This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.
Our main goal is developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.
This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.
We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.
MIT App Inventor is an intuitive, visual programming environment that allows everyone – even children – to build fully functional apps for smartphones and tablets.
We focus on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing educational methods.
The Weiss Lab seeks to create integrated biological systems capable of autonomously performing useful tasks, and to elucidate the design principles underlying complex phenotypes.
Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.
By crunching 130 million mouse-clicks, two CSAIL researchers have developed a machine-learning model that can predict with surprising accuracy whether or not a MOOC student will drop out of a given course.