Data

With lots of data, a strong model and statistical thinking, scientists can make predictions about all sorts of complex phenomena. Today, this practice is evolving to harness the power of machine learning and massive datasets. In this episode, co-host Steven Strogatz speaks with statistician Emmanuel Candès about black boxes, uncertainty and the power of inductive reasoning.

Dijkstra’s algorithm was long thought to be the most efficient way to find a graph’s best routes. Researchers have now proved that it’s “universally optimal.”

Across the social and biological sciences, statisticians use a technique that leverages randomness to deal with the unknown.

Mathematicians are using topological abstractions to find places where it’s hard to vote.

Seventy years after the invention of a data structure called a hash table, theoreticians have found the most efficient possible configuration for it.

Alexei Efros has spent his career learning how machines see differently from humans. Now he’s helping to bridge the gap.

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.

Built upon the ubiquitous Fourier transform, the mathematical tools known as wavelets allow unprecedented analysis and understanding of continuous signals.