We devise new mathematical tools to tackle the increasing difficulty and importance of problems we pose to computers.
The amount of data computers are expected to operate on has increased by several orders of magnitude. We’re asking computers to perform more and more intricate analyses on data, requiring them to answer many diverse questions, like calculating the 3-dimensional shape of a protein made up of many thousands of atoms, finding the most relevant web page to a query out of a pool of billions, or figuring out how best to allocate scarce resources among thousands of entities given only error-prone probabilistic information about the consequences of your decision. With that in mind, we’re at the forefront of scaling up optimization, network algorithms, computational geometry, distributed computing, algorithms for massive data sets, parallel computing, computational biology, and scientific computing.
We are trying to develop theoretical tools that can be used to analyze the convergence of iterative algorithms used for non-convex optimization problems.
The American Academy of Arts & Sciences recently elected MIT EECS professor, CSAIL member, and co-director of Foundations of Data Science Institute (FODSI) Piotr Indyk as one of their members.
In a 1999 paper, Erik Demaine — now a CSAIL principal investigator, but then an 18-year-old PhD student at the University of Waterloo, in Canada — described an algorithm that could determine how to fold a piece of paper into any conceivable 3-D shape. It was a milestone paper in the field of computational origami, but the algorithm didn’t yield very practical folding patterns. Essentially, it took a very long strip of paper and wound it into the desired shape. The resulting structures tended to have lots of seams where the strip doubled back on itself, so they weren’t very sturdy.
