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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.

Info

Please note the location for your selected class. We are offering both in-person and virtual classes this year.

Important note: for virtual classes, you should receive an confirmation email from the Harvard Training Portal with a Zoom link. If you do not have your email address on file in the Training Portal, such messages cannot be sent to you. Please contact us for the Zoom meeting details if you do not get a confirmation email.

Wednesday, March 6Wednesday, March 13Wednesday, April 3Wednesday, April 10Wednesday, April 24Wednesday, May 15

Class

Date

Time

Location

Training Materials

Registration

Intro to O2MATLAB

WednesdayThursday, February 21September 19, 2024

10am to 12pm

In-persononline

User Training github

Register here!

O2 Portal - Simplifying the Interaction and Experience of Using an HPC Environment

Intro to Parallel Computing

Thursday, September 26, 2024

10am to 12pm

Virtualonline

User Training github

Register here!

Intro to MATLAB

Optimizing O2 Jobs

Thursday, October 3, 2024

10am to 12pm

In-in person

User Training github

Register here!

Intro to Parallel ComputingPython

WednesdayThursday, March 20October 10, 2024

10am to 12pm

In-persononline

User Training github

Register here!

Optimizing O2 Jobs

Class in collaboration with MathWorks, subject TBA

Thursday, October 17, 2024

10am to 12pm

In-in person

User Training github

Register here!

Intro to Python

Coming soon!

Data Management: Computing Strategies and Resources

(in collaboration with Countway Library and FAS Research Computing)

Thursday, October 24, 2024

10am to 12pm

In-person

User Training github11am

online

Register here!

Troubleshooting O2 Jobs

O2 Portal - Simplifying the Interaction and Experience of Using an HPC Environment

Thursday, October 31, 2024

10am to 12pm

Virtualonline

User Training github

Register here!

Intro to O2Python

WednesdayThursday, May 1November 14, 2024

10am to 12pm

Virtualonline

User Training github

Register here!

Medical Image Processing and Machine Learning Workflow

(Taught by Mathworks)

Intro to O2

Thursday, November 21, 2024

10am to 12pm

In-in person

User Training github

Register here!

RCBio: easy and quick HPC pipeline builder & runner

WednesdayThursday, May 22December 5, 2024

10am to 12pm

Virtualonline

User Training github

Register here!

HBC Current Topics in Bioinformatics: Shell Tips and Tricks on O2

Wednesday, May 22, 2024

1pm to 4pm

Virtual

Register here!(Sponsored by HBC in collaboration with RC)

Additional classes will be added for the Summer Spring semester.

Class Registration Process

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Expand
titleMedical Image Processing and Machine Learning Workflow

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:

  • Import and manage large sets of images without loading them into memory,

  • Extract shape, intensity, and texture radiomics features,

  • Reduce the number of extracted features for classification,  

  • Build a deep learning network to classify images, and

  • Review and analyze the results.

Class Materials

Link to class materials

Expand
titleBioGrids Academic Software Platform: No Installation Required

Academic Software Platform (ASP) integrates two major stacks of scientific software: BioGrids stack of ~400 biomedical applications (https://biogrids.org/software/) as well as SBGrid stack of ~500 structural biology applications. Multiple versions of applications are maintained, and users can also easily install the same stack of software on laptops, research workstations or cloud resources, under Linux or Mac OS X operating systems. More information about this resource is available on O2 wiki: https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1630994700/Using+Software+Provided+by+BioGrids

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