Machine Learning Quantum Matter Data
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Andrew Millis, Ph.D.CCQ Co-Director, Flatiron Institute
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Eun-Ah Kim, Ph.D.Cornell University
This workshop will bring together a select group of experimentalists and theorists to address the new challenges raised by the prospect of using machine learning methods to analyze the new, complex, large datasets emerging in many classes of experiments. The workshop will feature a mix of experimental and theoretical talks, along with plenty of time for discussion. Our aim is to reach a consensus on the current status, near-term possibilities, and conceptual and practical challenges of this new area of science.
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Thursday, January 23
8:00 - 9:00 AM Breakfast 9:00 - 9:15 AM Eun-ah Kim and Andy Millis Welcome and Overview 9:15- 12:00 PM Scanning and Tunneling Spectroscopy 9:15 - 10:00 AM (30+15) Jenny Hoffman (Harvard University) Local correlations in disordered materials with neural networks 10:00 - 10:45 AM (30+15) Abhay Pasupathy (Columbia University) Blind deconvolution approach to pattern recognition in STM images 10:45 - 11:15 PM (30) Break 11:15 - 12:00 AM (30+15) Erica Carlson (Purdue University) Fractal views on quantum materials: learning physics from surface probe images Slides 12:00 - 2:00 PM Lunch and Discussion 2:00 - 4:15 PM Theory 2:00 - 2:45 PM (30+15) Roger Melko (Perimeter Institute/Waterloo) Reconstructing quantum states with generative models Slides 2:45 - 3:30 PM (30+15) Titus Neupert (University of Zurich) Many problems and some solutions for machine learning quantum matter 3:30 - 4:15 PM (30+15) Andrew Wilson (New York University) Bayesian Deep Learning Applied to Scientific Data 4:15 - 4:30 PM (15) Break 4:30 - 6:00 PM Quantum Gas Microscopy 4:30 - 5:15 PM (30+15) Giacomo Torlai (CCQ) Generative modeling of quantum simulators Slides 5:15 - 6:00 PM (30+15) Eugene Demler (Harvard University) Solving real world quantum problems with simulators and machine learning Friday, January 24
8:00 - 9:00 AM Breakfast 9:00 - 12:00 PM Scattering and TEM 9:00 - 9:45 AM (30+15) Ray Osborn (Argonne National Laboratories) Challenges in Studying Correlated Disorder 9:45 - 10:30 AM (30+15) Simon Billinge (Columbia University) Machine learning materials science from experimental and theoretical data Slides 10:30 - 11:00 AM (30) Break 11:00 - 11:45 AM (30+15) David Mueller (Cornell University) Phase Retrieval for Petavoxel Electron imaging as a big and deep data problem Slides 11:45 - 12:30 PM (30+15) Sergei Kalinin (Oak Ridge National Laboratories) Causal learning from structural electron and scanning probe microscopy data 12:30 - 2:00 PM Lunch and Informal Discussion 2:00 - 2:45 PM (30+15) Markus Greiner (Harvard University) Quantum simulation of the doped Hubbard model and machine learning 2:00 - 4:00 PM Eun-Ah Kim (Cornell University) Summary and Discussion -
Sayan Basak Purdue University Simon Billinge Columbia University Michael Cao Cornell University Giuseppe Carleo CCQ Erica Carlson Purdue University Iris Cong Harvard University Eugene Demler Harvard University Antoine Georges CCQ Paul Ginsparg Cornell University Markus Greiner Harvard University Kaylie Hausknecht Harvard University Jenny Hoffman Harvard University Sergei Kalinin Oak Ridge National Laboratory Eun-Ah Kim Cornell University Guangqi Li Columbia University Mike Matty Cornell University Roger Melko University of Waterloo, Perimeter Institute Andy Millis CCQ Lukas Muechler CCQ David Muller Cornell University Titus Neupert University of Zurich Ray Osborn Argonne National Laboratories Miles Stoudenmire CCQ Giacomo Torlai CCQ Andrew Wilson NYU