Advanced Research Computing

Support for researchers seeking help with statistical modeling, machine learning, data mining, data visualization, and software engineering

Data Science

The team of data scientists at CCV provide support for Brown researchers who would like help with statistical modeling, machine learning, and writing scientific software. The breath of methods with which we have expertise is broad—spanning anything from frequentist or Bayesian statistical inference to modern machine learning methods such as deep learning and reinforcement learning.

Software Engineering

CCV’s research software engineers work with researchers to develop scientific software that is essential to conduct their research. This includes, for example, the development of tools and libraries for working with data, building workflows and infrastructure, and web application development. Our team of research software engineers also work with researchers to help take existing software and optimize it for performance and efficiency.

Expertise, Reproducible Research, and Collaborations

We have expertise in C, C++, CUDA, Fortran, JavaScript, Julia, Matlab, OpenCL, Python, R, and SQL. To achieve best practices in reproducible research, we use state-of-the-art technologies like Docker, Git and GitHub, Travis CI, Conda, among others.

Our data scientists and research software engineers can work with researchers on a short-term basis or on a more long-term basis—and anywhere in between. We have existing long-term collaborations with several groups on campus, including the Computational Biology Core, part of the Brown’s COBRE Center for Computational Biology of Human Disease, and several of Brown’s administrative offices.

We also have long-running collaborations with researchers in Neuroscience, Sociology, Engineering, Epidemiology, and the Department of Earth, Environment, and Planetary Sciences.

Our Teams


In addition to directly working with researchers, our group also works to to advance best practices in software engineering and promote robust and reproducible research across the university. To that end, we frequently hold workshops that aim to disseminate the current state-of-the-art tools and best practices for data analysis that is both sound and reproducible.

Check out our code, containers, and applications!

GitHub , Docker

Computational Biology Core Tools