SummerofReproducibility23

Hello Again! I’m excited to present my final blog post summarizing the progress and achievements made over the 2023 Summer of Reproducibility Fellowship.I will be sharing the work I’ve created for the Teaching Computer Networks with Reproducible Research: Developing a ‘classroom competition’ for adaptive video delivery.

Welcome back! In my final blog post for the 2023 Summer of Reproducibility Fellowship, I’ll be sharing my experiences and the materials I’ve created for the Using Reproducibility in Machine Learning Education project.

Hello again! In this blog post, I will be discussing the second material I created for the 2023 Summer of Reproducibility Fellowship. As you may recall from my first post, I am working on the Using Reproducibility in Machine Learning Education project with Fraida Fund as my mentor.

Hey, I am Shekhar and I am one of several students who are working on developing materials for reproducibility in machine learning education, under the mentorship of Fraida Fund. My Proposal aims to develop interactive educational materials about reproducibility in machine learning, for use in graduate and undergraduate classes.

Hello again, I am Mohamed Saeed and this is my second blog post for the 2023 Summer of Reproducibility Fellowship. As you may recall from my previous post, I am working on the Using Reproducibility in Machine Learning Education project with Fraida Fund as my mentor.

Introduction As part of the Scalebugs Project, we have worked on building a dataset of reproducible scalability bugs. To achieve this, we go through existing bug reports for popular distributed systems, which include Cassandra, HDFS, Ignite, and Kafka.

Hello! I am Srishti Jaiswal and this is my second blog post for the 2023 Summer of Reproducibility Fellowship. Introduction As I reach the halfway mark of my internship journey, I have had the incredible opportunity to work on a project that revolves around reproducing an adaptive video research result using cloud-based experimentation.

We are currently midway into the OSRE 2023 program and the following post lists the progress that I have made on the project so far. As part of the Reproducible Analysis & Models for Predicting Genomics Workflow Execution Time our overall goal was to enumerate the effect of sequence data quality on execution times.

Hello there! I’m Jonathan Edwin, all the way from the beautiful archipelago of Indonesia. This year, I got the exciting chance to jump on board the 2023 Summer of Reproducibility initiative.

As part of the Reproducible Analysis & Models for Predicting Genomics Workflow Execution Time my proposal under the mentorship of In Kee Kim, Martin Putra and collaborator Charis Christopher Hulu (another OSRE fellow) aims to analyze large-scale sequencing datasets in order to gain insights on how ‘input quality’ affects genomic workflows’ execution times.