Computer Science and Artificial Intelligence Laboratory (CSAIL)

  • We study several natural problems in which an {\em unknown} distribution over an {\em unknown} population of vectors needs to be recovered from partial or noisy samples. Such problems naturally arise in a variety of contexts in learning, clustering, statistics, data mining and database privacy, where loss and error may be introduced by nature, inaccurate measurements, or on purpose.
  • Applying random linear projections, a.k.a. "sketching", has become a standard technique when analyzing high-dimensional data sets. The resulting algorithms are embarrassingly parallelizable and suitable for stream processing.
  • In his seminal paper, Myerson [1981] provides a revenue-optimal auction for a seller who is looking to sell a single item to multiple bidders. Extending this auction to simultaneously selling multiple heterogeneous items has been one of the central problems in Mathematical Economics. We provide such an extension that is also computationally efficient.
  • We solve a 20-year old problem posed by Yannakakis and prove that there exists no polynomial-size linear program (LP) whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric.
  • We are developing a new system for large-scale data analysis -- called "Naiad" -- which has the goal of supporting complex iterative queries over dynamic inputs at interactive timescales.
  • Polina Golland, associate professor in the MIT EECS department and principal investigator in the Computer Science and Artificial Intelligence Lab (CSAIL), working with EECS graduate student Archana Venkataraman has developed an algorithm which can aid in deciphering what regions of the brain are involved in certain diseases ultimately enabling drug companies to develop more effective treatments for the disease that specifically target these regions.
  • Members of the MIT Database Group including Sam Madden, an associate professor in the Department of Electrical Engineering and Computer Science at MIT and co-leader of the 'bigdata@CSAIL' initiative, EECS graduate student Alvin Cheung, and researchers from Cornell University are presenting work this week at the 38th International Conference on Very Large Databases on Pyxis - a new system that automatically streamlines websites’ database access patterns, making the sites up to three times as fast while allowing the types of languages already favored by Web developers.


Subscribe to Computer Science and Artificial Intelligence Laboratory (CSAIL)