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nanoHUB: Online Simulation and Data
Presentation Materials | 24 Sep 2020 | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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Portrait of a Black Hole & Beyond
Online Presentations | 26 Aug 2020 | Contributor(s):: Katie L. Bouman
Dr. Bouman, who was part of the Event Horizon Telescope team that captured the first photograph of a black hole, will talk about the challenges of the project.
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Katie L. Bouman
Katie Bouman is a Rosenberg Scholar and an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a...
https://nanohub.org/members/298104
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Jalil Reed
M Jalil Reed is a 4th year civil engineering and mathematics student at Florida A&M University and he is interested in data science research and transportation engineering.
https://nanohub.org/members/296553
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Hands-on Deep Learning for Materials
Tools | 10 Jun 2020 | Contributor(s):: Saaketh Desai, Edward Kim, Vinay Hegde
This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint
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Hae Ji Kwon
Hae Ji Kwon received AS in Engineering from Ivy Tech Community College. She is pursuing a BS in Engineering at Purdue University starting Fall 2020. Her interest is in Mechanical Engineering,...
https://nanohub.org/members/288457
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Rebecca Mosier
Rebecca Mosier is a second-year undergraduate student at Johns Hopkins University. Her majors are Biomedical Engineering and Applied Mathematics & Statistics. She is working on the Data-Driven...
https://nanohub.org/members/288446
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Nathan Killoran
Nathan holds a MSc in Mathematics from the University of Toronto and a PhD in Physics from the University of Waterloo. He specializes in quantum computing, deep learning, and quantum optics.
https://nanohub.org/members/286348
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Hands-on Sequential Learning and Design of Experiments
Online Presentations | 29 Apr 2020 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...
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Synthesis of Graphene by Chemical Vapor Deposition Part II: Data Science + Graphene Synthesis
Online Presentations | 29 Apr 2020 | Contributor(s):: Sameh H Tawfick
Overall, these two lectures are meant to be a general introduction on the opportunities and challenges related to graphene synthesis.
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Repositories and Data Management (2nd offering)
Online Presentations | 22 Apr 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan
This tutorial introduces database infrastructure and APIs for performing different scales of querying. You will learn how to access different suites of information from three prominent databases, and some advanced examples of data visualization and processing will be discussed. The Querying...
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Hands-on Data Science and Machine Learning Training Series
Courses | 21 Apr 2020 | Contributor(s):: Alejandro Strachan, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi
his 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.
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Repositories and Data Management (1st offering)
Online Presentations | 21 Apr 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan
This tutorial introduces database infrastructure and APIs for performing different scales of querying. You will learn how to access different suites of information from three prominent databases, and some advanced examples of data visualization and processing will be discussed. The Querying...
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Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)
Online Presentations | 21 Apr 2020 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...
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Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd offering)
Online Presentations | 21 Apr 2020 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...
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Apr 13 2020
Repositories and data management
Topics covered in this session:Introduction to repository APIsQuerying and advanced plottingOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Zachary McClureRegister for this seminarHands-on...
https://nanohub.org/events/details/1846
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Apr 10 2020
Repositories and data management
Topics covered in this session:Introduction to repository APIsQuerying and advanced plottingOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Zachary McClureRegister for this seminarHands-on...
https://nanohub.org/events/details/1841
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Querying Data Repositories
Tools | 03 Apr 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan
Query database repositories using Python based APIs and tips for managing data
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Machine Learning Workshop for Materials Science
Workshops | 27 Jan 2020 | Contributor(s):: Saaketh Desai
This workshop covers the fundamentals of machine learning and data science, with a focus on material science applications. This workshop includes a hands-on demonstration of the nanoHUB tool Machine Learning for Materials Science: Part 1.
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MSEML: Machine Learning for Materials Science Tool on nanoHUB
Online Presentations | 27 Jan 2020 | Contributor(s):: Saaketh Desai
This talk is a hands-on demonstration using the nanoHUB tool Machine Learning for Materials Science: Part 1.