The challenge that motivates the ANA group is to foster a healthy future for the Internet. The interplay of private sector investment, public sector regulation and public interest advocacy, as well as the global diversity in drivers and aspirations, makes for an uncertain future.
This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.
We develop techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior.
We build new protocols and architectures to improve the robustness and performance of computer networks. We develop practical solutions in wireless networks, network security, traffic engineering, congestion control, and routing.
The Systems CoR is focused on building and investigating large-scale software systems that power modern computers, phones, data centers, and networks, including operating systems, the Internet, wireless networks, databases, and other software infrastructure.
Led by Web inventor and Director, Tim Berners-Lee and CEO Jeff Jaffe, the W3C focus is on leading the World Wide Web to its full potential by developing standards, protocols and guidelines that ensure the long-term growth of the Web
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
Our goal is to build a system that predicts where people are looking in images. Given an image and the location of a head, our approach follows the gaze of the person and identifies the object being looked at.
The confluence of medicine and artificial intelligence stands to create truly high-performance, specialized care for patients, with enhanced precision diagnosis and personalized disease management. But to supercharge these systems we need massive amounts of personal health data, coupled with a delicate balance of privacy, transparency, and trust.
MIT’s Amar Gupta and his wife Poonam were on a trip to Los Angeles in 2016 when she fell and broke both wrists. She was whisked by ambulance to a reputable hospital. But staff informed the couple that they couldn’t treat her there, nor could they find another local hospital that would do so. In the end, the couple was forced to take the hospital’s stunning advice: return to Boston for treatment.
Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions.
A webpage today is often the sum of many different components. A user’s home page on a social-networking site, for instance, might display the latest posts from the users’ friends; the associated images, links, and comments; notifications of pending messages and comments on the user’s own posts; a list of events; a list of topics currently driving online discussions; a list of games, some of which are flagged to indicate that it’s the user’s turn; and of course the all-important ads, which the site depends on for revenues.
As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. Facial expressions aren’t always reliable: a smile can conceal frustration, while a poker face might mask a winning hand.But what if technology could tell us how someone is really feeling?Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.
When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.This month CSAIL researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.
CSAIL researcher Dina Katabi has been selected for the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT.
In his announcement, EECS Department Head Anantha Chandraksan said that Katabi 'is an ideal candidate for this professorship, given her outstanding technical contributions and leadership in wired and wireless networks.'
MIT CSAIL Principal Investigators Dina Katabi and Piotr Indyk have developed a new algorithm that improves on the fast Fourier transform (FFT), a fundamental concept in the information sciences that provides a method for representing irregular signals, compressing image and audio files, and solving differential equations and stock options.