Deep Learning for Procedural Content Generation in Games
Research using Deep Learning, specifically the unsupervised and supervised approaches involved with Convolutional Neural Networks to determine if in certain situations we can generate content which appears to a standard or beyond of that which is manually created; such as visually stunning and interactive procedural terrain, AI which reacts in a much more robust and lifelike mannerism and art that can be conjured from the simplest inputs.
Graduating with a First Class Honours in Computer Science and subsequently being awarded a full PhD scholarship for research in Deep Learning and Big Data analytics at the University of Hull, after a year transferring to The University of York to continue the research topic, however, applied to games. An outgoing researcher who wishes to continue developing technical skills and new approaches while providing meaningful real-world applications. Extensive range of relevant technical ability developed throughout time in academia. Highly motivated and hardworking, consistently looking to better oneself and others. Driven by self learning and the gripping desire for completion.
Home institution: York
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