big data

Cynthia Rudin wants machine learning models, responsible for increasingly important decisions, to show their work.

Arvind Narayanan uses quantitative methods to expose and correct the misuse of quantitative methods.

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

Rediet Abebe uses the tools of theoretical computer science to understand pressing social problems — and try to fix them.

Jelani Nelson designs clever algorithms that only have to remember slivers of massive data sets. He also teaches kids in Ethiopia how to code.

Mario Jurić is leading the push to get astronomy ready for the torrents of data that are about to flow.

Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.

To efficiently analyze a firehose of data, scientists first have to break big numbers into bits.

The computational immunologist Purvesh Khatri embraces messy data as a way to capture the messiness of disease. As a result, he’s making elusive genomic discoveries.