Hands-on Data Science and Machine Learning Training Series
Organizers: Alejandro Strachan, Saaketh Desai
This series of workshops introduces participants to important concepts and techniques in data science and machine learning in the context engineering and physical sciences applications. All workshops include hands-on activities, where participants apply the techniques to solve real problems using online resources at nanoHUB, no need to install any software.
Registration and online compute resources are free of charge.
Target audience: The workshops are designed for students, researchers, and industrial practitioners interested in exploring data science in a hands-on manner. The offerings assume little prior experience with machine learning and minimal programming experience. The Spring 2020 series contains introductory material.
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Get ready for the hands-on activities
Click below for pre-workshop instructions.
You will need a nanoHUB account and API keys for some activities.
SPRING 2021 SERIES
20. Active Learning via Bayesian Optimization for Materials Discovery
June 16th 2021, 1:30 PM - 2:30 PM EST
Hieu A. Doan and Garvit Agarwal, Argonne National Laboratory, Online.
19. Batch Reification Fusion Optimization (BAREFOOT) Framework
June 2nd 2021, 1:30 PM - 2:30 PM EST
Richard Couperthwaite, Texas A&M University, Online.
18. A Hands-on Introduction to Physics-Informed Neural Networks
May 26th 2021, 1:30 PM - 2:30 PM EST
Ilias Bilionis, Purdue University, Online.
16. An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
May 12th 2021, 1:30 PM - 2:30 PM EST
Benjamin Afflerbach, University of Wisconsin-Madison, Online.
14. Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
February 3rd 2021, 1 PM - 2 PM EST
Chi Chen, University of California San Diego, Online.
12. U-Net convolutional neural networks for image segmentation: application to scanning electron microscopy images of graphene
January 20th 2021, 1 PM - 2 PM EST
Aagam Shah, University of Illinois at Urbana-Champaign, Online.
FALL/SUMMER 2020 SERIES
10. Machine Learning Framework for Impurity Level Prediction in Semiconductors
November 11th 2020
Arun Mannodi, Argonne National Laboratory, Online.
9. Hands-on deep learning for materials science: convolutional networks and variational autoencoders
October 21st 2020
Vinay Hegde, Citrine Informatics, Online.
8. nanoHUB: Online Simulations and Data
August 8th 2020
at the NIST workshop on Atomistic Simulations for Industrial Needs, Alejandro Strachan, Purdue University
7. Machine Learning in Materials
June 7th 2020
at the Center for Advanced Energy Studies (CAES) and Idaho National Laboratory (INL) C3 Summer Boot Camp, Alejandro Strachan, Purdue University, Online