Antonio Torralba

Professor of Electrical Engineering and Computer Science, Head of the Faculty of AI and Decision-making

Antonio Torralba is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT and an investigator at the Computer Science and Artificial Intelligence Laboratory. He also heads the faculty of artificial intelligence and decision-making in the MIT Schwarzman College of Computing. Previously, he led the MIT Quest for Intelligence as its inaugural director, and was the MIT director of the MIT–IBM Watson AI Lab. Torralba researches computer vision, machine learning, and human visual perception, with an interest in building systems that can perceive the world the way humans do. He has received an NSF Career award, the International Association for Pattern Recognition’s JK Aggarwal Prize, a Frank Quick Faculty Research Innovation Fellowship and a Louis D. Smullin (’39) Award for Teaching Excellence. Torralba earned a BS from Telecom BCN, Spain, and a PhD from the Institut National Polytechnique de Grenoble, France.

Publications

Media

Top Work

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Generative Models

Publications with the MIT-IBM Watson AI Lab

CLEVRER: The first video dataset for neuro-symbolic reasoning
CLEVRER: The first video dataset for neuro-symbolic reasoning
 
Deep Audio Priors Emerge From Harmonic Convolutional Networks
Deep Audio Priors Emerge From Harmonic Convolutional Networks
 
Self-supervised Moving Vehicle Tracking with Stereo Sound
Self-supervised Moving Vehicle Tracking with Stereo Sound
 
The sound of motions
The sound of motions
 
Seeing What a GAN Cannot Generate
Seeing What a GAN Cannot Generate
 
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
 
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding