If Donald Rumsfeld were a scientist: talk by Professor Stephen Emmott

Duration: 1 hour 10 mins
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Description: Professor Stephen Emmott, Head of Computational Science at Microsoft Research Cambridge and author of '10 Billion', delivers a public lecture at Madingley Hall on 2 April 2014.

Prof Emmott leads an international, interdisciplinary research programme and scientific team, centred on Microsoft's Computational Science Laboratory, in Cambridge, whose goal is to make, enable and accelerate transformational advances in science in areas of societal importance. His team is responsible for developing the Madingley Model – so called because it was first proposed during a meeting at Madingley Hall between UNEP-WCMC and the CEES group at Microsoft Research. The Madingley Model is a global ecosystem model (GEM) which simulates how the structure and function of ecosystems at global scales emerges from the underlying ecology of individual organisms.

The lecture is chaired by Professor Paul Linden, G I Taylor Professor of Fluid Mechanics at Cambridge, and introduced by Dr Rebecca Lingwood, Director of Continuing Education.

Please note that the lecture proper begins at the 04:08 minute point in the video.
 
Created: 2014-04-24 12:13
Collection: Madingley Lectures
Madingley Lectures
Publisher: University of Cambridge Institute of Continuing Education
Copyright: Professor Stephen Emmott
Language: eng (English)
Distribution: World     (downloadable)
Keywords: science; Madingley; biology; scientists; Madingley Model;
Credits:
Author:  Professor Stephen Emmott
Orchestra:  Professor Stephen Emmott
Categories: iTunes - Science - Environment
Explicit content: No
Aspect Ratio: 16:9
Screencast: No
Bumper: UCS Default
Trailer: UCS Default
 
Abstract: The past five decades have been characterised by spectacular scientific advances, spanning particle physics, molecular biology and neuroscience. Yet some of the most 'basic' building blocks of biology, the brain and the biosphere remain poorly understood. What are some of the outstanding known unknowns? What might be the key unknown unknowns in some of the most important areas of science? Finally, what might we need to do differently in future to address them?
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