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SUMMARY:Kirk Lecture:  Machine-Learning Enabled Imaging: From Microscopy t
 o Medical Imaging to Astronomy - Rebecca Willett (University of Chicago)
DTSTART:20211027T150000Z
DTEND:20211027T160000Z
UID:TALK164641@talks.cam.ac.uk
DESCRIPTION:<p>In many scientific and medical settings\, we cannot directl
 y observe images of interest\, such as a person&rsquo\;s internal organs\,
  the microscopic structure of materials or cells\, or distant stars and ga
 laxies. Rather\, we use MRI scanners\, microscopes\, and satellites to col
 lect indirect data that require sophisticated algorithms to form an image.
  Historically\, these methods have relied on mathematical models of simple
  image structures to improve the quality and resolution of the resulting i
 mages. In this talk\, I will describe recent efforts to harness vast colle
 ctions of images to train computers to learn more complex models of image 
 structure\, yielding more accurate and higher-resolution images than ever.
  These new methods lead to new insights into designing neural networks in 
 a principled manner to jointly leverage both training data and physical mo
 dels of how imaging data is collected. We will conclude with a discussion 
 of some of the main open questions and exciting new directions in this eme
 rging field.</p>
LOCATION:Seminar Room 1\, Newton Institute
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