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SUMMARY:IMX Seminar Series - Machine Learning of Quantum Emergence
DTSTART:20210301T131500
DTEND:20210301T141500
DTSTAMP:20260415T042731Z
UID:b49dd2d2c1a22ec856e478324cfffae4b707cc88299de51dd14dea52
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Eun-Ah Kim\, Cornell University\, USA\nDecades of effort
 s in improving computing power and experimental instrumentation were drive
 n by our desire to better understand the complex problem of quantum emer
 gence. However\, the increasing volume and variety of data made available 
 to us today present new challenges. I will discuss how these challenges ca
 n be embraced and turned into opportunities by employing machine learning.
  The rigorous framework for scientific understanding physicists enjoy thro
 ugh our celebrated tradition requires the interpretability of any machine 
 learning essential. I will discuss our recent results using machine learni
 ng approaches designed to be interpretable from the outset. Specifically\,
  I will present discovering order parameters and its fluctuations in volum
 inous X-ray diffraction data and reconstructing atomic structure from 4D s
 canning transmission electron microscopy data.\nBio: Eun-Ah Kim is a profe
 ssor of physics at Cornell. She received her undergraduate degrees in phys
 ics at the Seoul National University and joined the University of Illinois
  at Urbana-Champaign to pursue her PhD. After a postdoctoral position in S
 tanford she started her independent research group at Cornell as an assist
 ant professor. Since 2014\, she is in her current role as tenured professo
 r. Her numerous awards well exceed the length of a short bio\, and she is 
 well recognized in the field for her creative approach to materials hostin
 g strong electronic correlations and her creative approach using machine l
 earning techniques to challenge and improve materials modeling.
LOCATION:Zoom https://epfl.zoom.us/j/95940364570 https://epfl.zoom.us/j/95
 940364570
STATUS:CONFIRMED
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