Last summer, MIT researchers published a paper describing an algorithm that can recover intelligible speech from the analysis of the minute vibrations of objects in video captured through soundproof glass. In June, at the Conference on Computer Vision and Pattern Recognition, researchers from the same groups will describe how the technique can be adapted to infer material properties of physical objects, such as stiffness and weight, from video. The technique could have application in the field of "nondestructive testing," or determining materials' physical properties without extracting samples from them or subjecting them to damaging physical tests. It might be possible, for instance, to identify structural defects in an airplane's wing by analyzing video of its vibration during flight. "One of the big contributions of this work is connecting techniques in computer vision to established theory on physical vibrations and to a whole body of work in nondestructive testing in civil engineering," says Abe Davis, an MIT graduate student in electrical engineering and computer science who, together with fellow graduate student Katie Bouman, is first author on the paper.

In recent years, the field of academic publishing has ballooned to an estimated 30,000 peer-reviewed journals churning out some 2 million articles per year. While this growth has led to more scientific scholarship, critics argue that it has also spurred increasing numbers of low-quality "predatory publishers" who spam researchers with weekly "calls for papers" and charge steep fees for articles that they often don't even read before accepting. Ten years ago, a few students at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) had noticed such unscrupulous practices, and set out to have some mischievous fun with it. Jeremy Stribling MS '05 PhD '09, Dan Aguayo '01 MEng '02 and Max Krohn PhD '08 spent a week or two between class projects to develop "SCIgen," a program that randomly generates nonsensical computer-science papers, complete with realistic-looking graphs, figures, and citations. SCIgen emerged out of Krohn's previous work as co-founder of the online study guide SparkNotes, which included a generator of high-school essays that was based on "context-free grammar."

Scientists have long known that our ability to think quickly and recall information, also known as fluid intelligence, peaks around age 20 and then begins a slow decline. However, more recent findings, including a new study from neuroscientists at MIT and Massachusetts General Hospital (MGH), suggest that the real picture is much more complex. The study, which appears in the journal Psychological Science, finds that different components of fluid intelligence peak at different ages, some as late as age 40. "At any given age, you're getting better at some things, you're getting worse at some other things, and you're at a plateau at some other things. There's probably not one age at which you're peak on most things, much less all of them," says Joshua Hartshorne, a postdoc in MIT's Department of Brain and Cognitive Sciences and one of the paper's authors.

Most recent advances in artificial intelligence -- such as mobile apps that convert speech to text -- are the result of machine learning, in which computers are turned loose on huge data sets to look for patterns. To make machine-learning applications easier to build, computer scientists have begun developing so-called probabilistic programming languages, which let researchers mix and match machine-learning techniques that have worked well in other contexts. In 2013, the U.S. Defense Advanced Research Projects Agency, an incubator of cutting-edge technology, launched a four-year program to fund probabilistic-programming research. At the Computer Vision and Pattern Recognition conference in June, MIT researchers will demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional systems with thousands of lines of code. "This is the first time that we're introducing probabilistic programming in the vision area," says Tejas Kulkarni, an MIT graduate student in brain and cognitive sciences and first author on the new paper.

To some degree, that's inevitable: Many people who enroll in MOOCs may have no interest in doing homework, but simply plan to listen to video lectures in their spare time. Others, however, may begin courses with the firm intention of completing them but get derailed by life's other demands. Identifying those people before they drop out and providing them with extra help could make their MOOC participation much more productive. The problem is that you don't know who's actually dropped out -- or, in MOOC parlance, "stopped out" -- until the MOOC has been completed. One missed deadline does not a stopout make; but after the second or third missed deadline, it may be too late for an intervention to do any good.

Much artificial-intelligence research addresses the problem of making predictions based on large data sets. An obvious example is the recommendation engines at retail sites like Amazon and Netflix. But some types of data are harder to collect than online click histories -- information about geological formations thousands of feet underground, for instance. And in other applications -- such as trying to predict the path of a storm -- there may just not be enough time to crunch all the available data. Dan Levine, an MIT graduate student in aeronautics and astronautics, and his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics, have developed a new technique that could help with both problems.

Michael Sipser, the Barton L. Weller Professor of Mathematics and head of the Department of Mathematics since 2004, has been named dean of the School of Science. Sipser has served as the school's interim dean since December, when he was chosen to replace Marc Kastner, the Donner Professor of Physics; in November, President Barack Obama announced his intention to nominate Kastner to head the Department of Energy's Office of Science. "In 10 years as head of MIT's Department of Mathematics, Mike Sipser sustained its extraordinary stature while building a warm sense of community," MIT President L. Rafael Reif says. "His integrity, fairness, and patience will serve him very well in the role of dean. And as the School of Science faces difficult trends in federal funding, I believe Mike's gift for explaining complex scientific concepts will be a tremendous asset in Washington."

When the MIT Big Data Challenge asked, "What can you learn from data about 2.3 million taxi rides?" graduate students in professor Marta González's research lab had some answers. Based on their experience writing machine-learning algorithms that find meaningful patterns in very large data sets, and on their skill applying those patterns to understand how people use transportation in urban areas, the students were able to predict the number of taxi pickups that had occurred in 700 time intervals at 36 locations in the Boston area. Their predictions were the best in the competition, earning them the number one spot and $4,000 in prize money. The scientific visualization of the data prepared by one team member garnered a second-place prize and an additional $1,000. The awards were announced mid-March.

Faculty, students, and industry representatives packed MIT's Grier Room on Thursday, Dec. 4, to learn about new research in robotics, machine learning, wireless power transfer, synthetic biology, and more -- all being conducted by undergraduates through the groundbreaking SuperUROP program within the Department of Electrical Engineering and Computer Science (EECS). "My feeling is some of this work is as good as master's-quality research. It's very good stuff," said Vincent Chan, the Joan and Irwin M. Jacobs Professor of Electrical Engineering and Aeronautics and Astronautics, who is advising two SuperUROP students this year. "We are creating a community of scholars. As they are exposed to the breadth of research in EECS, their excitement and enthusiasm to engage in research and innovation is contagious," said Anantha Chandrakasan, the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering and EECS department head, who launched the year-long SuperUROP research program in 2012 to expand the experience familiar to many through MIT's Undergraduate Research Opportunities Program (UROP).

The late professor Seth Teller created 6.811 (Principles and Practices in Assistive Technologies, or PPAT) in the fall of 2011. Through his extensive experience developing assistive technologies (AT) at MIT, his compassion for making technology available to all, and his innovative approach and drive to build this class, student interest in PPAT and AT has grown steadily since. Following Teller's untimely death on July 1 this year, a group of former PPAT and AT students including his graduate student William Li SM '12, who TA'd the inaugural PPAT offering; Grace Teo PhD '14, a former student and member of the MIT Assistive Technology Club; and a core group of students who took the class in 2013 have formed a team to continue Teller's legacy through both the coninuation of PPAT and an outgrowth known as "AT Hack," a one-day workshop launched in spring 2014. Li and Teo, who will co-instruct this year's class, and three other members of the team will work with Professor Rob Miller, MIT MacVicar Faculty Fellow, member of the Computer Science and Artificial Intelligence Lab (CSAIL), and co-education officer of the Department of Electrocal Engineering and Computer Science (EECS). Every year since the inaugural offering of PPAT, Miller had worked with Teller to help develop and teach the course.