Quantum Cafe: Eun-Ah Kim

Date & Time

Title: Learning Quantum Matter Data, Synthetic and Real, with AI

Abstract: Decades of efforts in improving computing power and experimental instrumentation were driven by our desire to better understand the complex problem of quantum emergence. However, increasing volume and variety of data made available to us today present new challenges.  I will discuss how these challenges can be embraced and turned into opportunities by employing machine learning. Learning quantum emergence with AI requires collective wisdom of applied math, computer science, and condensed matter physics.  I will discuss interpreting what machine learned from synthetic data and gaining new insights and accelerating discovery from experimental data.

March 20, 2019

Eun-Ah Kim: Learning Quantum Matter Data, Synthetic and Real, with AI

Video Thumbnail

By clicking to watch this video, you agree to our privacy policy.

Advancing Research in Basic Science and MathematicsSubscribe to Flatiron Institute announcements and other foundation updates