Our vision is data-driven machine learning systems that advance the quality of healthcare, the understanding of cyber arms races and the delivery of online education.
Our projects are centered around the problems of navigation and mapping for autonomous mobile robots operating in underwater and terrestrial environments.
We develop techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior.
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.This month CSAIL researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.
