Data Science and Machine Learning

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Join Policy: Restricted
Created: 09 Sep 2019
Data Science and Machine Learning Logo

Hands-on Data Science and Machine Learning Training Series

  1. data science
  2. machine learning
  3. NCN Group - Machine Learning
  4. neural networks

Organizers: Alejandro Strachan, Arun Mannodi Kanakkithodi, 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.

Fall 2022 Series

31. Introduction to Machine Learning for materials science
September 13, 2022,  1:00 PM - 3:00 PM EDT

Benjamin Afflerbach, Ph.D., University of Wisconsin – Madison and
Ryan Jacobs, Ph.D., University of Wisconsin – Madison

Summer 2022 Series

30. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression
August 10, 2022,  1:30 PM - 2:30 PM EDT

Davis McGregor, Ph.D., Senior Manufacturing Scientist at Fast Radius Inc.

29. Gaussian Process Regression for Surface Interpolation
July 6, 2022,  1:30 PM - 2:30 PM EDT

Zhiqiao Dong, Ph.D. candidate, University of Illinois Urbana-Champaign and
Manan Mehta, Ph.D. candidate, University of Illinois Urbana-Champaign

Spring 2022 Series

28. Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop
April 13, 2022,  1:30 PM - 2:30 PM EDT

Kevin Greenman, Ph.D. candidate, MIT

27. Machine Learning with MATLAB
February 23, 2022,  1:30 PM - 2:30 PM EST

Gaby Arellano Bello, Customer Success Engineer, MathWorks

26. Data Analysis with MATLAB
February 16, 2022,  1:30 PM - 2:30 PM EST

Gen Sasaki, Customer Success Engineer, MathWorks

Fall 2021 Series

25. Integrating Machine Learning with a Genetic Algorithm for Materials Exploration
November 17, 2021,  1:00 PM - 2:00 PM EST

Joe Kern, Georgia Institute of Technology

24. Autonomous Neutron Diffraction Experiments with ANDiE
November 3, 2021,  1:30 PM - 2:30 PM EST

Dr. Austin McDannald, National Institute of Standards and Technology

Summer 2021 Series

23. Debugging Neural Networks
August 11, 2021,  1:30 PM - 2:30 PM EST

Rishi Gurnani, PhD student, Ramprasad Group, Georgia Institute of Technology

22. A Machine Learning aided hierarchical screening strategy for materials discovery
June 21, 2021, 1:30 PM - 2:30 PM EST 

Anjana Talapatra, Postdoctoral Fellow, Los Alamos National Laboratory

21. Active Learning via Bayesian Optimization for Materials Discovery
June 16, 2021,  1:30 PM - 2:30 PM EST

Hieu A. Doan and Garvit Agarwal, Argonne National Laboratory

20. Batch Reification Fusion Optimization (BAREFOOT) Framework
June 2, 2021,  1:30 PM - 2:30 PM EST

Richard Couperthwaite, Texas A&M University

Spring 2021 Series

19. A Hands-on Introduction to Physics-Informed Neural Networks
 May 26, 2021,  1:30 PM - 2:30 PM EST

 Ilias Bilionis, Purdue University

18. Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
May 19, 2021,  1:30 PM - 2:30 PM EST

Ryan Jacobs, University of Wisconsin-Madison

17. An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties
May 12, 2021,  1:30 PM - 2:30 PM EST

Benjamin Afflerbach, University of Wisconsin-Madison

16. Parsimonious Neural Networks Learn Interpretable Physical Laws
April 23, 2021,  1:30 PM - 2:30 PM EST

 Saaketh Desai, Sandia National Laboratories,

15. Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
February 3, 2021,  1 PM - 2 PM EST

Chi Chen, University of California San Diego

14. Convenient and efficient development of Machine Learning Interatomic Potentials
January 27, 2021,  1 PM - 2 PM EST

Yunxing Zuo, University of California San Diego

13. U-Net convolutional neural networks for image segmentation: application to scanning electron microscopy images of graphene
January 20, 2021,  1 PM - 2 PM EST

Aagam Shah, University of Illinois at Urbana-Champaign

12. Unsupervised clustering methods for image segmentation: application to scanning electron microscopy images of graphene
January 13, 2021,  1 PM - 2 PM EST

 Aagam Shah, University of Illinois at Urbana-Champaign

Fall/Summer 2020 Series

11. Machine Learning Framework for Impurity Level Prediction in Semiconductors
November 11, 2020

Arun Mannodi, Argonne National Laboratory

10. Hands-on deep learning for materials science: convolutional networks and variational autoencoders
October 21, 2020 

Vinay Hegde, Citrine Informatics

9. nanoHUB: Online Simulations and Data
August 8, 2020

At the NIST workshop on Atomistic Simulations for Industrial Needs, Alejandro Strachan, Purdue University

8. Machine Learning in Materials
June 7, 2020

At the Center for Advanced Energy Studies (CAES) and Idaho National Laboratory (INL) C3 Summer Boot Camp, Alejandro Strachan, Purdue University

Spring 2020 Series

7. Hands-on Machine Learning and Data Science Training
April 27, 2020

Saaketh Desai, Purdue University

6. Sequential Learning and Design of Experiments
April 20, 2020

Juan Carlos Verduzco, Purdue University

5. Unsupervised Learning: Dimensionality Reduction Via Matrix Decomposition
April 17, 2020

Michael Sakano, Purdue University

4. Supervised Learning 2: Classification and Random Forestss
April 15, 2020

Saaketh Desai, Purdue University

3. Supervised Learning 1: Regression and Neural Networks
April 14, 2020

Saaketh Desai, Purdue University

2. Repositories and Data Management
April 13, 2020

Zachary McClure, Purdue University

1. Intro to Jupyter in nanoHUB, Pandas for Data Organization and Plotting
April 13, 2020

Juan Carlos Verduzco, Purdue University