reproducibility

Namaste🙏🏻! I’m Adi Akhilesh Singh, and I’m excited to share progress updates on the Reproducibility Checklists project by StatWrap, under the mentorship of Luke Rasmussen. Project Overview The project aims to integrate customizable reproducibility checklists into StatWrap, using metadata and user input to automate their generation.

Introduction As my project with the Summer of Reproducibility (SoR) 2024 comes to a close, I’d like to reflect on the journey and the outcomes achieved. My project focused on enhancing the reproducibility and portability of network experiments by integrating the RO-Crate standard into the TUM intern testbed pos (plain orchestrating service), and deploying this testbed on the Chameleon cloud infrastructure.

Hello again! This is my final blog post, and I will be discussing the second material I created for the 2024 Summer of Reproducibility Fellowship. As you may recall from my first post, I am working on the Exploring Data Leakage in Applied ML: Reproducing Examples of Irreproducibility project with Fraida Fund and Mohamed Saeed as my mentors.

Hi everyone, I’m excited to wrap up the AutoAppendix project with our final findings and insights. Over the course of this initiative, we’ve worked to assess the reproducibility of artifacts submitted to the SC24 conference and create guidelines that aim to improve the standard for reproducible experiments in the future.

Reproducing and validating fixes for throttling bugs in HDFS improved system stability and performance.

Hello! I’m Joanna! I have been contributing to the ML in Detecting and Addressing System Drift project under the mentorship of Ray Andrew Sinurat and Sandeep Madireddy. My project aims to design a pipeline to evaluate drift detection algorithms on system traces.

Hello everyone! I’m Triveni, a Master’s student in Computer Science at Northern Illinois University (NIU). I’m excited to share my progress on the OSRE 2024 project Categorize Differences in Reproduced Visualizations focusing on data visualization reproducibility.

Background Hello there! I’m William and this is my final blog for my proposal “Developing A Comprehensive Pipeline to Benchmark Drift Management Approaches” under the mentorship of Ray Andrew Sinurat and Sandeep Madireddy under the LAST project.

Hello! In this blog post, I will explore a common issue in machine learning called data leakage, using an example from the paper: Benedetti, P., Perri, D., Simonetti, M., Gervasi, O.

The project aims to develop a service that facilitates the automated replication of COMPSs experiments within the Chameleon infrastructure