YCCSA is a community of researchers drawn from different departments developing novel mathematical, computational and analytical methods and tools for the analysis and modelling of complex systems.
Welcome to YCCSA. We are an open community of cross-disciplinary researchers, drawn from a diverse range of departments across the University at York. We are working together, developing and applying novel methods and tools to analyse, model, explore, and solve complex problems that cannot be tackled by one discipline alone.
Some 70 YCCSA staff and research students are co-located in purpose-built dedicated research space in the Ron Cooke Hub on the new Heslington East campus. Other YCCSA members reside in their home departments, and are very much part of the YCCSA community.
As well as carrying out cross-disciplinary research, we also reflect on the process itself, and develop new ways to help bring researchers together and bridge the gaps between disciplines. We each bring our own different and valuable perspectives to the challenges of researching complex systems.
Do browse our site to find out more about our people, our past and current research projects, and our engagement activities and events.
Come and take your sabbatical in YCCSA
Are you interested in cross-disciplinary work and want to work with cross-disciplinary scientists - then consider taking your sabbatical in YCCSA. Please contact the member of staff you would like to work with and they will sponsor your application.
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See Research page
YCCSA Summer Seminar Series
Date and time: Thursday 13th May 2.30 pm
Speaker: Gordana Dodig Crnkovic University of Technology, Sweden
Title: Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines
Via Zoom passcode 508044
Abstract: The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This talk explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. I propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with a symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, I describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. This presents a contribution to the epistemology of the contemporary philosophy of nature.