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< Back Dr Jo Iacovides University of York Supervisor Jo Iacovides, is a Lecturer in Computer Science at the University of York, UK. Her research interests lie in Human Computer Interaction with a particular focus on understanding the role of learning within the player experience, and on investigating complex emotional experiences in the context of digital play. In addition, she is interested in exploring how games and playful technologies can created for a range of purposes, such as education, citizen science, or wellbeing. She is an active member of the HCI and games community and serves on the Steering Committee for the annual CHI PLAY conference. She has received awards for a work on examining reflection and gaming (best paper, CHI PLAY 2018), evaluating serious experience in games (honourable mention, CHI 2015) and for the game Resilience Challenge, which encourages healthcare practitioners to consider how they adapt safely under pressure (first prize, 2017 Annual Resilience Healthcare Network symposium). She is interested supervising students that have a mix of qualitative, mixed method or design experience that they wish to apply to the study of digital games and playful technologies. Possible topics include exploring the effects of negative emotion in the context of playful approaches to persuasion; or examining how games can support wellbeing (particularly in relation to challenging life experiences). Research themes: Game Design Games with a Purpose Player Experience jo.iacovides@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/people/?group=Academic%20and%20Teaching%20Staff&username=ii Other links Website https://uk.linkedin.com/in/joiacovides LinkedIn BlueSky Github Themes Applied Games Design & Development Player Research - Previous Next

< Back - @ Develop:Brighton 2025 - Doruk Balcı University of York iGGi PG Researcher Available for placement I am a game maker interested in the relationship between player creativity and game design. My work is centered around the transformative capabilities of players to invent their own metagames and play-practices, and how to support this through game design. My other interests include: drawing, literature, making zines and browser games, and playing with tools I don’t really understand. Designing for Appropriative Play How do we make games which we want to be messed with, changed fundamentally beyond our expectations in play? How do we make up rules that are intended to be bent, changed or broken? Why would we want that? Play practices that transform structures, subvert expectations and re-define their contexts are celebrated in many aspects of culture and can lead to personal and meaningful experiences. Yet research on this topic from a game design perspective has been scarce. In my project, I am exploring how we can design game systems that invite players to assume ownership of their play-practices through exploring alternative paradigms of game design. doruk.balci@york.ac.uk Email Mastodon https://fuzul.itch.io Other links Website https://www.linkedin.com/in/doruk-balc%C4%B1-19749a151 LinkedIn https://bsky.app/profile/dorukb.bsky.social BlueSky Github Supervisor: Dr Jo Iacovides Themes Design & Development Player Research - Previous Next

< Back James Gardner University of York iGGi PG Researcher I am a third-year PhD student at The University of York, specialising in computer vision and machine learning for 3D scene understanding. Supervised by Dr William Smith, my research focuses on neural-based vision and language priors in inverse rendering and scene representation learning. I'm particularly interested in neural fields, generative models, 3D computer vision, differentiable rendering, geometric deep learning, multi-modal models, and 3D scene understanding in general. My research has been recognised with publications at prestigious conferences including NeurIPS and ECCV. Currently, I am working as a research fellow on the ALL.VP project, funded by BridgeAI and Dock10, developing relightable green screen performance capture using deep learning and inverse rendering techniques. This work aims to provide greater creative control to film and TV productions without requiring expensive LED volumes or post-production. I hold an MEng in Electronic Engineering from The University of York, for which I was awarded the IET Prize for outstanding performance and the Malden Owen Award for the best-graduating student on an MEng programme. A description of James' research: My research lies at the intersection of computer vision, machine learning, and 3D scene understanding, with a particular focus on neural-based approaches and the integration of vision and language priors. My work spans a range of topics including neural fields, generative models, differentiable rendering, and geometric deep learning. A key theme in my research is the use of 3D inductive biases for inverse rendering, addressing challenges such as illumination estimation, albedo/geometry disentanglement, and shadow handling in complex outdoor scenes. I've made contributions in creating a rotation-equivariant neural illumination model and spherical neural models for sky visibility estimation in outdoor inverse rendering. Additionally, my work extends to learning rotation-equivariant latent representations of the world from 360-degree videos, aimed at advancing the field of 3D scene understanding and developing models with an understanding of core physical principles such as object permanence. Through my research, I aim to build computer systems capable of deeply comprehending the 3D world, utilising self-supervised, generative, and non-generative approaches to push the boundaries of what's possible in computer vision and scene representation learning. james.gardner@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/jadgardner/ LinkedIn BlueSky https://jadgardner.github.io/ Github Featured Publication(s): The Sky's the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and Outside-In Visibility Themes Game AI - Previous Next

< Back Dr Ildar Farkhatdinov Queen Mary University of London Supervisor Dr Ildar Farkhatdinov is a Lecturer in Robotics at QMUL since 11/2016 and a Turing Institute Fellow. He is an internationally leading expert in assistive robotics and human-machine interaction. He is a principle investigator of several projects on wearable robotics, mobility assistance and haptic interfaces (including funding from the UK government on supernumerary robotic limbs and assistive wheelchairs, £500k+). Several of his research works were recognised as the best paper or finalists for best paper awards at leading robotics conferences. Before joining QMUL, he was a postdoctoral research associate at the Human Robotics group of the Department of Bioengineering, Imperial College London (2013-16). He earned Ph.D. in Robotics in 2013 (Sorbonne University, UPMC, France), M.Sc. in Mechanical Engineering in 2008 (KoreaTech, South Korea) and B.Sc. in Automation and Control in 2006 (Moscow University, Russia). He has actively collaborated on a number of large-scale research projects: EPSRC NCNR to create novel robotic solutions for the nuclear industry; EU FP7 BALANCE to develop balance and robotic walking assistance for the elderly; EU FP7 SYMBITRON to develop exoskeleton control for people with spinal cord injury. My research interest relevant to CDT IGGI include serious games for medical applications, as well as using game theory to investigate human-machine interaction. Research themes: Game Design Serious games Virtual reality Game theory i.farkhatdinov@qmul.ac.uk Email Mastodon https://hair-robotics.qmul.ac.uk Other links Website https://www.linkedin.com/in/ildar-farkhatdinov-33075016 LinkedIn BlueSky Github Themes Design & Development Game AI Immersive Technology Player Research - Previous Next

< Back Filip Sroka Queen Mary University of London iGGi PG Researcher Filip is a Computer Science researcher specialising in Game AI. He acquired an Integrated Masters in Computer Science from Queen Mary University of London and is pursuing a PhD in Game AI with iGGi. With a passion for algorithms and problem-solving, he constantly seeks new challenges to enhance his skills. As an avid LEGO collector and investor, he brings a unique blend of technical and creative abilities. He is excited about the potential of the Metaverse and is driven by the role of technology in shaping its future. His research explores the integration of Dynamic Difficulty Adjustment (DDA) into VR rhythm games such as Beat Saber, with the goal of enhancing player skill development and motivation through the application of learning theories. By addressing difficulty spikes, the project creates personalised learning experiences using human-made maps designed to accelerate the learning process. Key components include player evaluation, map segmentation, and procedural generation. The broader aim is to extend these findings to other rhythm games, offering benefits to players, game developers, and the health and fitness industry. f.sroka@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/filip-sroka-134954197/ LinkedIn BlueSky https://github.com/FilipSroka Github Supervisor: Dr Laurissa Tokarchuk Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Andrei Iacob University of Essex iGGi Alum Identifying Immersion in games using EEG and other measures (Industry placement at Sony SIE) The project aims to identify markers for immersion in player’s EEG signals. A few steps towards it include designing an experiment that reduces data noise and helps identify time frames for immersion during gameplay, recording EEG data among other “tests” to improve the accuracy of the state localization on a timeline. This research could prove useful for the games industry in a few ways: - it can provide tools for game testing (e.g. which parts of the game are immersive, which parts lack in that aspect) – thus making it easier to improve the game experience across the board; - it could also be used in making real-time adjustments to games (increase / decrease difficulty levels, pace, etc. to enhance the player’s immersion). Although the EEG data is the main focus of the project, it is not the only one. Correlations will be analyzed between different tests and in-game behaviors that should render even more information regarding the player’s state and mindset during gameplay. This information will be just as valuable and perhaps more readily available for widespread use in the near future. Andrei is a keen programmer and gamer. He graduated with a BSc (Hons) in Computer Science from the University of Essex. Andrei’s research interests are in the field of brain- computer interfaces and computer games. His hobbies include programming, gaming, guitar and skiing. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Player Research - Previous Next

< Back Prasad Sandbhor University of York iGGi PG Researcher Available for placement Prasad is a serious game designer and researcher. He has designed digital, tabletop and hybrid games in diverse areas such as education, healthcare, entrepreneurship, social safety, accessibility and sustainability. He is a part of the ‘Play in Nature’ initiative that crafts playful experiences to connect people with nature around them. He also teaches game design and user experience design. As a multidisciplinary design consultant, Prasad has been involved in conceptualising and creating B2C and B2B digital products for Indian as well as international organisations. His professional experience of 8 years in setting and leading design teams has made him proficient in strategic management of design. Prasad has been able to maintain his secret identity as a freelance author too. He writes short stories and essays in his native language, Marathi. A description of Prasad's research: Prasad’s PhD research explores designing games that facilitate the sensemaking of climate actions among university students. It defines ‘sensemaking’ as a structured process aiding the understanding of alternative pro-environmental actions within complex constraints, involving activities like reflection, brainstorming, and critiquing. The primary objective of his work is to identify game elements that impact players’ ability to make sense of climate actions to articulate design and facilitation guidelines for researchers, designers, and educators from climate change education and communication domains. It also aims to explore the transferability of sensemaking from the game into the real world. As a part of his research, Prasad is designing 3 climate change games using user-centred methods and exploratively evaluating them to see how they help players experience and develop sensemaking. He started with ‘Climate Club’, a tabletop role-playing game dealing with climate action-related decision-making challenges within everyday constraints. Its evaluation showed that the use of curated group setup, relatable contexts, problem-solving mechanic, and explicit mention of climate issues enhances sensemaking while group dynamics and asymmetric role-plays may cause hindrance. Combining these with other literature findings, Prasad designed ‘Climate Club 2.0’, a mini-live action role-playing game (LARP) about planning a climate-friendly holiday which is currently under evaluation. prasad.sandbhor@york.ac.uk Email Mastodon https://linktr.ee/prasadsandbhor Other links Website https://www.linkedin.com/in/prasad-sandbhor/ LinkedIn BlueSky Github Supervisor: Dr Jon Hook Featured Publication(s): Radical Alternate Futurescoping: Solarpunk versus Grimdark Climate Club: A Group-based Game to Support Sensemaking of Climate Actions Radical Alternate Futurescoping: Solarpunk versus Grimdark Themes Applied Games Design & Development - Previous Next

< Back Dr Miles Hansard Queen Mary University of London Supervisor Miles Hansard is a computer vision researcher, working on geometric and statistical methods for 3D scene understanding and rendering. He is also interested in active 3D sensing technologies, including depth cameras, lidar, and millimetre-wave radar. His recent projects include GPU methods for real-time atmospheric effects, commodity radar localization of UAVs, and grasp planning for robotic manipulation. He has also worked on human perceptual processes, including eye-movements, geometric judgements, and binocular stereopsis. Miles Hansard is a Senior Lecturer in computer graphics, and a member of the Vision Group and Centre for Advanced Robotics, at QMUL. He is available to supervise projects in the following areas: Simulation of complex physical effects (e.g. the motion of cloth, fire, and fluids), using machine learning. Physically plausible character animation in complex environments (e.g. slippery terrain), using machine learning. miles.hansard@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/~milesh/ Other links Website LinkedIn BlueSky Github Themes Design & Development Game AI Game Data Immersive Technology - Previous Next

< Back Prof. Nick Pears University of York Supervisor Nick Pears is a Professor of Computer Vision in York’s Vision, Graphics and Learning (VGL) research group. He works on statistical modelling of 3D shapes, with an emphasis on the human face and head. The Liverpool-York Head Model and the associated Headspace training set has been downloaded by over 100 research groups internationally, with the Universal Head Model being downloaded by 50 research groups. His most recent work with his PhD students has focused on semantic disentanglement of 3D images and how to make autonomous vehicles safer and more trustworthy when using computer vision systems. He is assessor for many PhDs including construction of generative models for novel video content using adversarial deep learning techniques. nick.pears@york.ac.uk Email Mastodon https://www-users.cs.york.ac.uk/np7/ Other links Website https://www.linkedin.com/in/nick-pears-90970312/ LinkedIn BlueSky Github Themes Creative Computing Game AI - Previous Next

< Back Connor Watts Queen Mary University of London iGGi PG Researcher Available for placement I am a machine learning research engineer and software developer with commercial experience deploying and maintaining models for start-ups and larger organizations. I have experience researching and developing novel algorithms, as well as designing custom environments for application in domains such as combinatorial optimization, finance and games. c.watts@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/connor-watts-363354232/ LinkedIn BlueSky https://ConnorWatts.github.io Github Supervisor: Dr Paulo Rauber Themes Game AI - Previous Next

< Back - @ Develop:Brighton 2025 - 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. p.c.smithii@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://pjsmith97.github.io/ Github Themes Applied Games Design & Development - 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