In the Media

System fixes bugs by importing functionality from other programs — without access to source code.

At the recent International Conference on Robotics and Automation, MIT researchers led by Daniela Rus, the Andrew and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science, presented a printable origami robot that folds itself up from a flat sheet of plastic when heated and measures about a centimeter from front to back. Read more.

The MIT DARPA Robotics Challenge Team led by Professor Rus Tedrake reached new heights in the June 5-6 international DARPA Robotics Challenge in Pomona California, as they nimbly programmed their Atlas robot to perform a wide range of tasks in one hour. The goal of the event was to develop mobile robots to perform useful tasks in disaster-relief situations — in response to the 2011 Fukushima nuclear disaster. Read more.

A computer vision enabled technology developed by a team of EECS faculty Bill Freeman and Frédo Durand and their students is enabling a new way to identify structural defects in objects. The group will report this latest work at the Conference on Computer Vision and Pattern Recognition in June. Read more.

Members of the Computer Science and Artificial Intelligence Lab's (CSAIL's) Computer Graphics Group including Professors Frédo Durand and William Freeman and EECS graduate student YiChang Shih have developed an algorithm that removes reflections from photos taken through windows. Read more.

Is it still an either-or choice to receive (or not) all those mailing list emails? EECS graduate student Amy Zhang working with EECS Prof. David Karger in the Computer Science and Artificial Intelligence Lab's Haystack Group, has developed a new system that uses techniques from social media to give the recipient more control over his/her inbox. Read more.

Students and graduates of Prof. Rob Miller's group, the User Interface Design Group have designed a system for visualizing and exploring thousands of solutions to a programming problem, ultimately enhancing online teaching and learning. Members of the group including first author and EECS graduate student Elena Glassman will present their work in April at the Association for Computing Machinery's Conference on Human Factors in Computing Systems. Read more.

Here’s one way to get kids excited about programming: a "robot garden" with dozens of fast-changing LED lights and more than 100 origami robots that can crawl, swim, and blossom like flowers. Read more.

Mapping the human genome, accomplished a decade ago, was heralded for laying the foundation for understanding genetic variation and links to a wide range of diseases. But genes can be switched on and off by many chemical modifications, aka "epigenetic marks." Now Manolis Kellis, EECS professor and member of the Computer Science and Artificial Intelligence Lab and the Broad Institute has led an NIH group that has created a similar map of the human epigenome. This work will lead to a global map towards understanding fundamental developmental and disease processes in humans. Read more.

In a paper appearing in a forthcoming issue of the International Journal of Robotics Research, Professors and member of the Learning and Intelligent Systems Group Leslie Kaelbling and Tomas Lozano Perez and EECS graduate student Lawson Wong show that a system using an off-the-shelf algorithm to aggregate different perspectives can recognize four times as many objects as one that uses a single perspective, while reducing the number of misidentifications. Read more.

Vivienne Sze, core member of the Microsystems Technology Laboratories (MTL), principal investigator in the Research Laboratory of Electronics (RLE) and the Emanuel E. Landsman (1958) Career Development Professor in the Electrical Engineering and Computer Science Department (EECS), has received a 2014 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (YFA). Read more.

Using Bayesian regression, Devavrat Shah, member of the Laboratory for Information and Decision Systems and the Computer Science and Artificial Intelligence Lab (LIDS) and recent graduate student Kang Zhang have identified patterns from five months of price data from all major Bitcoin exhanges — enabling them to predict the price of Bitcoin — thereby allowing them to double their investment over a 50 day period. Read more.