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< Back Dr Josh Reiss Queen Mary University of London Supervisor Josh Reiss investigates transformative technologies focused around audio production and sound design. He has published more than 200 scientific papers (including over 50 in premier journals and 5 best paper awards), and co-authored two books. His research has been featured in dozens of original articles and interviews on TV, radio and in the press. He is a Fellow and former Governor of the Audio Engineering Society. He co-founded the highly successful spin-out company, LandR, and recently formed a second start-up, FXive. He maintains a popular blog, YouTube channel and twitter feed for scientific education and dissemination of research activities. Prof. Reiss has a strong interest in games research, especially procedural audio content generation. Procedural content generation supports creation of rich and varied games, maps, levels, characters and narrative elements. But sound design has not kept pace with such innovation. Often the visual aspects of every object in the scene may be procedurally rendered, yet sound designers still rely on huge libraries of pre-recorded samples. This approach is inflexible, limited and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, computational, or tailored only to a narrow class of sounds. Machine learning from the sample libraries, to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. He welcomes the opportunity to supervise students interested in this or related topics. Research themes: Procedural Content Generation Game Audio and Music Game AI Game Design Computational Creativity Player Experience joshua.reiss@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/~josh/index.htm Other links Website https://www.linkedin.com/in/reissjoshua/ LinkedIn BlueSky Github Themes Creative Computing Game AI Game Audio - Previous Next

< Back Cooperative games with partial observability Link Author(s) PR Williams, D Perez-Liebana, SM Lucas 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. Forma

< Back Better Dead than a Damsel: Gender Representation and Player Churn Link Author(s) Lauren Winter, Sarah Masters Abstract More info TBA Link

< Back Evaluating virtual reality experiences through participant choices Link Author(s) M Murcia-López, T Collingwoode-Williams, W Steptoe, R Schwartz, ... Abstract More info TBA Link

< Back VERTIGØ: visualisation of rolling horizon evolutionary algorithms in GVGAI Link Author(s) R Gaina, S Lucas, D Perez-Liebana Abstract More info TBA Link

< Back UCL+ Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound Link Author(s) J Goodman, A Vlachos, J Naradowsky Abstract More info TBA Link

< Back The @artbhot Text-To-Image Twitter Bot. Link Author(s) A Smith, S Colton Abstract More info TBA Link

< Back From Theory to Behaviour: Towards a General Model of Engagement Link Author(s) V Bonometti, C Ringer, M Ruiz, A Wade, A Drachen Abstract More info TBA Link

< Back The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods Link Author(s) O Withington, L Tokarchuk Abstract More info TBA Link

< Back Bodystorming in SocialVR to Support Collaborative Embodied Ideation Link Author(s) C Gonzalez Diaz, R Fiebrink, P Perry, R Gibson, B Martelli, S Deterding, ... Abstract More info TBA Link

< Back The need for the human-centred explanation for ML-based clinical decision support systems Link Author(s) Y Jia, JA McDermid, N Hughes, MA Sujan, T Lawton, I Habli Abstract More info TBA Link