"Machine Learning Data from Electronic Quantum Matter" by Eun-Ah Kim (Cornell University)

Date: 
Thursday, January 17, 2019 - 3:30pm to 4:30pm
Series: 
Physics and Astronomy Colloquium

Thursdays, 3:30-4:30 pm

1-434 Physics and Astronomy (map)
Reception from 3:30-4:00 p.m.
(unless otherwise posted)

 

Guest Speaker: Eun-Ah Kim (Cornell University)

 

Talk Title:  “Machine Learning Data from Electronic Quantum Matter”

Abstract:  In recent years, enormous data sets have begun to appear in real-space  visualizations (scanning probes) and reciprocal-space visualizations (scattering probes) of electronic quantum matter. Increasing volume and variety of such data present new challenges and opportunities that are ripe for a new approach: machine learning. However, the scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons: (1) quantum mechanical imaging of electronic behavior is probabilistic, (2) inference from data should be subject to fundamental laws governing microscopic interactions. In this talk, I will review the aspects of machine learning that are appealing for dealing with quantum complexity and present how we implemented a machine learning approach to analysis of scanning tunneling spectroscopy data.

 

For more information, contact Yaroslav Tserkovnyak

We thank the following people for their contributions to the wine fund for the post-colloquium reception:
Professors Katsushi Arisaka, Andrea Ghez, Karoly Holczer, Huan Huang, HongWen Jiang, Per Kraus, Alexander Kusenko, Matthew Malkan, Mayank Mehta, Warren Mori, Ni Ni, Seth Putterman, Yaroslav Tserkovnyak, Vladimir Vassiliev, Shenshen wang, and Nathan Whitehorn.

Location: 
1-434 PAB