Computer Science and Artificial Intelligence Laboratory (CSAIL)

  • How much does your smartphone know about you — even when it's turned off? Under the guidance of CSAIL Principal Investigator Hal Abelson, the Class of 1922 Professor in the Department of Electrical Engineering and Computer Science, CSAIL graduate students Fuming Shih and Frances Zhang are investigating how much certain smartphone applications know about users.
  • David Gifford, EECS professor and director of the Computational Genomics Group in the Computer Science and Artificial Intelligence Lab (CSAIL), working with members of his group, has developed a new algorithm for analyzing millions of experimentally identified DNA fragments and allowing the inference -- with 55% accuracy in the most difficult cases -- of the precise locations at which transcription factors bind to them. Read more!
  • This fall, the faculty and students in the Electrical Engineering and Computer Science (EECS) Department at MIT are coming together for a new program that has created a buzz since its announcement last spring. The Advanced Undergraduate Research Program — now officially called the SuperUROP — for EECS department juniors and seniors has already enticed over 200 students with more than 100 exciting research projects proposed by the department's faculty. Read more!
  • Manolis Kellis, an associate professor of computer science at MIT and an associate member of the Broad Institute, is one of the lead computational scientists and authors of a paper that describes the functionality of the non-gene regions (about 80 percent) of the human genome, the so-called 'junk DNA'.
  • 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.


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