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Research topic: High-Fidelity Diversity in Data-Driven Generative Systems

My research interests focus on machine intelligence in the creative process and on creativity as a cognitive phenomenon. I study generative machine learning algorithms in a computational creativity setting. In this context I am particularly interested in combining representation learning with symbolic reasoning systems.

Before transitioning into computation I worked independently as a graphic designer, specialising in web, typography and communication for art and culture.

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GAIG Publications

2024

2023

2022

2021

• 2021 Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
  Hagg, Alexander and Berns, Sebastian and Asteroth, Alexander and Colton, Simon and Back, Thomas, in arxiv:2105.04247, 2021.
  pubs-2021  arxiv  computational-creativity  quality-diversity  optimisation
  
  

2020

• ICCC 2020 Bridging Generative Deep Learning and Computational Creativity
  Berns, Sebastian and Colton, Simon, in Proceedings of the 11th International Conference on Computational Creativity (ICCC’20), 2020.
  pubs-2020  iccc  computational-creativity  deep-learning
  
  
• ICCC 2020 Creativity Theatre for Demonstrable Computational Creativity
  Colton, Simon and McCormack, Jon and Cook, Michael and Berns, Sebastian, in Proceedings of the 11th International Conference on Computational Creativity (ICCC’20), pp. 288-291, 2020.
  pubs-2020  iccc  computational-creativity
  
  
• Evo* 2020 Adapting and Enhancing Evolutionary Art for Casual Creation
  Colton, Simon and McCormack, Jon and Cook, Michael and Berns, Sebastian, in International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), pp. 17-34, 2020.
  pubs-2020  evo-star  computational-creativity  art  casual-creators
  
  

2019