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PRODID:UW-Madison-Physics-Events
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SEQUENCE:1
UID:UW-Physics-Event-6179
DTSTART:20201202T170000Z
DTEND:20201202T181500Z
DTSTAMP:20260415T010433Z
LAST-MODIFIED:20201130T204243Z
LOCATION:Online Seminar: Please sign up for our mailing list at www.ph
 ysicsmeetsml.org for zoom link
SUMMARY:Harnessing Data Revolution in Quantum Matter\, Physics ∩ ML 
 Seminar\, Eun-Ah Kim\, Cornell University
DESCRIPTION:Our desire to better understand quantum emergence drove ef
 forts in improving computing power and experimental instrumentation dr
 amatically. However\, the resulting increase in volume and complexity 
 of data present new challenges. I will discuss how these challenges ca
 n be embraced and turned into opportunities by employing principled ma
 chine learning approaches. The rigorous framework for scientific under
 standing we enjoy in physics makes interpretability an essential featu
 re for machine learning to lead to scientific progress. I will discuss
  our recent results using machine learning approaches designed to be i
 nterpretable from the outset. Specifically\, I will present discoverin
 g order parameters and their fluctuations in voluminous X-ray diffract
 ion data and discovering signature correlations in quantum gas microsc
 opy data as concrete examples.
URL:https://www.physics.wisc.edu/events/?id=6179
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