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Eun-Ah Kim



Cornell University


Machine Learning Quantum Emergence


Decades of efforts in improving computing power and experimental instrumentation were driven by our desire to better understand the complex problem of quantum emergence. The resulting "data revolution" presents new challenges. I will discuss how these challenges can be embraced and turned into opportunities through machine learning. The scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons: (1) quantum mechanics restricts our access to information, (2) inference from data should be subject to fundamental laws of physics. Hence machine learning quantum emergence requires collective wisdom of data science and condensed matter physics. I will review rapidly developing efforts by the community in using machine learning to solve problems and gain new insight. I will then present my group’s results on the machine-learning-based analysis of complex experimental data on quantum matter.


Jan. 5 (Thu), 2023,
(KIAS Bldg #1, Rm 1503)