machine learning

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.

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.

Background Hello, I’m Lihaowen (Jayce) Zhu, a 2024 SoR contributor for the FEP-bench project, under the mentorship of Yuyang (Roy) Huang. Before we started, let’s recap the goal of our project and our progress until mid term.

Introduction Hello! I hope you’re enjoying the summer as much as I am. I’m excited to join the SOR community as a 2024 contributor. My name is Xikang Song, and I’m thrilled to collaborate with mentors Ruidan Li and Kexin Pei on the FSA-Benchmark project.

Introduction Hello, I’m Peiran Qin, a CS student at the University of Chicago. This summer I worked on the project FetchPipe: Data Science Pipeline for ML-based Prefetching under the mentorship of Prof.

Introduction Hello, I’m Peiran Qin, a CS student at the University of Chicago, currently working on the project FetchPipe: Data Science Pipeline for ML-based Prefetching under the mentorship of Prof. Haryadi S.