machine learning

Hi everyone! My name is Zeyu, and I will be working on a project for a retrieval-enhanced generative framework for spatial transcriptomics during Google Summer of Code 2025. My project is called RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics and is supervised by Ziheng Duan.

Hello, My name is Yiming Cheng. I am a Pre-doc researcher in Computer Science at University of Chicago. I’m excited to be working with the Summer of Reproducibility and the Chameleon Cloud community as a project leader.

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?

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

Overview The complexity of environment setup and the expertise required to configure specialized software stacks can often hinder efforts to reproduce important scientific achievements in HPC and systems studies. Researchers often struggle with incomplete or ambiguous artifact descriptions that make assumptions about “common knowledge” that is actually specific domain expertise.

Overview The complexity of environment setup and the expertise required to configure specialized software stacks can often hinder efforts to reproduce important scientific achievements in HPC and systems studies. Researchers often struggle with incomplete or ambiguous artifact descriptions that make assumptions about “common knowledge” that is actually specific domain expertise.

Topics: computational pathology, image generation, disentangled representations, latent space manipulation, deep learning Skills: Programming Languages: Proficient in Python, with experience in machine learning libraries such as PyTorch or TensorFlow. Generative Models: Familiarity with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and contrastive learning methods.

Topics: bioinformatics, spatial transcriptomics, gene expression generation, retrieval-augmented generation, large models Skills: Programming Languages: Proficient in Python, and familiarity with machine learning libraries such as PyTorch. Data Analysis: Experience with spatial transcriptomics datasets and statistical modeling.

Hello! My name is Syed Mohammad Qasim, a PhD candidate at the Department of Electrical and Computer Engineering, Boston University. This summer I worked on the project ML-Powered Problem Detection in Chameleon as part of the Summer of Reproducibility (SoR) program with the mentorship of Ayse Coskun and Michael Sherman.

Hello! I’m Qianru! I have been contributing to the BenchmarkST: Cross-Platform, Multi-Species Spatial Transcriptomics Gene Imputation Benchmarking project under the mentorship of Ziheng Duan. My project aims to provide a standardized, easily accessible evaluation framework for gene imputation in spatial transcriptomics.