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< Back Autoencoding Blade Runner: Reconstructing Films with Artificial Neural Networks Link ​ Author(s) T Broad, M Grierson Abstract ​ More info TBA ​ Link

< Back TAG: Terraforming Mars Link ​ Author(s) RD Gaina, J Goodman, D Perez-Liebana Abstract ​ More info TBA ​ Link

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: ​ Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! ​ There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. Introversion Software Limited

< Back Revealing game dynamics via word embeddings of gameplay data Link ​ Author(s) Y Rabii, M Cook Abstract ​ More info TBA ​ Link

< Back Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Link ​ Author(s) R Sasso, M Sabatelli, MA Wiering Abstract ​ More info TBA ​ Link

< Back Automated motion analysis of adherent cells in monolayer culture Link ​ Author(s) Z Zhang, M Bedder, SL Smith, D Walker, S Shabir, J Southgate Abstract ​ More info TBA ​ Link

< Back Delivering Bad News: VR Embodiment of Self Evaluation in Medical Communication Training Link ​ Author(s) T Collingwoode-Williams, M Gillies, P Nambyiah, C Fertleman, X Pan Abstract ​ More info TBA ​ Link

< Back Generative design in Minecraft: Chronicle challenge Link ​ Author(s) C Salge, C Guckelsberger, MC Green, R Canaan, J Togelius Abstract ​ More info TBA ​ Link

Modelling the interactions in metaverse videogames Theme Player Research Project proposed & supervised by Ignacio Castro To discuss whether this project could become your PhD proposal please email: i.castro@qmul.ac.uk < Back ​ Modelling the interactions in metaverse videogames Project proposal abstract: What is the role of AR and VR technologies in the interactions of participants? Do they foment or reduce social interactions and if so what type of interactions? This project will seek to inform AR and VR enabled videogames by analysing existing online platforms supporting these technologies. The project will collect comprehensive datasets from existing online metaverse platforms and analyse them using a variety of tools and techniques from social network analysis, natural language processing and economics. The findings will then be fed and compared with those from existing AR/VR videogames. Supervisor: Ignacio Castro Based at:

< Back Computer aided design of handwriting trajectories with the kinematic theory of rapid human movements Link ​ Author(s) D Berio, FF Leymarie, R Plamondon Abstract ​ More info TBA ​ Link

< Back Twitchchat: A dataset for exploring livestream chat Link ​ Author(s) C Ringer, M Nicolaou, J Walker Abstract ​ More info TBA ​ Link

Building Player Profiles in Mobile Monetisation: A Machine Learning Approach Theme Game Data Project proposed & supervised by David Zendle To discuss whether this project could become your PhD proposal please email: david.zendle@york.ac.uk < Back ​ Building Player Profiles in Mobile Monetisation: A Machine Learning Approach Project proposal abstract: This project aims to use machine learning techniques to segment and profile mobile gamers in terms of their in-game spending. Estimates suggest that more than 2.6bn people play mobile games globally; that more than 80 billion mobile games are downloaded annually; and that mobile gaming accounts for almost $100bn in transactions every year. Despite the profitability of mobile gaming, little is known about how different kinds of players spend money in mobile games. Informal theories regarding specific differences in gaming are widely espoused: one influential model, for example, posits the existence of a small but profitable layer of heavily-involved 'whales', and much larger groups of smaller-spending 'dolphins' and 'minnows'. However, it is unclear whether this structure really does explain the monetisation of most games; and whether monetisation may vary between games; and between cultural contexts. In this project, we will take a data-driven approach, and apply a variety of machine learning techniques to large datasets of real player transactions. By both applying and developing algorithmic techniques for the analysis of such data, we will help build an understanding of how in-game spending may be profiled. This project would suit a machine learning specialist; a quantitative social scientist, or a data scientist wishing to do impactful work. It will be supervised by David Zendle, one of the world's leading experts on video game monetisation, and may involve one or more industrial partners who will share player data for the project. Supervisor: David Zendle Based at: