Cybersecurity@CSAIL: combatting cyber-crime

Without security, almost no other computing innovation will be possible. Attacks from criminals, terrorists and rogue nation states threaten our infrastructure and our personal data. Until now, we’ve relied on “patch and pray”, but it’s time to do better. CSAIL researchers are making advances in cryptography, software design and analysis, and specialized hardware, that will lay for the foundation for a more secure future.
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Systems That Learn

A new MIT research initiative to accelerate the development, deployment, and evolution of large-scale software systems that incorporate machine learning and artificial intelligence.

The goal of STL is to accelerate the development of systems and applications that learn.  We intend to accomplish this goal through combining our expertise in systems and machine learning to create new applications for understanding complex relationships from the avalanche of data available today. STL has been created to enable cross-collaboration and accelerate development of innovative human-like systems to serve the world.
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A huge part of our computing future will be wireless and mobile.

To realize that future, CSAIL researchers are developing novel solutions to four challenges: dramatic improvements in spectrum use, systems to develop and run distributed mobile applications at scale, security and privacy in the mobile world, and energy-efficient designs.

These efforts are part of the MIT Center for Wireless Networks and Mobile Computing, housed at CSAIL, whose participants also include researchers from LIDS, MTL, and RLE.
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