September 25, 2017 UMD Home FabLab AIMLab
MLMR 2017
Machine Learning
for
Materials Research
Bootcamp & Workshop
2017

Last updated: June 26, 2017, 11:24 am EDT

Tuesday June 27, 2017
Instructor: Alex Belianinov (Oak Ridge National Lab)
8:30am

Registration & Breakfast

9:00am

Basics of Python and Data Cleaning

10:30am

Break

12:00pm

Lunch

1:00pm

Data Transformation

3:00pm

Break

6:00pm

Reception and dinner in Kim Rotunda

Wednesday, June 28, 2017
Instructors: Dan Samarov (NIST) and Tim Mueller (Johns Hopkins U.)
8:30am

Registration & Breakfast

9:00am

Supervised learning

10:30am

Break

12:00pm

Lunch

1:00pm

ML Applied to Computational Materials Science

2:30pm

Break

2:45pm

Continue Supervised Learning

5:00pm

End of Day - Dinner on own

Thursday, June 29, 2017
Instructor: Gilad Kusne (NIST/UMD)
8:30am

Registration & Breakfast

9:00am

Unsupervised Learning

10:30am

Break

12:00pm

Lunch

1:00pm

Continue Unsupervised Learning

3:00pm

Break

6:00pm

Dinner & Poster Session

Friday, June 30, 2017
Chair: Ichiro Takeuchi (UMD)

Invited Session: Recent Advances in Machine Learning

8:30am

Registration & Breakfast

9:00am

"Deep Learning and Computer Vision"
David Jacobs (UMD)

9:30am

"Sequential Learning for Materials Science"
Julia Ling (Citrine Informatics)

10:00am

"Designing with Data: How machine learning is morphing human, product, and system design"
Mark Fuge (UMD)

10:30am

Coffee Break

10:45am

"Machine Learning for materials growth: growth dynamics and processing condition-functional property relations using computational and text mining methods"
Rama Vasudevan (Oak Ridge National Laboratory)

11:15am

"Enabling Materials by Design through NSF's DMREF program"
John Schlueter/Eva Campo (NSF)

11:30am

"A New Model for Materials Genome Initiative - Driven Research: High-throughput Experimental Materials Collaboratory (HTEMC)"
Zachary Trautt (NIST)

11:45am

Closing Discussions/Feedback from participants

12:00pm

Departure / Boxed lunch to go

Conference Support

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

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