Monthly Archives: March 2017

Strengthen the intersection of policy and technology

“When you’re part of a community, you want to leave it better than you found it,” says Keertan Kini, an MEng student in the Department of Electrical Engineering, or Course 6. That philosophy has guided Kini throughout his years at MIT, as he works to improve policy both inside and out of MIT.

As a member of the Undergraduate Student Advisory Group, former chair of the Course 6 Underground Guide Committee, member of the Internet Policy Research Initiative (IPRI), and of the Advanced Network Architecture group, Kini’s research focus has been in finding ways that technology and policy can work together. As Kini puts it, “there can be unintended consequences when you don’t have technology makers who are talking to policymakers and you don’t have policymakers talking to technologists.” His goal is to allow them to talk to each other.

At 14, Kini first started to get interested in politics. He volunteered for President Obama’s 2008 campaign, making calls and putting up posters. “That was the point I became civically engaged,” says Kini. After that, he was campaigning for a ballot initiative to raise more funding for his high school, and he hasn’t stopped being interested in public policy since.

High school was also where Kini became interested in computer science. He took a computer science class in high school on the recommendation of his sister, and in his senior year, he started watching computer science lectures on MIT OpenCourseWare (OCW) by Hal Abelson, a professor in MIT’s Department of Electrical Engineering and Computer Science.

“That lecture reframed what computer science was. I loved it,” Kini recalls. “The professor said ‘it’s not about computers, and it’s not about science’. It might be an art or engineering, but it’s not science, because what we’re working with are idealized components, and ultimately the power of what we can actually achieve with them is not based so much on physical limitations so much as the limitations of the mind.”

In part thanks to Abelson’s OCW lectures, Kini came to MIT to study electrical engineering and computer science. Kini is currently pursuing an MEng in electrical engineering and computer science, a fifth-year master’s program following his undergraduate studies in electrical engineering and computer science.

Preserving their fundamental mathematical relationships

One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.

The methods for creating such “coresets” vary according to application, however. Last week, at the Annual Conference on Neural Information Processing Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and the University of Haifa in Israel presented a new coreset-generation technique that’s tailored to a whole family of data analysis tools with applications in natural-language processing, computer vision, signal processing, recommendation systems, weather prediction, finance, and neuroscience, among many others.

“These are all very general algorithms that are used in so many applications,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper. “They’re fundamental to so many problems. By figuring out the coreset for a huge matrix for one of these tools, you can enable computations that at the moment are simply not possible.”

As an example, in their paper the researchers apply their technique to a matrix — that is, a table — that maps every article on the English version of Wikipedia against every word that appears on the site. That’s 1.4 million articles, or matrix rows, and 4.4 million words, or matrix columns.

That matrix would be much too large to analyze using low-rank approximation, an algorithm that can deduce the topics of free-form texts. But with their coreset, the researchers were able to use low-rank approximation to extract clusters of words that denote the 100 most common topics on Wikipedia. The cluster that contains “dress,” “brides,” “bridesmaids,” and “wedding,” for instance, appears to denote the topic of weddings; the cluster that contains “gun,” “fired,” “jammed,” “pistol,” and “shootings” appears to designate the topic of shootings.

Joining Rus on the paper are Mikhail Volkov, an MIT postdoc in electrical engineering and computer science, and Dan Feldman, director of the University of Haifa’s Robotics and Big Data Lab and a former postdoc in Rus’s group.

Engineering senior Caroline Colbert has found herself

During January of her junior year at MIT, Caroline Colbert chose to do a winter externship at Massachusetts General Hospital (MGH). Her job was to shadow the radiation oncology staff, including the doctors that care for patients and medical physicists that design radiation treatment plans.

Colbert, now a senior in the Department of Nuclear Science and Engineering (NSE), had expected to pursue a career in nuclear power. But after working in a medical environment, she changed her plans.

She stayed at MGH to work on building a model to automate the generation of treatment plans for patients who will undergo a form of radiation therapy called volumetric-modulated arc therapy (VMAT). The work was so interesting that she is still involved with it and has now decided to pursue a doctoral degree in medical physics, a field that allows her to blend her training in nuclear science and engineering with her interest in medical technologies.

She’s even zoomed in on schools with programs that have accreditation from the Commission on Accreditation of Medical Physics Graduate Programs so she’ll have the option of having a more direct impact on patients. “I don’t know yet if I’ll be more interested in clinical work, research, or both,” she says. “But my hope is to work in a hospital setting.”

Many NSE students and faculty focus on nuclear energy technologies. But, says Colbert, “the department is really supportive of students who want to go into other industries.”

It was as a middle school student that Colbert first became interested in engineering. Later, in a chemistry class, a lesson about nuclear decay set her on a path towards nuclear science and engineering. “I thought it was so cool that one element can turn into another,” she says. “You think of elements as the fundamental building blocks of the physical world.”

Colbert’s parents, both from the Boston area, had encouraged her to apply to MIT. They also encouraged her towards the medical field. “They loved the idea of me being a doctor, and then when I decided on nuclear engineering, they wanted me to look into medical physics,” she says. “I was trying to make my own way. But when I did look seriously into medical physics, I had to admit that my parents were right.”

Identifies letters printed on first nine pages

MIT researchers and their colleagues are designing an imaging system that can read closed books.

In the latest issue of Nature Communications, the researchers describe a prototype of the system, which they tested on a stack of papers, each with one letter printed on it. The system was able to correctly identify the letters on the top nine sheets.

“The Metropolitan Museum in New York showed a lot of interest in this, because they want to, for example, look into some antique books that they don’t even want to touch,” says Barmak Heshmat, a research scientist at the MIT Media Lab and corresponding author on the new paper. He adds that the system could be used to analyze any materials organized in thin layers, such as coatings on machine parts or pharmaceuticals.

Heshmat is joined on the paper by Ramesh Raskar, an associate professor of media arts and sciences; Albert Redo Sanchez, a research specialist in the Camera Culture group at the Media Lab; two of the group’s other members; and by Justin Romberg and Alireza Aghasi of Georgia Tech.

The MIT researchers developed the algorithms that acquire images from individual sheets in stacks of paper, and the Georgia Tech researchers developed the algorithm that interprets the often distorted or incomplete images as individual letters. “It’s actually kind of scary,” Heshmat says of the letter-interpretation algorithm. “A lot of websites have these letter certifications [captchas] to make sure you’re not a robot, and this algorithm can get through a lot of them.”

Timing terahertz

The system uses terahertz radiation, the band of electromagnetic radiation between microwaves and infrared light, which has several advantages over other types of waves that can penetrate surfaces, such as X-rays or sound waves. Terahertz radiation has been widely researched for use in security screening, because different chemicals absorb different frequencies of terahertz radiation to different degrees, yielding a distinctive frequency signature for each. By the same token, terahertz frequency profiles can distinguish between ink and blank paper, in a way that X-rays can’t.

Terahertz radiation can also be emitted in such short bursts that the distance it has traveled can be gauged from the difference between its emission time and the time at which reflected radiation returns to a sensor. That gives it much better depth resolution than ultrasound.

The system exploits the fact that trapped between the pages of a book are tiny air pockets only about 20 micrometers deep. The difference in refractive index — the degree to which they bend light — between the air and the paper means that the boundary between the two will reflect terahertz radiation back to a detector.

In the researchers’ setup, a standard terahertz camera emits ultrashort bursts of radiation, and the camera’s built-in sensor detects their reflections. From the reflections’ time of arrival, the MIT researchers’ algorithm can gauge the distance to the individual pages of the book.