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Current Training Schedule:
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Fall 2024
Please note that we are transitioning to offering classes on Thursdays, when we have held classes on Wednesdays for many years.
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Class | Date | Time | Location | Training Materials | Registration |
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Intro to MATLAB | Thursday, September 19, 2024 | 10am to 12pm | online | ||
Intro to Parallel Computing | Thursday, June 27thSeptember 26, 2024 | 10am to 12pm | online | ||
Optimizing O2 Jobs | Thursday, October 3, 2024 | 10am to 12pm | in person | ||
Intro to Python | Thursday, October 10, 2024 | 10am to 12pm | online | ||
Parallel Computing with MATLAB | Thursday, July 11thOctober 17, 2024 | 10am to 12pm | onlinein person | ||
Data Management: Computing Strategies and Resources (in collaboration with Countway Library and FAS Research Computing) | Thursday, October 24, 2024 | 10am to 11am | online | ||
O2 Portal - Simplifying the Interaction and Experience of Using an HPC Environment | Thursday, July 18thOctober 31, 2024 | 10am to 12pm | online | ||
Intro to Python | Thursday, August 1November 14, 2024 | 10am to 12pm | online | ||
Intro to O2 | Thursday, August 8November 21, 2024 | 10am to 12pm | in person | ||
RCBio: easy and quick HPC pipeline builder & runner | Thursday, December 5, 2024 | 10am to 12pm | online | ||
Shell Tips and Tricks on O2: HBC Current Topics in Bioinformatics | Wednesday, December 11, 2024 | 1pm to 4pm | online |
Additional classes will be added for the Fall Spring semester.
Class Registration Process
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Classes sponsored by HMS Research Computing but taught by our partner organizations:
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Overview We will introduce parallel and distributed computing with a focus on speeding up application codes. By working through common scenarios and workflows using hands-on demos, you will gain a detailed understanding of the parallel constructs in MATLAB, their capabilities, and some of the common hurdles that you'll encounter when using them. Highlights
Who Should Attend Researchers, scientists, and students interested in advancing the pace of their research by using parallel computing strategies. |
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Overview Medical images are obtained from various sources including MRI, CT, X-ray, ultrasound, and PET scans. Analyzing these images necessitates a comprehensive environment for data access, visualization, processing, and algorithm development. A key challenge involves extracting quantitative features to generate clinically relevant information using advanced techniques like machine learning algorithms. This presentation will explore radiomics, which captures characteristics not typically visible. Radiomics features can be employed across various medical imaging modalities and applications, enabling the study of associations between imaging features and patient biology, as well as the prediction of clinical outcomes, making radiomics a versatile technique in medical imaging. These features quantify shape, intensity, and texture characteristics within medical images, reducing reliance on subjective interpretation for clinical workflows. Highlights In this session, you will learn how to:
Class Materials |
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