Keynote Speakers
 Anna Abraham  University of Georgia
 Kevin Buzzard  Imperial College London
 Simon Colton  University of London
 Maithilee Kunda  Vanderbilt University
 Catholijn Jonker  Leiden University
 Paige Randall North  University of Pennsylvania
 Catherine Menon  University of Hertfordshire
 YangHui He  University of London
 Gemma Anderson  University of Exeter
Anna Abraham: 'The Nature of Human Creativity'
A selective overview, as evidenced by theory and research in psychology and neuroscience, is presented on the nature of human creativity in terms of its core features. Attention will be drawn into some of the key differences that are apparent when comparing human creativity with AIbased forms. These include (A) multiplicity and flexibility within and across domains of creative engagement, (B) endogenous assessment of appropriateness of the creative response, (C) use of the same cognitive toolboxes to creative and noncreative ends, (D) the dynamic engagement of multiple distinct operations in human creativity as well as (E) intraindividual and interindividual differences in the same.
E. Paul Torrance Professor, Torrance Center

Kevin Buzzard: 'Can computers be mathematically creative?'
It's reasonable to believe that in some unspecified future time, computers will be better at research level mathematics than humans. But when will this actually happen  is it ten years away or 100 years away? How will it happen? How can human mathematicians help to make it happen? And what about those who have concerns about this happening at all? Of course I don't really know the answers to these questions but at least I can say something about where we are, and where we might be going, when it comes to modern mathematical research and the computer's understanding of it.
Kevin Buzzard is a professor of pure mathematics 
In the AI subfield of computational creativity, we research how to hand over creative responsibilities to AI systems in arts and science projects. My first (and ongoing) study was in generative mathematics, where I built a system called HR (after Hardy and Ramanujan) which invented mathematical concepts in finite algebras, number theory, graph theory and elsewhere, then found empirical relationships between concepts, expressed as conjectures. The most current version of HR performs automated software engineering and is still heavily influenced by creative reasoning in mathematics. In the talk, I'll describe how the HR projects have influenced the philosophical development of computational creativity, covering notions such as essentially contested concepts, the humanity gap, computational authenticity and the machine condition.
Simon Colton is a professor of computational creativity at Queen Mary University of London and Monash University, having previously been an academic at Imperial College London and Goldsmiths College. He obtained his PhD in Artificial Intelligence from the University of Edinburgh. He is a founding member of the computational creativity research field and is best known for AI systems such as HR for mathematical discovery, The Painting Fool automated artist and the WhatIf Machine for fictional ideation, as well as contributions to the philosophical understanding of creativity in people and machines. 
Maithilee Kunda: 'A computational view of visual imagery in humans and in AI systems'
Visual imagery has been linked to many examples of human creativity, including in mathematical and scientific discovery. However, from a computational perspective, we do not fully understand the lowlevel cognitive representations and operations that enable visual imagery, or how these lowlevel elements can be combined by an intelligent agent to achieve highlevel reasoning. I present a series of computational studies centered on using visual imagery to solve Raven's Progressive Matrices, a widely used test of human intelligence, that begin to answer these questions. I also discuss how people might learn abstract visual imagery skills during concrete spatial play with objects during infancy, and how similar learning experiences can be simulated for AI systems.
Maithilee Kunda is an assistant professor of computer 
Computer proof assistants (also called interactive theorem provers) are software tools that check the correctness of mathematical proofs and assist the user in constructing such proofs. They first began to be developed in the late twentieth century, but have only recently begun to encroach upon mainstream mathematics. I will speak about the history of computer proof assistants, and focus on their success and development in a research program called homotopy type theory. This program uses a speciallydesigned mathematical language and computer proof assistants in which the language is implemented, to do much of the heavy lifting of pushing forward a preexisting research program that has been impeded by its complexity.
Paige Randall North is an assistant professor of 
Catherine Menon: 'Narrative forms in mathematics, engineering and fiction'
Stories are all around us, from between the covers of our favourite book to the way we construct a mathematical proof or describe a software system. I describe how creativity manifests in areas of science traditionally considered to be logical or processdriven, and how our early experiences of story and narrative form influence the way we think about mathematics and software engineering. I also discuss the links between creative writing and mathematics, and the ways techniques from one domain can be used in another.
Catherine Menon is a principal lecturer in robotics 
YangHui He: 'Universes as Bigdata: from Geometry, to Physics, to MachineLearning'
We briefly overview how historically string theory led theoretical physics first to algebraic/differential geometry, and then to computational geometry, and now to data science. Using the CalabiYau landscape  accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades  as a startingpoint and concrete playground, we then launch to review our recent programme in machinelearning mathematical structures and address the tantalizing question of how AI helps doing mathematics, ranging from geometry, to representation theory, to combinatorics, to number theory.
Professor YangHui He is a Fellow of the London 
Gemma Anderson has been working with Alessio 
Workshop: Creativity in Mathematics & AI
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