All Events

Machine Learning Quantum Matter Data

Workshop

Computational Quantum Physics

When

January 23, 2020
All Day
January 24, 2020
All Day

Where

162 Fifth Avenue, 9th Floor Classroom

160 Fifth Avenue

New York, NY 10010

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.

Agenda

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

Participants

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