October 17, 2017 UMD Home FabLab AIMLab
MLMR 2016
Host: University of Maryland
Dates: June 28 - July 1, 2016
Location: Jeong H. Kim Engineering Building, University of Maryland, College Park, MD
Sponsors: National Institute of Standards and Technology (NIST)
University of Maryland

Bootcamp

The bootcamp consists of three days of lectures and hands-on exercises covering a range of data analysis topics from data pre-processing through advanced machine learning analysis techniques. Example topics include:

  • Identifying important features in complex/high dimensional data
  • Visualizing high dimensional data to facilitate user analysis.
  • Identifying the fabrication 'descriptors' that best predict variance in functional properties.
  • Quantifying similarities between materials using complex/high dimensional data

The hands-on exercises will focus on demonstrating practical use of machine learning tools on real materials data. Attendees will learn to analyze a range of data types from scalar properties such as material hardness to high dimensional spectra and micrographs.

Workshop

The workshop will feature talks by top researchers in the field as well as open discussions in which attendees can discuss their data analysis needs with experts. Program for the Workshop is to be announced on a later date.

Sponsors

Organizers

A. Gilad Kusne A. Gilad Kusne National Institute of Standards & Technology Materials Measurement Science Division
Alexei Belianinov Alexei Belianinov Oak Ridge National Laboratory Center for Nanophase Materials Sciences
Daniel Samarov Daniel Samarov National Institute of Standards and Technology Information Technology Laboratory
Tim Mueller Tim Mueller John Hopkins University Department of Materials Science & Engineering
Stefano Ermon Stefano Ermon Stanford University Department of Computer Science
Ichiro Takeuchi Ichiro Takeuchi University of Maryland, College Park Department of Materials Science & Engineering

Colleges A. James Clark School of Engineering
The College of Computer, Mathematical, and Natural Sciences

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