IIT@MIT is the result of a collaborative agreement between the Istituto Italiano di Tecnologia and the Massachusetts Institute of Technology. Our aim is to advance the frontiers of learning theory and machine learning, while building algorithmic tools for the analysis of complex systems and high dimensional data. Our approach blends well established concepts and methods from computer science, statistics and signal processing with modern results in (high dimensional) probability, into new computational tools for statistical inference. Our goal is to channel theory and algorithms into new applications including smarter technologies and sophisticated engines for inference from high-dimensional data/signals. The ultimate objective of the lab is a future generation of intelligent technologies.

Robotics

Robotics is a natural testbed for machine learning solutions. The variety of sensory modalities robots are endowed with requires for the robot learning to adapt and interact with the environment and humans. We develop and apply cutting edge machine learning techniques to solve perception, cognition and control problems in humanoid robotics.

Vision

In recent years, statistical machine learning approaches had a huge impact in the solution of computer vision problems such as object classification. We develop novel algorithms for image representation that could reduce the need of labeled data and scalable machine learning solutions to classify large image and video datasets.

Speech

The dynamic and highly variable nature of speech signals provide a challenging setting for machine learning methods. Automatic speech recognition is still largely unsolved, despite recent advances through deep learning representations. The goal of this project is to derive invariant speech representations for recognition and classification.