Background

Global Internet routing information is available in public collections such as routing tables and relationship datasets. Network studies of the Internet are often inspired by discoveries of interesting statistics within these data.

Research Goals

We will use public datasets of Internet topology and Internet censorship, along with a statistical inference model, to learn where censorship happens on the Internet. This project will require data set analysis, data visualization, network and graph algorithms, and the use of Bayesian inference. The work will include tutorials explaining the process used to infer the location of censorship on networks followed by the creation of maps of various censorship types.

Skills Needed

Python programming, interest in networks, data science basics with numpy, pandas, etc.

Skills Gained

Processing scientific data sets, using networks as data structures for science, Bayesian statistical inference, and insight into Internet censorship research. Optionally: High-performance computing.