Registrations are now managed through the Harvard Training Portal, which requires a Harvard ID (HUID). Those members of the extensive HMS community who do not currently have HUIDs - such as employees at affiliate hospitals, or collaborators from other institutions - may self-register for one as a "Person of Interest" with their faculty member's sponsorship. This form may take several days to process, and is available here:
Medical Image Processing and Deep Learning with MATLAB
MATLAB helps you gain insight into your image and video data, develop algorithms, and automate image analysis tasks. With just a few lines of MATLAB code, you can apply deep learning techniques and build deep learning models without having to be an expert.
Join us for a complimentary 3-part webinar series and learn how to:
Use new apps and features to simplify image data exploration, processing, visualization, and algorithm development
Apply advanced computer vision techniques in biomedical imaging for object detection, tracking, and feature extraction
Preprocess and label datasets faster: automate ground-truth labeling of image, video, and audio data with our Deep Network Designer app
Access the latest models from TensorFlow, Keras, Caffe, PyTorch, and other open-source frameworks using ONNX
Accelerate your algorithms on NVIDIA® GPUs, clusters, and cloud resources
Session 1: Medical Image Processing with MATLAB (May 13, 2021)
Session 2: Pixels to Models: Taking Medical Imaging to the Next Level (May 20, 2021)
Session 3: Deep Learning in Medical Imaging (May 27, 2021)
The HMS Research Computing R Biostatistics course is a three-class, advanced instructional course covering the basics of RNA-seq analysis with Bioconductor and the R statistical programming language. Bioconductor provides tools for the analysis and comprehension of all types of high-throughput genomic data. Students should have a beginner's level of proficiency in R programming and an understanding of basic statistical principles before registering for the course.
The course covers standard supervised statistical approaches for the comprehensive analysis of a published Cancer Genome Atlas (TCGA) human breast cancer RNA-seq dataset. Topics include edgeR differential gene expression analysis and GOSeq functional enrichment analysis of gene ontology terms. Data visualization techniques are emphasized, and each two-hour class includes a lecture and R practicum. The third two-hour class includes a lecture on deep learning in the biomedical sciences. Course registration includes all 3 classes.
Comprehensively commented R scripts are provided to the student (R Biostatistics Files), as the objective of the course is to learn common biostatistical methods used for RNA-Seq analysis. Students are strongly encouraged to use personal laptops for the course.
To be prepared for the classes, students are encouraged to download and install the latest versions of R and RStudio prior to the first class.
Please join us for the following spring series of live webinars. We hope you find the information helpful for both teaching and research activities. The series is open to all faculty, researchers and students. We look forward to your attendance.
Important Notice: After registration, 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 be sent to you. Please contact us for the Zoom meeting details if you did not get a confirmation email.
O2 for New Users addresses the needs of users who have very little linux experience, and are just getting started with HPC. More time will be devoted to covering linux basics, and the concepts of schedulers and jobs, and data management best practices. The lecture portion of this class is one hour, the second hour will be spent clinic-style with HMS RC staff to address workflow-specific questions and help convert commands to O2 SLURM syntax.
Intro to Python
Python is a popular scripting language for scientific computing and available across all computer platforms. The course will introduce you to some of the basics of the Python language as well as some of the nuances involved with its use specific to the O2 environment. The goal is to provide users with a foundational level of familiarity. Topics covered include basic data types and declaration, flow control (if/else), loops, a brief introduction to constructing a script, and a briefer introduction to modules. The course will be taught on O2, but general concepts are easily translatable to desktop and local installations.
Intro to R/Bioconductor
Intro to using R and Bioconductor. R is a powerful, open-source, highly adaptable statistical language useful for crunching numbers to datasets like those produced by next-gen sequencing. This class covers R basics and learning to think like/understand R. Users will learn how to set up personal R libraries on O2, and use O2 R for its high memory allocations and parallelization. Topics include how to install packages, learn about variables, data types. data manipulation, flow control, and functions, perform simple statistical tests, and create a variety of plots. Laptops are encouraged.
Matlab has become the “language of science” in the past few decades. It is simple to use, yet powerful enough to be productive on large computing infrastructures. If you need: 1) Fast prototyping of research ideas; or 2) avoid spending too much time in coding instead of doing real science by taking advantage of Matlab’s built-in functions; 3) User friendly graphical interface and educational documentation; 4) Simplicity of code; 5) Easy access to GPU computing power; 6) Easy plotting and presentation of data; you will find this introduction course useful. This course will introduce the basics of the MATLAB coding language with O2-scalability and data presentation.
Intro to Parallel Computing
This is a short introduction to Parallel Computing that will include an overview of the basic concepts of parallel programming: from running your job in an embarrassingly parallel way to writing simple shared and distributed memory parallelization codes in different languages. The seminar will cover several examples of actual parallel codes however it will not have any "hands on" components. A basic programming experience (of any language, no parallelization) is preferred in order to better follow the topics presented during the seminar.
Intermediate O2 is for current O2 users who would like to brush up on their bash skills, learn more advanced file transfer techniques, and unleash some of the powerful features of the SLURM scheduler.
Intro to Git and GitHub
This course introduces Git and GitHub and covers topics including: Getting Started with Git for version control, Using GitHub Desktop effectively, Collaborating with others on GitHub, and Utilizing GitHub Flow for better workflow. No previous exposure is assumed. We hope attendees will leave the class with the knowledge and tools necessary to start integrating Git into their workflows and excited to begin collaborating on GitHub.
Introduction to Globus Data Transfer and Data Collaboration on O2
Additional computational trainings are available through other groups: