Garrett Parrish grew up singing and dancing as a theater kid, influenced by his older siblings, one of whom is an actor and the other a stage manager. But by the time he reached high school, Parrish had branched out significantly, drumming in his school’s jazz ensemble and helping to build a state-championship-winning robot.
MIT was the first place Parrish felt he was able to work meaningfully at the nexus of art and technology. “Being a part of the MIT culture, and having the resources that are available here, are what really what opened my mind to that intersection,” the MIT senior says. “That’s always been my goal from the beginning: to be as emotionally educated as I am technically educated.”
Parrish, who is majoring in mechanical engineering, has collaborated on a dizzying array of projects ranging from app-building, to assistant directing, to collaborating on a robotic opera. Driving his work is an interest in shaping technology to serve others.
“The whole goal of my life is to fix all the people problems. I sincerely think that the biggest problems we have are how we deal with
The age of big data has seen a host of new techniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.
That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.
An international team of computer scientists hopes to change that, with a new system called Data Civilizer, which automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.
“Modern organizations have many thousands of data sets spread across files, spreadsheets, databases, data lakes, and other software systems,” says Sam Madden, an MIT professor of electrical engineering and computer science and faculty director of MIT’s bigdata@CSAIL initiative. “Civilizer helps analysts in these organizations quickly find data
U.S. News and World Report has again placed MIT’s graduate program in engineering at the top of its annual rankings, continuing a trend that began in 1990, when the magazine first ranked such programs.
The MIT Sloan School of Management also placed highly; it shares with Stanford University the No. 4 spot for the best graduate business program.
This year, U.S. News also ranked graduate programs in the social sciences and humanities. The magazine awarded MIT’s graduate program in economics a No. 1 ranking, along with Harvard University, Princeton University, Stanford, the University of California at Berkeley, and Yale University.
Among individual engineering disciplines, MIT placed first in six areas: biomedical/bioengineering (tied with Johns Hopkins University — MIT’s first-ever No. 1 U.S. News ranking in this discipline); chemical engineering; computer engineering; electrical/electronic/communications engineering; materials engineering; and mechanical engineering (tied with Stanford). The Institute placed second in aerospace/aeronautical/astronautical engineering (tied with Georgia Tech) and nuclear engineering.
In the rankings of graduate programs in business, MIT Sloan moved up one step from its No. 5 spot last year. U.S. News awarded a No. 1 ranking to the school’s specialties in
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new computational model of a neural circuit in the brain, which could shed light on the biological role of inhibitory neurons — neurons that keep other neurons from firing.
The model describes a neural circuit consisting of an array of input neurons and an equivalent number of output neurons. The circuit performs what neuroscientists call a “winner-take-all” operation, in which signals from multiple input neurons induce a signal in just one output neuron.
Using the tools of theoretical computer science, the researchers prove that, within the context of their model, a certain configuration of inhibitory neurons provides the most efficient means of enacting a winner-take-all operation. Because the model makes empirical predictions about the behavior of inhibitory neurons in the brain, it offers a good example of the way in which computational analysis could aid neuroscience.
The researchers will present their results this week at the conference on Innovations in Theoretical Computer Science. Nancy Lynch, the NEC Professor of Software Science and Engineering at MIT, is the senior author on the paper. She’s joined by Merav Parter, a postdoc in her group, and Cameron Musco, an MIT graduate student in electrical
Today, loading a web page on a big website usually involves a database query — to retrieve the latest contributions to a discussion you’re participating in, a list of news stories related to the one you’re reading, links targeted to your geographic location, or the like.
But database queries are time consuming, so many websites store — or “cache” — the results of common queries on web servers for faster delivery.
If a site user changes a value in the database, however, the cache needs to be updated, too. The complex task of analyzing a website’s code to identify which operations necessitate updates to which cached values generally falls to the web programmer. Missing one such operation can result in an unusable site.
This week, at the Association for Computing Machinery’s Symposium on Principles of Programming Languages, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory presented a new system that automatically handles caching of database queries for web applications written in the web-programming language Ur/Web.
Although a website may be fielding many requests in parallel — sending different users different cached data, or even data cached on different servers — the system guarantees that, to the user, every transaction will look exactly as
The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices.
In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.
In a real-world application, that probably translates to a power savings of 90 to 99 percent, which could make voice control practical for relatively simple electronic devices. That includes power-constrained devices that have to harvest energy from their environments or go months between battery charges. Such devices form the technological backbone of what’s called the “internet of things,” or IoT, which refers to the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock will soon have sensors that report information directly to networked servers, aiding with maintenance and the coordination of tasks.
“Speech input will become a natural interface for many wearable applications and intelligent devices,” says Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science
Every other year, the International Conference on Automated Planning and Scheduling hosts a competition in which computer systems designed by conference participants try to find the best solution to a planning problem, such as scheduling flights or coordinating tasks for teams of autonomous satellites.
On all but the most straightforward problems, however, even the best planning algorithms still aren’t as effective as human beings with a particular aptitude for problem-solving — such as MIT students.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory are trying to improve automated planners by giving them the benefit of human intuition. By encoding the strategies of high-performing human planners in a machine-readable form, they were able to improve the performance of competition-winning planning algorithms by 10 to 15 percent on a challenging set of problems.
The researchers are presenting their results this week at the Association for the Advancement of Artificial Intelligence’s annual conference.
“In the lab, in other investigations, we’ve seen that for things like planning and scheduling and optimization, there’s usually a small set of people who are truly outstanding at it,” says Julie Shah, an assistant professor of aeronautics and astronautics at MIT. “Can we take the insights and the high-level strategies from the
Regina Barzilay is working with MIT students and medical doctors in an ambitious bid to revolutionize cancer care. She is relying on a tool largely unrecognized in the oncology world but deeply familiar to hers: machine learning.
Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, was diagnosed with breast cancer in 2014. She soon learned that good data about the disease is hard to find. “You are desperate for information — for data,” she says now. “Should I use this drug or that? Is that treatment best? What are the odds of recurrence? Without reliable empirical evidence, your treatment choices become your own best guesses.”
Across different areas of cancer care — be it diagnosis, treatment, or prevention — the data protocol is similar. Doctors start the process by mapping patient information into structured data by hand, and then run basic statistical analyses to identify correlations. The approach is primitive compared with what is possible in computer science today, Barzilay says.
These kinds of delays and lapses (which are not limited to cancer treatment), can really hamper scientific advances, Barzilay says. For example, 1.7 million people are diagnosed with cancer in the U.S. every year, but only about 3 percent enroll in
Like MIT’s campus computing environment, Athena, a pre-cloud solution for enabling files and applications to follow the user, Dropbox’s Drew Houston ’05 brings his alma mater everywhere he goes.
After earning his bachelor’s in electrical engineering and computer science, Houston’s frustration with the clunky need to carry portable USB drives drove him to partner with a fellow MIT student, Arash Ferdowsi, to develop an online solution — what would become Dropbox.
Dropbox, which now has over 500 million users, continues to adapt. The file-sharing company recently crossed the $1-billion threshold in annual subscription revenue. It’s expanding its business model by selling at the corporate level — employees at companies with Dropbox can use, essentially, one big box.
True to his company’s goal of using technology to bring people (and files) together, Houston is keen to share his own wisdom with others, especially those at MIT. Houston gave the 2013 Commencement address, saying “The hardest-working people don’t work hard because they’re disciplined. They work hard because working on an exciting problem is fun.”
He has also been a guest speaker in ‘The Founder’s Journey,” a course designed to demystify entrepreneurship, and at the MIT Enterprise Forum Cambridge; a frequent and active participant in StartMIT, a
MIT has been honored with 12 No. 1 subject rankings in the QS World University Rankings for 2017.
MIT received a No. 1 ranking in the following QS subject areas: Architecture/Built Environment; Linguistics; Computer Science and Information Systems; Chemical Engineering; Civil and Structural Engineering; Electrical and Electronic Engineering; Mechanical, Aeronautical and Manufacturing Engineering; Chemistry; Materials Science; Mathematics; Physics and Astronomy; and Economics.
Additional high-ranking MIT subjects include: Art and Design (No. 2), Biological Sciences (No. 2), Earth and Marine Sciences (No. 5), Environmental Sciences (No. 3), Accounting and Finance (No. 2), Business and Management Studies (No. 4), and Statistics and Operational Research (No. 2).
Quacquarelli Symonds Limited subject rankings, published annually, are designed to help prospective students find the leading schools in their field of interest. Rankings are based on research quality and accomplishments, academic reputation, and graduate employment.
MIT has been ranked as the No. 1 university in the world by QS World University Rankings for five straight years.
From Reddit to Quora, discussion forums can be equal parts informative and daunting. We’ve all fallen down rabbit holes of lengthy threads that are impossible to sift through. Comments can be redundant, off-topic or even inaccurate, but all that content is ultimately still there for us to try and untangle.
Sick of the clutter, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed “Wikum,” a system that helps users construct concise, expandable summaries that make it easier to navigate unruly discussions.
“Right now, every forum member has to go through the same mental labor of squeezing out key points from long threads,” says MIT Professor David Karger, who was senior author on a new paper about Wikum. “If every reader could contribute that mental labor back into the discussion, it would save that time and energy for every future reader, making the conversation more useful for everyone.”
The team tested Wikum against a Google document with tracked changes that aimed to mimic the collaborative editing structure of a wiki. They found that Wikum users completed reading much faster and recalled discussion points more accurately, and that editors made edits 40 percent faster.
Karger wrote the new paper with PhD students
Daniel Zuo came to MIT with a plan: He wanted to study algorithms and one day to become a research professor.
The senior has more than accomplished the former goal, conducting innovative research on algorithms to reduce network congestion, in the Networks and Mobile Systems group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). And, as he graduates this spring with a bachelor’s degree in computer science and electrical engineering and a master’s in engineering, he is well on his way to achieving the latter one.
But Zuo has also taken some productive detours from that roadmap, including minoring in creative writing and helping to launch MakeMIT, the nation’s largest “hardware hackathon.”
The next step in his journey will take him to Cambridge University, where he will continue his computer science research as a Marshall Scholar.
“The Marshall affords me the opportunity to keep exploring for a couple more years on an academic level, and to grow on a personal level, too,” Zuo says. While studying in the Advanced Computer Science program at the university’s Computer Laboratory, “I’ll be able to work with networks and systems to deepen my understanding and take more time to explore this field,” he says.
Algorithms to connect the
Markov decision processes are mathematical models used to determine the best courses of action when both current circumstances and future consequences are uncertain. They’ve had a huge range of applications — in natural-resource management, manufacturing, operations management, robot control, finance, epidemiology, scientific-experiment design, and tennis strategy, just to name a few.
But analyses involving Markov decision processes (MDPs) usually make some simplifying assumptions. In an MDP, a given decision doesn’t always yield a predictable result; it could yield a range of possible results. And each of those results has a different “value,” meaning the chance that it will lead, ultimately, to a desirable outcome.
Characterizing the value of given decision requires collection of empirical data, which can be prohibitively time consuming, so analysts usually just make educated guesses. That means, however, that the MDP analysis doesn’t guarantee the best decision in all cases.
In the Proceedings of the Conference on Neural Information Processing Systems, published last month, researchers from MIT and Duke University took a step toward putting MDP analysis on more secure footing. They show that, by adopting a simple trick long known in statistics but little applied in machine learning, it’s possible to accurately characterize the value of a given decision
Distributed planning, communication, and control algorithms for autonomous robots make up a major area of research in computer science. But in the literature on multirobot systems, security has gotten relatively short shrift.
In the latest issue of the journal Autonomous Robots, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams’ communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical.
“The robotics community has focused on making multirobot systems autonomous and increasingly more capable by developing the science of autonomy. In some sense we have not done enough about systems-level issues like cybersecurity and privacy,” says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper.
“But when we deploy multirobot systems in real applications, we expose them to all the issues that current computer systems are exposed to,” she adds. “If you take over a computer system, you can make it release private data — and you can do a lot of other bad things. A cybersecurity attack
Most website visits these days entail a database query — to look up airline flights, for example, or to find the fastest driving route between two addresses.
But online database queries can reveal a surprising amount of information about the people making them. And some travel sites have been known to jack up the prices on flights whose routes are drawing an unusually high volume of queries.
At the USENIX Symposium on Networked Systems Design and Implementation next week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Stanford University will present a new encryption system that disguises users’ database queries so that they reveal no private information.
The system is called Splinter because it splits a query up and distributes it across copies of the same database on multiple servers. The servers return results that make sense only when recombined according to a procedure that the user alone knows. As long as at least one of the servers can be trusted, it’s impossible for anyone other than the user to determine what query the servers executed.
“The canonical example behind this line of work was public patent databases,” says Frank Wang, an MIT graduate student in electrical engineering and computer science and
The transmission control protocol, or TCP, which manages traffic on the internet, was first proposed in 1974. Some version of TCP still regulates data transfer in most major data centers, the huge warehouses of servers maintained by popular websites.
That’s not because TCP is perfect or because computer scientists have had trouble coming up with possible alternatives; it’s because those alternatives are too hard to test. The routers in data center networks have their traffic management protocols hardwired into them. Testing a new protocol means replacing the existing network hardware with either reconfigurable chips, which are labor-intensive to program, or software-controlled routers, which are so slow that they render large-scale testing impractical.
At the Usenix Symposium on Networked Systems Design and Implementation later this month, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory will present a system for testing new traffic management protocols that requires no alteration to network hardware but still works at realistic speeds — 20 times as fast as networks of software-controlled routers.
The system maintains a compact, efficient computational model of a network running the new protocol, with virtual data packets that bounce around among virtual routers. On the basis of the model, it schedules transmissions on the
In a world where hackers can sabotage power plants and impact elections, there has never been a more crucial time to examine cybersecurity for critical infrastructure, most of which is privately owned.
According to MIT experts, over the last 25 years presidents from both parties have paid lip service to the topic while doing little about it, leading to a series of short-term fixes they liken to a losing game of “Whac-a-Mole.” This scattershot approach, they say, endangers national security.
In a new report based on a year of workshops with leaders from industry and government, the MIT team has made a series of recommendations for the Trump administration to develop a coherent cybersecurity plan that coordinates efforts across departments, encourages investment, and removes parts of key infrastructure like the electric grid from the internet.
Coming on the heels of a leak of the new administration’s proposed executive order on cybersecurity, the report also recommends changes in tax law and regulations to incentivize private companies to improve the security of their critical infrastructure. While the administration is focused on federal systems, the MIT team aimed to address what’s left out of that effort: privately-owned critical infrastructure.
“The nation will require a coordinated, multi-year effort
In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department.
Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory.
The technique then enjoyed a resurgence in the 1980s, fell into eclipse again in the first decade of the new century, and has returned like gangbusters in the second, fueled largely by the increased processing power of graphics chips.
“There’s this idea that ideas in science are a bit like epidemics of viruses,” says Tomaso Poggio, the
Hyper-connectivity has changed the way we communicate, wait, and productively use our time. Even in a world of 5G wireless and “instant” messaging, there are countless moments throughout the day when we’re waiting for messages, texts, and Snapchats to refresh. But our frustrations with waiting a few extra seconds for our emails to push through doesn’t mean we have to simply stand by.
To help us make the most of these “micro-moments,” researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a series of apps called “WaitSuite” that test you on vocabulary words during idle moments, like when you’re waiting for an instant message or for your phone to connect to WiFi.
Building on micro-learning apps like Duolingo, WaitSuite aims to leverage moments when a person wouldn’t otherwise be doing anything — a practice that its developers call “wait-learning.”
“With stand-alone apps, it can be inconvenient to have to separately open them up to do a learning task,” says MIT PhD student Carrie Cai, who leads the project. “WaitSuite is embedded directly into your existing tasks, so that you can easily learn without leaving what you were already doing.”
WaitSuite covers five common daily tasks: waiting for WiFi to connect, emails
Layla Shaikley SM ’13 began her master’s in architecture at MIT with a hunger to redevelop nations recovering from conflict. When she decided that data and logistics contributed more immediately to development than architecture did, Shaikley switched to the Media Lab to work with Professor Sandy Pentland, and became a cofounder of Wise Systems, which develops routing software that helps companies deliver goods and services.
“There’s nothing more creative than building a company,” Shaikley says. “We plan the most effective routes and optimize them in real time using driver feedback. Better logistics can dramatically reduce the number of late deliveries, increase efficiency, and save fuel.”
But Shaikley is perhaps better known for a viral video, “Muslim Hipsters: #mipsterz,” that she and friends created to combat the media stereotypes of Muslim women. It reached hundreds of thousands of viewers and received vigorous positive and negative feedback.
The video “is a really refreshing, jovial view of an underrepresented identity: young American Muslim women with alternative interests in the arts and culture,” Shaikley says. “The narrow media image is so far from the real fabric of Muslim-American life that we all need to add our pieces to the quilt to create a more accurate image.”