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Overview
As investigators on a current EPSRC-funded research project, Profs Alison McKay and David Hogg will introduce potential benefits of improved design configuration capabilities, and demonstrate progress on two prototype design tools: one that supports the definition of multiple product configurations while also ensuring consistency across these configurations and a second that is being developed to explore potential applications of machine learning in engineering design.
They will highlight future opportunities for advanced design tools and implications for both engineering design and machine learning communities.
The webinar will conclude with a discussion, led by Dr Mark Robinson, on potential applications of machine learning to simplify and accelerate engineering design processes.
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Day 1
12:00 - Webinar Programme
Start time:
12:00 London
07:00 New York
13:00 Berlin
16:30 New Delhi
20:00 Tokyo
12:00 GMT Webinar begins
13:00 GMT Webinar ends
Professor Alison McKay: Professor of Design Systems, School of Mechanical Engineering, University of Leeds
Alison is Professor of Design Systems in the School of Mechanical Engineering at University of Leeds. She is an inter-disciplinary researcher in engineering design informatics working across disciplinary boundaries with colleagues in computer science, business and organisational psychology. Her research centres on socio-technical aspects of engineering design systems and the networks of organisations that both develop and deliver products to market and support them through life to disposal or reuse. Her research is positioned within the context of stage gate processes that typify current industry practice and aims to facilitate improved modes of working through the exploitation of digital technology and to establish design methods and tools to support systematic evaluation of design alternatives at decision gates.
Professor David Hogg: Professor of Artificial Intelligence, University of Leeds
HoggDavid is Professor of Artificial Intelligence at the University of Leeds. He is internationally recognized for his work on computer vision, particularly in the areas of video analysis and activity recognition. He works extensively across disciplinary boundaries, applying AI in engineering design, biology, medicine and environmental sciences. David pioneered the use of three-dimensional geometric models for tracking flexible structures (e.g. the human body) in natural scenes, and contributed to establishing statistical approaches to learning of shape and motion as one of the pre-eminent paradigms in the field. Current research is on representation and learning of activities from video, specifically models of interaction, and applications of machine learning in science and engineering. Part of this work is exploring the integration of vision within a broader cognitive framework that includes audition, language, action, and reasoning.
Dr Mark Robinson: Associate Professor, Leeds University Business School, University of Leeds
Mark is an Associate Professor at Leeds University Business School and Director of the Socio-Technical Centre. He is an organizational psychologist, but much of his research is interdisciplinary and involves working with other social scientists, engineers, and computer scientists. He adopts a socio-technical approach to examining complex societal and business problems and developing integrated and systemic solutions. He is particularly interested in how social scientists conceptualise and measure human and business performance more effectively, using new techniques to examine complex systems more dynamically, and incorporating more naturalistic data into research.
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