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Laboratory for Information and Decision Systems (LIDS)

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In these time-lapse photos, a robot is guided by two different algorithms as it attempts to grasp a coffee cup on a desk. In the first (top), the robot flails about randomly before reaching toward the cup. But when it runs a new algorithm designed by MIT researchers (bottom), its movements are much more efficient and predictable.

Smarter robot arms

A combination of two algorithms developed at MIT allows autonomous robots to execute tasks much more efficiently — and move more predictably.

How wise are crowds?

By melding economics and engineering, researchers show that as social networks get larger, they usually get better at sorting fact from fiction.

MIT researchers are developing a theoretical framework that could eventually be used to help pinpoint the location of mobile devices — represented here as blue dots — indoors, where GPS reception can be unreliable and inaccurate.

Can you find me now?

By demonstrating fundamental limits on their accuracy, MIT researchers show how to improve wireless location-detection systems.

If the relationships between data can be thought of as lines connecting points — or “graphs” — then machine learning is a matter of inferring the lines from the points. MIT researchers have shown that graphs shaped like stars and chains establish, respectively, the worst- and best-case scenarios for computers doing pattern recognition.

Sizing samples

Many scientific disciplines use computers to infer patterns in data. But how much data is enough to ensure that the inferences are right?

Pablo Parrilo, the Finmeccanica Career Development Professor at MIT’s Laboratory for Information and Decision Systems

Nonlinear thinker

With techniques for translating complicated equations into ‘sums of squares,’ Pablo Parrilo helps make sense of previously insoluble problems.

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