Machine Learning

The Machine Learning Group works on a variety of topics spanning theoretical foundations, algorithms, and applications. The group studies a range of research areas related to machine learning and their applications for robotics, health care, language processing, information retrieval and more. Among these subjects include precision medicine, motion planning, computer vision, Bayesian inference, graphical models, statistical inference and estimation. Our work is interdisciplinary and deeply rooted in systems and computer science theory.

Many of the researchers have affiliations with other groups at MIT, including the Institute for Medical Engineering & Science (IMES) and the Institute for Data, Systems and Society (IDSS).

Modern use of data relies heavily on predictive modeling. Machine learning methods are needed to distill large, heterogeneous, and fragmented data sources into useful pieces of information such as answers to search queries, purchasing patterns of customers, or likely actions of mobile users. This research focuses on predicting the behavior of mobile users -- actions they are likely to take in any particular context -- based on a collection of intermittent sensors such as GPS, wifi, accelerometer, and others. The goal is to develop methods that will be useful more broadly. Work addresses the following key problems: (1) scaling to realistic problem sizes, (2) robustness, and (3) maintaining privacy even as data are used collaboratively.