reproducibility

Hi everyone, I’m Sam! I’m excited to be working with the Argonne National Laboratory and SoR this summer on Smart Environments. Have you ever encountered a great opensource project and wanted to run it or use it locally, only to find that it’s such a headache to set up all the dependencies?

Hello, My name is Zahra Temori. I am a rising senior in Computer Science at University of Delaware. I’m excited to be working with the Summer of Reproduciblity and the Chameleon Cloud community.

Creating a comprehensive framework to enable fair comparison of edge replication systems and coordination protocols through standardized benchmarking tools and realistic workload simulation.

This is now common practice and an efficient way to track activities within OSRE projects. Include links to proposals, presentations, reports, and experience.

Hi Everyone, I’m Rohan Babbar from Delhi, India. This summer, I’m excited to be working with the Argonne National Laboratory and the Chameleon Cloud community. My project focuses on developing an MPI Appliance to support reproducible High-Performance Computing (HPC) research on the Chameleon testbed.

Hello👋! I am Debangi Ghosh, currently pursuing a degree in Mathematics and Computing at IIT (BHU) Varanasi, India. This summer, I will be working on the StatWrap: Cross-Project Searching and Classification using Local Indexing project under the mentorship of Luke Rasmussen.

Topics: machine learning, MLOps, reproducibility Skills: Python, machine learning, GitOps, systems, Linux, data, Docker Difficulty: Hard Size: Large (350 hours) Mentors: Fraida Fund and Mohamed Saeed Project Idea Description

FairFace: Reproducible Bias Evaluation in Facial AI Models via Controlled Skin Tone Manipulation Bias in facial AI models remains a persistent issue, particularly concerning skin tone disparities. Many studies report that AI models perform differently on lighter vs.

Reproducibility in distributed AI training is a crucial challenge due to several sources of uncertainty, including stragglers, data variability, and inherent randomness. Stragglers—slower processing nodes in a distributed system—can introduce timing discrepancies that affect the synchronization of model updates, leading to inconsistent states across training runs.

Retrieval-Augmented Generation (RAG) frameworks, which merge the capabilities of retrieval systems and generative models, significantly enhance the relevance and accuracy of responses produced by large language models (LLMs). These frameworks retrieve relevant documents from a large corpus and use these documents to inform the generative process, thereby improving the contextuality and precision of the generated content.