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< 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 Previous Next

< Back Dr Pengcheng Liu Queen Mary University of London Supervisor Dr Pengcheng Liu is a Lecturer (Assistant Professor) at the Department of Computer Science, University of York, UK. He is an internationally leading expert in robotics, Artificial Intelligence and human-machine interaction. He has been leading and involving in several research projects, including EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, NSFC, etc. Several of his research works were published on top-tier journals and leading conferences in the fields of robotics and AI. Before joining York, he has held several academic positions including a Senior Lecturer at Cardiff School of Technologies, Cardiff Metropolitan University, UK, a joint Research Fellowship at Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute of Agri-Food Technology (LIAT), University of Lincoln, UK, a Research Assistant and a Teaching Assistant at Bournemouth University, UK. I also held academic positions as a Visiting Fellow at Institute of Automation, Chinese Academy of Sciences, China and Shanghai Jiao Tong University, China. Dr Liu is a Member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Systems, Man and Cybernetics Society (SMC), IEEE Control Systems Society (CSS) and IFAC. He is member of IEEE Technical Committees (TC) on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. He has published over 60 journal and conference papers. Dr Liu serves as an Associate Editor for IEEE Access and PeerJ Computer Science. He received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He was selected as regular Fundings/Grants reviewer for EPSRC, NIHR and NSFC. Dr Liu’s research interest relevant to CDT IGGI include applied games for healthcare and rehabilitation applications, as well as using mixed reality and machine learning for human-machine interactions. He is particularly interested in supervising students with a design, HCI, computer science or behavioural sciences background on the following topics: applied games for healthcare and rehabilitation design for adaptive mixed reality system for physical therapy and neurological rehabilitation design for physical and cognitive behaviour change learning for human intention prediction analysis of mixed reality rehabilitation system with biological signals (EEG, sEMG) pengcheng.liu@york.ac.uk Email Mastodon https://sites.google.com/view/pliu Other links Website https://www.linkedin.com/in/pengcheng-liu-12703288/ LinkedIn BlueSky Github Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Lizzie Vialls University of York iGGi Alum Discrete Models and Algorithms to create a more satisfying and strategic opponents For many 4x and Grand Strategy computer games (e.g. Civilisation, Europa Universalis), the player will be playing against one or more AI opponents. For many games, the AI is not clever enough to stand up to a player without being given the ability to "cheat" - ability to spawn in resources, see what the player is doing, etc. This creates an unsatisfactory opponent for a player, as it gives them opponents that fight through "cheating" over strategy or out-manoeuvring the player. The aim for my PhD is to look into the potential uses of SAT and similar to create a more satisfying and strategic opponent for players to play against in these styles of computer games. To this end, I’ll be identifying potential for improvement regarding my proposal, and once I’ve narrowed down the specifics - be it related to improving how SAT solvers can handle problems, or how better to encode AI into SAT - I will be working on ways to improve AI for turn based strategic games. Lizzie Vialls is a recent Computer Science graduate of University of Leicester, having graduated with a 2:1 and a prize for best third year project, which was the project that fueled her interest in SAT. When not searching for an errant semicolon in her code she can be found working with various online gaming communities, hunched over many a tabletop game, or attempting to make friends with the local feline populace. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Anne Hsu Queen Mary University of London Supervisor Anne Hsu’s research includes machine learning, artificial agents, natural language processing and learning, human decision making, interaction design, and well-being technology. Her interests include developing interactive systems that use machine learning and understanding of human psychology to improve human behaviour. She is particularly interested in supervising students with a machine learning, design, HCI, or behavioural sciences background on the following topics: understanding and designing for curiosity in games design for behaviour change motivational/educational games Research themes: Game AI Game Design Games with a Purpose Player Experience Gamification anne.hsu@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/anne-showen-hsu LinkedIn BlueSky Github Themes Applied Games Design & Development Esports Player Research - Previous Next

< Back Dr Alena Denisova University of York Supervisor Alena Denisova is a Lecturer in Computer Science at the University of York, UK. She is actively involved in collaborative and interdisciplinary projects that involve conceptualising and measuring user experience of video games and designing and building educational and persuasive interactive media. Her research explores the role of the `placebo effect’ of technology in shaping player experiences, perceived challenge and uncertainty in video games, and, more recently, emotionally impactful player experiences - understanding how these experiences are shaped with the view to inform the design of games that promote these experiences. Alena is an active member of the games HCI community: she is a co-chair of the IEEE Task Force on Automatic Gameplay Evaluation and a member of the Programme Committee for the annual CHI and CHI Play conferences. She is interested in supervising students that have qualitative, mixed method or design experience that they wish to apply to studying digital games. Possible research topics include exploring what makes choices in games meaningful for players, how perceived uncertainty, risk-aversion, and luck affect decision making in games, and how skill is acquired and advanced throughout while playing video games. She is also keen to work with students who wish to work on games with a purpose. For instance, designing and developing games that promote informed decision-making about moral and ethical choices, such as promoting sustainable lifestyle, reflecting on important real-life issues, developing personally, etc. alena.denisova@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/alenadenisova/ LinkedIn BlueSky Github Themes Applied Games Design & Development Player Research - Previous Next

< Back Dr Mihail Morosan University of Essex iGGi Alum Computational Intelligence and Game Balance. (Industry placement at MindArk) Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. This process can, and should, be aided by automated systems, as machines have proven to be capable of finding innovative ways to complement human intuition and inventiveness. When man and machine cooperate, better products are created and the world has only to benefit. My research seeks to find, test and assess methods to apply computational intelligence to human-led game balance. Early research has proven that AI can successfully aid game designers in analysing the viability of various game rules and I intend to document this and polish the techniques that will result from my work. To achieve this, I am making use of cutting edge algorithms, powerful AI techniques and novel methods. Most of the current work done involves the use of evolutionary algorithms, as well as statistical analysis and evaluation of intelligent agents in various video games. Programmer (with a focus on optimisation and quick deliverables, mostly due to competitive experience), gamer (games are fun, relaxing and a great social experience), technology consumer (comes with the programmer bit) and all around happy guy stumbling through the world. Once ended up in a management internship at a bank thinking the application was for a programming position. And another time told an interviewer that "buying and eating a burger to solve hunger" is a legitimate problem-solving skill. Somehow received an invitation to the next interview stage. me@morosanmihail.com Email Mastodon Other links Website https://uk.linkedin.com/in/morosanmihail LinkedIn BlueSky Github Featured Publication(s): Automating game-design and game-agent balancing through computational intelligence Lessons from testing an evolutionary automated game balancer in industry Genetic optimisation of BCI systems for identifying games related cognitive states Online-Trained Fitness Approximators for Real-World Game Balancing Evolving a designer-balanced neural network for Ms PacMan Speeding up genetic algorithm-based game balancing using fitness predictors Automated game balancing in Ms PacMan and StarCraft using evolutionary algorithms Themes Design & Development Game AI Player Research - Previous Next

< Back David Hull University of York iGGi Manager iGGi Admin I have worked at the University of York since October 1995, almost all of it in the Department of Computer Science. My various roles have included Laboratory and Facilities Manager, Technical Manager and, most recently, Project Manager. Outside work, I have been a change-ringer for almost 50 years, and am currently a member of the band that rings the bells weekly at York Minster. I am also an accredited teacher of bellringing. I do parkrun most weeks, alongside the occasional 10k and half marathon, like to watch cricket, and play the clarinet and piano. iggi-admin@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Dr Adrián Barahona-Ríos University of York iGGi Alum From 2018 and in collaboration with Sony Interactive Entertainment Europe, Adrián is researching strategies to increase the efficiency in the creation of procedural audio models for video games by using DSP and machine learning approaches. His main research interests, applied to the synthesis of sound effects, are generative deep learning (GANs, RNNs and VAEs) to synthesise raw audio and machine learning to find out the best parameters for a synthesiser to generate a target sound. Adrián has been enthusiastic about sound and more specifically about game audio since he began his studies. By the time he completed an HND in Creative Media Production in Madrid, he started working in the industry as a recording engineer in an ADR studio for the Spanish localisation of video games (such as Fallout 4, Until Dawn or Just Cause 3). He moved from Spain to the UK in 2015 to take a BA (top-up) in Music Production at the Southampton Solent University and an MSc in Sound Design at the University of Edinburgh immediately after. During that journey, he focused his career in procedural audio and explored ways to create models for interactive applications by using different techniques. adrian.barahona.rios@gmail.com Email Mastodon https://www.adrianbarahonarios.com/ Other links Website https://www.linkedin.com/in/adrianbarahona LinkedIn BlueSky https://github.com/adrianbarahona Github Supervisor Dr Tom Collins Featured Publication(s): Deep Learning for the Synthesis of Sound Effects NoiseBandNet: controllable time-varying neural synthesis of sound effects using filterbanks Sonifying energy consumption using SpecSinGAN SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs Synthesising Knocking Sound Effects Using Conditional WaveGAN Perception of emotions in knocking sounds: An evaluation study Perceptual Evaluation of Modal Synthesis for Impact-Based Sounds Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Creative Computing Game Audio - 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 Dr Matthew Whitby University of York iGGi Alum Matthew Whitby is a games designer, and player experience academic investigating how games can shape how perspectives on a small or grand scale. In particular, his work considers how we can make the development of perspective challenging processes easier for game developers. Previously, Matthew has published his undergraduate dissertation within the Games Journal, which explored the creation and design of Games Installations. Games that make full use of their surrounding space, and in fact incorporate the real world with its digital counterpart. In addition, he’s worked with Motek Medical, a rehabilitation company based in Amsterdam, where he developed socially focused multiplayer applications. More recently, he attended CHI Play 2019 to present the foundational study of his PhD titled: “One of the Baddies All Along: Perspective Challenging Moments in Games”. He continues to develop this idea forward, while developing games (both digital and table-top) in his spare time. Matthew’s work hopes to answer; how games can challenge a player’s perspective, and if this is a phenomenon that can be intentionally designed for? matt_whitby@hotmail.com Email Mastodon https://www.matt-whitby.com Other links Website https://www.linkedin.com/in/matthew-whitby-b324ab83 LinkedIn BlueSky Github Supervisor(s): Prof. Sebastian Deterding Dr Jo Iacovides Themes Design & Development 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 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