- Organized by
Andrew Millis, Ph.D.CCQ Co-Director, Flatiron Institute
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.
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) 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 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 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) Mikhail Lukin (Harvard University) 5:15 - 6:00 PM (30+15) Eugene Demler (Harvard University)
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 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 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) 2:00 - 4:00 PM Eun-Ah Kim (Cornell University) Summary and Discussion