Research

We live in a world of wireless signals flowing around us and bouncing off our bodies. MIT researchers are now leveraging those signal reflections to provide scientists and caregivers with valuable insights into people’s behavior and health.

Constantinos (“Costis”) Daskalakis, an MIT professor and CSAIL principal investigator, has won the 2018 ACM Grace Murray Hopper Award. Daskalakis was honored for “proving that the computational complexity of finding Nash equilibria is the same as that of finding Brouwer fixed points, a proof since extended to several other equilibrium notions.”

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future. They trained their model on mammograms and known outcomes from over 60,000 patients treated at MGH, and their model learned the subtle patterns in breast tissue that are precursors to malignancy.

Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have shown that neural networks contain subnetworks that are up to one-tenth the size yet capable of being trained to make equally accurate predictions — and sometimes can learn to do so even faster than the originals.