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Getting Started - Computational Projects

Git / Github

All computational projects in the lab are stored and coordinated through the version tracking program git and the website Github. Dr. Meyer can invite you to become a member of the organization account as soon as you have made a Github account.


The lab primarily uses Python for its computational analysis. This language is one of a few that offer the right breadth of capabilities for probabilistic and scientific programming. The syntax is likely familiar if you have a background in Matlab or C++. Software Carpentry offers two excellent tutorials for introducing yourself to the language:

On top of Python, we commonly use the numpy, scipy, pandas, scikit-learn, matplotlib, seaborn, and TensorLy packages.


We are increasingly using Julia for new projects in the lab. The language provides a unique breadth of tools necessary for our work, Matlab-like syntax, and valuable benefits in language expressiveness.

On top of Julia, we commonly use DifferentialEquations.jl, TensorDecompositions.jl, Turing.jl, and Plots.jl.


The lab has a high-performance server that can be used for programming and running programs. The most convenient interface for using this server is JupyterLab (https://aretha.seas.ucla.edu:8000/hub/). Dr. Meyer is able to create accounts on this server for you.


Most projects in the lab roughly follow continuous integration principles. That is, individuals make desired changes to the project’s code on their own branches, then regularly merge their changes with the whole group’s version. We use an automated build server to check our code for stylistic rules, correctness, and build the project’s outputs (papers, programs, etc). This server (https://transduc.seas.ucla.edu) uses Jenkins to automate these tasks. You should have access to the server once your Github account is linked to the lab’s organization.