CREATIVE AI RESEARCH

In 2021, I co-organized and chaired the NeurIPS workshop on Machine Learning for Creativity and Design.

In 2019, I was first author of a creative AI research paper that was accepted to the NeurIPS 2019 Workshop on Machine Learning for Creativity and Design.

2021: Organized NeurIPS ML4CD workshop

In 2021, I was a co-organizer and chair for the NeurIPS workshop on Machine Learning for Creativity and Design (ML4CD), after presenting my research there two years prior. This included reviewing paper and artwork submissions, and selecting invited speakers.

I also manage the online gallery for the accepted AI artworks.

The workshop was co-organized with other AI researchers Tom White (Victoria University of Wellington), Mattie Tesfaldet (McGill University/ MILA), Samaneh Azadi (Facebook AI Research), Daphne Ippolito (University of Pennsylvania/ Google Brain), and David Ha (Google Brain).

2019: Presented AI research

In 2019, I worked as an AI Researcher with the Vision Creative AI Research group at King Abdullah University of Science and Technology (KAUST) in 2019. From this work, I was first author of a paper that was accepted to the NeurIPS 2019 Workshop on Machine Learning for Creativity and Design.

With KAUST professor Mohamed Elhoseiny as my research advisor, I conducted AI research on the problem of evaluating machine-generated art, and showed a promising way to make it scalable.

I was invited to present our paper Towards a Principled Evaluation of Likeability for Machine-Generated Art in a poster session at the NeurIPS 2019 Workshop in Vancouver, BC. You can check out the poster below.

Our associated AI-generated artwork was accepted to the NeurIPS Online AI Art Gallery as well.

FULL CITATION

L. Coleman, P. Achlioptas, M. Elhoseiny. “Towards a Principled Evaluation of Likeability for Machine-Generated Art.” NeurIPS Workshop on Creativity, 2019.