www.york.ac.uk/psychology/staff/academicstaff/alex-wade/ Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing Esports Game Data Player Research - Previous Next

< Back George Long Queen Mary University of London iGGi PG Researcher Available for placement George is an IGGI PhD student interested in AI assisted game design, particularly in how it can be used to assist in the creation and balancing of game mechanics. After graduating with a BSc in Computer Science at the University of Essex, he joined IGGI in 2021 to be able to research how Artificial Intelligence can be applied specifically to reduce the prevalence of Min-Maxing in Role-Playing Games. A description of George's research: My research focuses on the concepts of Min-Maxing and Meta in Role-Playing Games, and how we can use AI assisted game design to reduce their prevalence. Min-Maxing in Role-Playing Game refers to the idea of building a character in a Role-Playing Game by maximising their positive traits while minimising negative ones, often through exploiting game mechanics. This can cause optimal strategies to emerge which not only have the potential to upset the game balance, but when these strategies become prominent enough in the community to form a Meta, it can have wider consequences such as the shunning of players deemed not to be using optimal strategies, and loss of creative choice when building characters. There are two methods I am looking into to reduce the effectiveness of Min-Maxing. The first is using AI to discover these Min-Maxed strategies. Secondly, how AI can be used in the game balancing process to identify and modify the mechanics which enable these strategies. Currently, I am focusing on the first method, with my research looking into how we can measure the effectiveness of units in combat scenarios to identify which units could be considered unbalanced. g.e.m.long@qmul.ac.uk Email Mastodon http://www.longhouse.dev Other links Website https://www.linkedin.com/in/georgelonghouse/ LinkedIn BlueSky Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Themes Design & Development Game AI Game Data - Previous Next

< Back Remo Sasso Queen Mary University of London iGGi PG Researcher I hold a BSc and MSc in Artificial Intelligence at the University of Groningen (NL) and am currently a PhD student at the Queen Mary University of London under the supervision of Paulo Rauber. In addition to my academic work, I have worked as a Machine Learning engineer, and am currently the Head of AI at xDNA, an AI/Cybersecurity-based start-up from the Netherlands. Here I'm leading the initiative Project Aletheia, where we develop AI-driven tools to optimize the workflow of professional fact-checkers, with the overarching goal of ensuring information integrity in the world. Foundation World Models and Foundation Agents for Reinforcement Learning My research focuses on developing reinforcement learning algorithms that are both scalable and sample-efficient through Bayesian methods and model-based approaches, recently with a particular emphasis on Large Language Models (LLMs). My previous research focused on principled, efficient and scalable exploration algorithms for reinforcement learning, e.g. Poster Sampling for Deep Reinforcement Learning (ICML 2023), where we developed a reinforcement learning algorithm that can be considered state-of-the-art in Atari games. Currently I'm particularly interested in the integration of LLMs in the reinforcement learning framework, both as decision-making agents and simulators. My current research, called "Foundation World Models and Foundation Agents for Reinforcement Learning" investigates this integration in-depth and shows that large models show significant potential in various reinforcement learning tasks, ranging from decision-making in stochastic environments to serving as world models. r.sasso@qmul.ac.uk Email https://remosasso.github.io/ Mastodon Other links Website https://www.linkedin.com/in/remo-sasso-b9837a1ba/ LinkedIn BlueSky https://github.com/remosasso Github Supervisor: Dr Paulo Rauber Featured Publication(s): VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts Making Connections: Neurodevelopmental Changes in Brain Connectivity after Adverse Experiences in Early Adolescence Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Simultaneous multi-view object recognition and grasping in open-ended domains Posterior Sampling for Deep Reinforcement Learning Themes Game AI - Previous Next