Maria Zuber awarded the Gerard P. Kuiper Prize in Planetary Sciences

The following news is adapted from a press release issued by the Division for Planetary Sciences of the American Astronomical Society.

The American Astronomical Society’s Division for Planetary Sciences (DPS) has awarded the 2019 Gerard P. Kuiper Prize for outstanding contributions to the field of planetary science to MIT Professor Maria Zuber for her advancements in geophysics, planetary gravity mapping, and laser altimetry. Zuber is the E.A. Griswold Professor of Geophysics in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) and vice president for research at MIT.

The Gerard P. Kuiper Prize honors scientists whose lifetime achievements have most advanced society’s understanding of the planetary system. Zuber’s numerous accomplishments include her seminal 2000 paper in Science combining Mars Global Surveyor laser altimetry data and gravity data to determine the crustal and upper mantle structure of Mars. Zuber became the first woman to lead a NASA spacecraft mission as principal investigator of the Gravity Recovery and Interior Laboratory (GRAIL) mission. GRAIL constructed a model of the moon’s gravitational field to spherical harmonic degree 1800, which exceeded the baseline requirement of the mission by an order of magnitude. Zuber has turned her attention to many different solid bodies in the solar system, focusing on structure and tectonics, including Mercury, Venus, Eros, Vesta, and Ceres. Since 1990, she has held leadership roles associated with scientific experiments or instrumentation on nine NASA missions.

Zuber has been at the helm of MIT’s research endeavors, overseeing more than a dozen interdisciplinary research laboratories and centers, ensuring intellectual integrity, and fostering research relationships. Over the years, she has advised a number of students and postdocs, and one reports that she strikes the perfect balance of being demanding, supportive, encouraging, and open-minded. 

As the recipient of the prize, Zuber will be invited to present a lecture at a DPS meeting and publish a written version of it in Icarus.

de MIT News http://bit.ly/2WgERI1

CSAIL hosts first-ever TEDxMIT

On Tuesday, May 28, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) hosted a special TEDx event featuring an all-female line-up of MIT scientists and researchers who discussed cutting-edge ideas in science and technology.

TEDxMIT speakers included roboticists, engineers, astronomers, and policy experts, including former White House chief technology officer Megan Smith ’86 SM ’88 and MIT Institute Professor Emerita Barbara Liskov, winner of the A.M. Turing Award, often considered the “Nobel Prize for computing.”

From Professor Nergis Mavalvala’s work on gravitational waves to Associate Provost Krystyn Van Vliet’s efforts to improve cell therapy, the afternoon was filled with energizing and historic success stories of women in STEM.

In an early talk, MIT Associate Professor Julie Shah touched on the much-discussed narrative of artificial intelligence and job displacement, and how that relates to her own work creating systems that she described as “being intentional about augmenting human capabilities.”She spoke about her efforts developing robots to help reduce the cognitive burden of overwhelmed workers, like the nurses on labor wards who have to make hundreds of split-second decisions for scheduling deliveries and C-sections.

“We can create a future where we don’t have robots who replace humans, but that help us accomplish what neither group can do alone,” said Shah.

CSAIL Director Daniela Rus, a professor of electrical engineering and computer science, spoke of how computer scientists can inspire the next generation of programmers by emphasizing the many possibilities that coding opens up.

“I like to say that those of us who know how to ... breathe life into things through programming have superpowers,” said Rus.

Throughout the day scientists showed off technologies that could fundamentally transform many industries, from Professor Dava Newman’s prototype Mars spacesuit to Associate Professor Vivienne Sze’s low-power processors for machine learning.

Judy Brewer, director of the World Wide Web Consortium’s Web Accessibility Initiative, discussed the ways in which the web has made the world a more connected place for those with disabilities — and yet, how important it is for the people who design digital technologies to be better about making them accessible.

“When the web became available, I could go and travel anywhere,” Brewer said. “There’s a general history of excluding people with disabilities, and then we go and design tech that perpetuates that exclusion. In my vision of the future everything is accessible, including the digital world.”

Liskov captivated the audience with her tales of the early days of computer programming. She was asked to learn Fortran on her first day of work in 1961 — having never written a line of code before.

“I didn’t have any training,” she said. “But then again, nobody did.”

In 1971 Liskov joined MIT, where she created the programming language CLU, which established the notion of “abstract data types” and laid the groundwork for languages like Java and C#. Many coders now take so-called “object-oriented programming” (OOP) for granted: She wryly reflected on how, after she won the Turing Award, one internet commenter looked at her contributions to data abstraction and pointed out that “everybody knows that, anyway.”

“It was a statement to how much the world has changed,” she said with a smile. “When I was doing that work decades earlier, nobody knew anything about [OOP].”

Other researchers built off of Liskov’s remarks in discussing the birth of big data and machine learning. Professor Ronitt Rubinfeld spoke about how computer scientists’ work in sublinear time algorithms has allowed them to better make sense of large amounts of data, while Hamsa Balakrishnan spoke about the ways in which algorithms can help systems engineers make air travel more efficient.

The event’s overarching them was to highlight examples of female role models in a field where they’ve often been overlooked. Paula Hammond, head of MIT’s Department of Chemical Engineering, touted the fact that more than half of undergrads in her department this year were women. Rus urged the women in the audience, many of whom were MIT students, to think about what role they might want to play in continuing to advance science in the coming years.

“To paraphrase our hometown hero, President John F. Kennedy, we need to prepare [women] to see both what technology can do for them — and what they can do for technology,” Rus said.

Rus led the planning of the TEDxMIT event alongside MIT research affiliate John Werner and student directors Stephanie Fu and Rucha Kelkar, both first-years.

de MIT News http://bit.ly/2HN6b7N

Professor Timothy Jamison named to new associate provost position

MIT announced today that it has created a new associate provost position, to be filled by Timothy Jamison, the Robert R. Taylor Professor of Chemistry and head of the Department of Chemistry. The Institute is also launching an expansive search for a new Institute community and equity officer (ICEO).

The new approach is intended to bolster MIT’s ability to implement programs and strategies that advance diversity, inclusion, equity, a positive climate, and a sense of community. It will also enable the Institute to conduct rigorous self-assessment of its own progress on these issues.

Jamison, who will serve as associate provost for a three-year term, will work with the incoming ICEO to help MIT’s departments create an inclusive campus community. Both Jamison and the ICEO will report to MIT Provost Martin A. Schmidt, who announced the new approach today in an email to the MIT community.

“I am delighted that Tim has agreed to assume this important role. Since 2015, he has led energetic efforts to enhance the quality of life for all members of the Department of Chemistry, and I have been tremendously impressed with his insight, sensitivity, and ability to inspire positive change,” Schmidt wrote in the email.

“I am very grateful for and look forward to this new opportunity to serve the Institute,” Jamison says. “It has been a privilege and pleasure to be head of the Department of Chemistry for the past four years. Looking ahead to this new role, my overarching aim is to support the faculty and their roles in the MIT community. My highest priorities include promoting diversity, inclusion, equity, and community, and to facilitate the search for our next ICEO.”

Alyce Johnson, who has been serving as MIT’s interim ICEO, is retiring this summer after a distinguished career in the Instutute’s leadership ranks. Since last fall, she has been consulting with the MIT community and working with Schmidt to plan the new path forward.

“I am extremely grateful to Alyce for her service as interim ICEO, and for her thoughtful engagement and guidance,” Schmidt wrote to the community.

“I appreciate the broad strategic approach these two roles embody in MIT’s long-standing pursuit of excellence in equity, inclusion and belonging,” Johnson says. “While we continue to collaborate and make forward strides, having dedicated leadership in this area will have a substantial impact on advancing our vision in a more directed and measurable way. We will benefit from the depth of knowledge and experience that both Tim and the new ICEO can bring.”

The new ICEO search will be open to candidates beyond the ranks of MIT faculty, a shift from how the position was originally implemented. This allows MIT to broaden the search and include experts with professional backgrounds in diversity and equity issues. This change was made after consideration of input from the MIT community.

MIT’s ICEO position was created in 2013 to advance activities and public discussion in the areas of community, equity, inclusion, and diversity — comprehensively across the Institute, for students, staff, and faculty. The first ICEO at the Institute, Ed Bertschinger, served from 2013 to 2018 and oversaw a widely read 2015 report identifying a range of inclusion issues in need of ongoing attention. 

Jamison will assume his new role beginning July 1. Jamison has been an MIT faculty member since 1999; he earned tenure in 2006 and was promoted to full professor in 2009.

As the new associate provost, Jamison will work to further codify and implement equitable practices across the full range of faculty experiences — including hiring practices, as well as review, promotion, and tenure cases. He says there are also important equity issues centered around the fair distribution of service roles among faculty, which he expects to evaluate as well.

The associate provost will work extensively with MIT’s MindHandHeart coalition — a campus initiative founded in 2015 that develops new approaches in support of health, well-being, and inclusion for people in the MIT community.

MindHandHeart often develops programs tailored to specific portions of the MIT community, an effort that converges with the associate provost’s goal of providing more departmental-level support at MIT, says Maryanne Kirkbride, the executive administrator of MindHandHeart. “We’re looking forward to working with Tim and the next ICEO to develop better individualized support for our academic departments,” she says.

Additionally, Jamison will bring new support to departments, as well as MIT’s five schools and the new MIT Stephen A. Schwarzman College of Computing, to help them create a fully professional climate of inclusion and community in daily life at the Institute.

Jamison brings a record of service and experience to these matters. He and Paula Hammond, head of MIT’s Department of Chemical Engineering and the David H. Koch Professor in Engineering, are currently co-chairs of a working group focused on implementing recommendations from a recent report on sexual harrassment produced by the National Academies of Sciences, Engineering, and Medicine.

The chemistry department, under the supervision of Jamison along with Sarah Rankin, the Institute’s Title IX cooordinator, and Kelley Adams, assistant dean in the Division of Student Life, has also instituted all-inclusive workshops on preventing sexual harrassment at MIT. Similar programs are now being implemented elsewhere at the Institute, including the chemical engineering department.

In the near future, Schmidt stated, he hopes that the presence of Jamison as associate provost, alongside the incoming ICEO, “will help us to move together toward our goal of One MIT.”

de MIT News http://bit.ly/2W7K6o9

For the women of McCormick, a new space in which to create

Outdated wall art has been replaced with a whiteboard for ideas, couches with an ergonomic work bench, and an old coffee table has made way for a 3-D printer. This is the new craft studio at McCormick Hall that has transformed a previously under-to-unused room into a thriving studio for crafts lovers.

Creating and crafting has long been a tradition at McCormick, MIT’s only all-women and women-identifying dormitory. To honor this tradition, McCormick has had a sewing room since 1967, although its drab ambience and lack of organization had dampened its usage.

“The original sewing room was basically the size of a closet and it was dark, unused, and really cluttered. We wanted a nicer space that more people would be able to use,” says sophomore Nyssa Miller, a resident and chair of sewing at McCormick.

In order to establish a more creative and welcoming space, Nyssa approached Emma Johnson, area director at McCormick, and Lily Gabaree, learning designer at the Media Lab. They brainstormed with other residents of the dorm who showed interest in having a community space to create. With the support of the residents, Johnson and Gabaree applied to the MindHandHeart Innovation Fund and were awarded a grant to found a modern craft studio at McCormick.

“We were really lucky. The process actually went very smoothly. We asked students more about their interests and heard a lot of interest in crafting, 3-D printing, and fiber arts. We talked to the [staff] in the Women’s and Gender Studies Program, and they were really supportive and gave us some ideas about things that were happening on campus. We wrote the proposal for MindHandHeart, which was a great process, and we got funding,” says Gabaree.

After much renovation with the help of MIT Housing and Residential Services, the craft studio opened its doors to McCormick residents and their friends earlier this semester. The entire process, from planning to execution, was an exercise in community building.

The residents of the hall spent several nights assembling furniture from IKEA and organizing an array of crafting tools, including a sewing machine, a serger for advanced sewing, a button maker, a 3-D printer, and other essential supplies for embroidery, knitting, crochet, and woodworking.

One of the first big community projects undertaken in the studio was the creation of McCormick’s "next-generation quilt." A similar quilt was first designed by residents on McCormick’s 50th anniversary six years ago to showcase the diverse ethnicities and cultures of the hall. It is now on display in the hall’s west tower.

“The McCormick Craft Studio is the fantastic result of a community effort … The students have been enthusiastically enjoying the new space and all the cool tools available,” says Raul Radovitzky, professor in the Department of Aeronautics and Astronautics at MIT and head of house of McCormick.

The studio’s founders believe the space encourages women and women-identifying students to continue being creative outside of their academic and work lives. Having an in-dorm space, they attest, will help to foster social connections and reduce isolation.

“Because we are MIT, we are known as hackers and makers, and having that in our dorm actually helps propel the culture that we want as MIT students,” says first-year student Varnika Sinha, a resident in charge of the 3-D printer who conducts regular trainings to instruct residents in the technology.

Now that there is a dedicated space for crafts, Johnson and Gabaree plan to organize open craft nights and more hands-on workshops to engage McCormick’s vibrant community of makers.

de MIT News http://bit.ly/2wvGYII

Cracking open the black box of automated machine learning

Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them.

Designing a machine-learning model for a certain task — such as image classification, disease diagnoses, and stock market prediction — is an arduous, time-consuming process. Experts first choose from among many different algorithms to build the model around. Then, they manually tweak “hyperparameters” — which determine the model’s overall structure — before the model starts training.

Recently developed automated machine-learning (AutoML) systems iteratively test and modify algorithms and those hyperparameters, and select the best-suited models. But the systems operate as “black boxes,” meaning their selection techniques are hidden from users. Therefore, users may not trust the results and can find it difficult to tailor the systems to their search needs.

In a paper presented at the ACM CHI Conference on Human Factors in Computing Systems, researchers from MIT, the Hong Kong University of Science and Technology (HKUST), and Zhejiang University describe a tool that puts the analyses and control of AutoML methods into users’ hands. Called ATMSeer, the tool takes as input an AutoML system, a dataset, and some information about a user’s task. Then, it visualizes the search process in a user-friendly interface, which presents in-depth information on the models’ performance.

“We let users pick and see how the AutoML systems works,” says co-author Kalyan Veeramachaneni, a principal research scientist in the MIT Laboratory for Information and Decision Systems (LIDS), who leads the Data to AI group. “You might simply choose the top-performing model, or you might have other considerations or use domain expertise to guide the system to search for some models over others.”

In case studies with science graduate students, who were AutoML novices, the researchers found about 85 percent of participants who used ATMSeer were confident in the models selected by the system. Nearly all participants said using the tool made them comfortable enough to use AutoML systems in the future.

“We found people were more likely to use AutoML as a result of opening up that black box and seeing and controlling how the system operates,” says Micah Smith, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in LIDS.

“Data visualization is an effective approach toward better collaboration between humans and machines. ATMSeer exemplifies this idea,” says lead author Qianwen Wang of HKUST. “ATMSeer will mostly benefit machine-learning practitioners, regardless of their domain, [who] have a certain level of expertise. It can relieve the pain of manually selecting machine-learning algorithms and tuning hyperparameters.”

Joining Smith, Veeramachaneni, and Wang on the paper are: Yao Ming, Qiaomu Shen, Dongyu Liu, and Huamin Qu, all of HKUST; and Zhihua Jin of Zhejiang University.

Tuning the model

At the core of the new tool is a custom AutoML system, called “Auto-Tuned Models” (ATM), developed by Veeramachaneni and other researchers in 2017. Unlike traditional AutoML systems, ATM fully catalogues all search results as it tries to fit models to data.

ATM takes as input any dataset and an encoded prediction task. The system randomly selects an algorithm class — such as neural networks, decision trees, random forest, and logistic regression — and the model’s hyperparameters, such as the size of a decision tree or the number of neural network layers.

Then, the system runs the model against the dataset, iteratively tunes the hyperparameters, and measures performance. It uses what it has learned about that model’s performance to select another model, and so on. In the end, the system outputs several top-performing models for a task.

The trick is that each model can essentially be treated as one data point with a few variables: algorithm, hyperparameters, and performance. Building on that work, the researchers designed a system that plots the data points and variables on designated graphs and charts. From there, they developed a separate technique that also lets them reconfigure that data in real time. “The trick is that, with these tools, anything you can visualize, you can also modify,” Smith says.

Similar visualization tools are tailored toward analyzing only one specific machine-learning model, and allow limited customization of the search space. “Therefore, they offer limited support for the AutoML process, in which the configurations of many searched models need to be analyzed,” Wang says. “In contrast, ATMSeer supports the analysis of machine-learning models generated with various algorithms.”

User control and confidence

ATMSeer’s interface consists of three parts. A control panel allows users to upload datasets and an AutoML system, and start or pause the search process. Below that is an overview panel that shows basic statistics — such as the number of algorithms and hyperparameters searched — and a “leaderboard” of top-performing models in descending order. “This might be the view you’re most interested in if you’re not an expert diving into the nitty gritty details,” Veeramachaneni says.

Similar visualization tools present this basic information, but without customization capabilities. ATMSeer includes an “AutoML Profiler,” with panels containing in-depth information about the algorithms and hyperparameters, which can all be adjusted. One panel represents all algorithm classes as histograms — a bar chart that shows the distribution of the algorithm’s performance scores, on a scale of 0 to 10, depending on their hyperparameters. A separate panel displays scatter plots that visualize the tradeoffs in performance for different hyperparameters and algorithm classes.

Case studies with machine-learning experts, who had no AutoML experience, revealed that user control does help improve the performance and efficiency of AutoML selection. User studies with 13 graduate students in diverse scientific fields — such as biology and finance — were also revealing. Results indicate three major factors — number of algorithms searched, system runtime, and finding the top-performing model — determined how users customized their AutoML searches. That information can be used to tailor the systems to users, the researchers say.

“We are just starting to see the beginning of the different ways people use these systems and make selections,” Veeramachaneni says. “That’s because now that this information is all in one place, and people can see what’s going on behind the scenes and have the power to control it.”

de MIT News http://bit.ly/2W3pVrF

Mentoring model developed at MIT spreads to new campuses

In the fall of 2018, three first-year students at Trinity University in Texas had an idea for a nonprofit that would connect artists with the special-needs community and help people engage with artwork regardless of their age or disability.

Fortunately for the students, Trinity had just launched a new program on campus to support entrepreneurs. In November, the students began meeting regularly with a team of experienced mentors from their community. The mentors helped the students refine their idea and prioritize their next steps to form an organization. The following semester, the project, called heARTful, was named a finalist in a local venture competition and awarded $5,000.

Around the same time, a family in Mobile, Alabama, was looking to adapt its furniture store to the world of online retail. It became a member of the first cohort of companies to go through a team-based mentoring program hosted by the University of South Alabama. The program also included a dental practices management platform, an online retailer of cigars, and someone with a software solution to help coordinate fishing tournaments that has raised almost $1 million to date.

What do all of these ventures have in common? They’re all benefiting from a mentoring model for entrepreneurs that was developed at MIT nearly 20 years ago.

The core methodology of MIT’s Venture Mentoring Service (VMS) is straightforward: Entrepreneurs meet with a team of mentors in ongoing, confidential meetings. The mentors are volunteers and commit to avoiding any conflict of interest to ensure that they give objective and unbiased advice.

But while the basic tenets of the model are simple, they require a robust and disciplined support structure to be effective.

The VMS team, facing increasing interest from outside organizations, launched the VMS Outreach Training Program in 2006 to formally disseminate the model to other organizations.

The Outreach Training Program has since trained close to 100 organizations around the world, including economic development organizations, business accelerators and incubators, and around 40 colleges and universities.

In that time, the VMS model has been tested on campuses of all types, and its success has earned it a reputation in higher education as an outstanding methodology for supporting entrepreneurs.

A model for impact

In 2000, VMS was founded by the late MIT Professor David Staelin and the late Alexander Dingee ’52, both successful serial entrepreneurs. Not much has changed from their early idea: Members of the MIT community, including students, faculty, staff, and alumni, can be at any stage of venture creation when they begin using VMS. A team of three to five volunteer mentors, meeting in person with the entrepreneur, provides business advice through a carefully structured process.

The program attracted attention from MIT’s peer institutions almost immediately.

“As we talk about our program with other organizaions, the concept of the trusted environment for the entrepreneur resonates; many people see that value,” says VMS Outreach Training Program Manager Ariane Martins, who also believes the team mentoring approach is a key to the program’s success. “Most people we talk to have never done mentoring in teams — certainly not in this structured way — but what we’ve seen is that it raises the quality and breadth of advice the entrepreneurs are given.”

The Outreach Training Program was formed in response to a growing demand, which was driven in part by several long-term trends in higher education, according to VMS Co-director Jerome Smith.

“These days, there are very few universities that aren’t talking about entrepreneurship or innovation” Smith says. “[VMS] is very complementary to other programs, because it is very practical. People gain a lot from the academic theory of entrepreneurship, but mentors in the VMS Model are working with entrepreneurs who are actually trying to start or grow a business.”

Schools have also collaborated with VMS to address increasing student interest in entrepreneurship. Luis Martinez is the director of the Center for Innovation and Entrepreneurship at Trinity University. Having worked with students in higher education for the better part of the last 20 years, he jokingly refers to college students today as “the Shark Tank generation.”

“When I was growing up, it used to be cool to start a band,” Martinez says. “Now it’s cool to start a company.”

As the VMS methodology has spread, its applicability has been proven in a wide range of settings, from Mexico to Australia. Some campuses, like Trinity, a small, liberal arts school, predominantly serve undergraduates with VMS, while others primarily help professors, researchers, and even members of the local community.

The University of Texas (UT) has participated in the Outreach Training Program three times to implement the VMS model on a number of campuses including the UT MD Anderson Cancer Center and UT Austin.

“Texas is very, very different from Boston, but what we’ve found is that the core principals of team-based, conflict-free mentoring still hold true everywhere we’ve tested it,” says Matt Sorenson, innovation program manager for the University of Texas system. “Each ecosystem is so different, but we’ve found the methodology is equally effective.”

Universities lifting communities

University officials also say the experiences of both the mentee and mentors in the VMS program can make a difference in the communities around their campuses.

“The impact [of the Trinity VMS program] has already been made in the community,” Martinez says. “It’s exciting, there’s a whole group of people now trained in the VMS methodology, both in the programming and the mentoring, so we’re trying to leverage the lessons being learned around the state and city.”

When the University of South Alabama decided to adopt the VMS methodology, it partnered with the city of Mobile, the local county, and the Chamber of Commerce to offer mentoring services not just to people affiliated with the school but also to local business owners.

“What’s great for smaller communities like ours is all this [meeting and learning in groups] means you’re building infrastructure,” says Michael Chambers, the associate vice president of research at the University of South Alabama. “All of a sudden you have an organized network of mentors, and they become aware of all these other local companies, and they become cheerleaders for those companies when they’re out in the community. … I don’t know where else we’d get that.”

de MIT News http://bit.ly/30Vier8

A behavioral economist explores poverty and development

On a sunny May day, Pierre-Luc Vautrey sits in 1369 Coffeehouse in Cambridge, talking enthusiastically about his work — five research projects to be exact. He speaks quickly, and the coffee gives him an extra boost. He has a lot of ground to cover, and at times he has to re-explain certain areas of his research. Luckily, he’s patient and wants to ensure that people understand his work.

Vautrey is a third-year doctoral student in MIT’s Department of Economics. While he spent his undergraduate years studying applied math and physics in his home country of France, he was always drawn to the humanities and social sciences.

“I still had this itch to go back to social science at some point. It just seemed like a really nice way to bridge science and quantitative approach with social science and humans in general. That’s how I got into economics,” he says.

As a behavioral economist, Vautrey aims to extend our understanding of economic decisions using psychology. This approach questions traditional assumptions, ever so slightly, in order to make outcomes more realistic regarding human behavior.

“Traditional economics has been modeling everything as rational. We assume that the agent learns like a statistician and makes rational decisions. And in the last 20 or 30 years, this model has shown its limits. It’s still very popular for many things, but for others we can do a lot better at explaining people’s behavior and why certain social systems work and some systems don’t work, by using psychology [to understand] how people actually think and make decisions,” he says.

The unifying theme throughout his current work is understanding how people form beliefs and expectations.

“You can use psychology to take a small departure, that’s the key, from rational behavior, which is having correct expectations and basing decisions on these expectations,” he says. “You still make decisions based on expectations, but you have incorrect beliefs for various psychological reasons. That’s kind of the key psychological, irrational approach that I’m interested in. What is the role of beliefs, how do we best measure them, and in various contexts can we explain why people have irrational beliefs? Can we predict incorrect beliefs of people based on context? Does it help us explain sometimes puzzling decisions?”

One of the projects Vautrey is working on, along with Professor Frank Schilbach from the Department of Economics, is how mental health affects beliefs and economic decision making. They began conducting research in India among people with depression in low-income communities with no access to mental health services. They want to evaluate whether depression affects a person’s self-confidence and, consequently, their ability to participate in their economy. They are working with Sangath, an NGO providing low-cost psychotherapy to the study’s participants, to measure the effects of psychotherapy on not only mental health, but also economic decisions. Vautrey began working on the project the fall of 2017, during its early brainstorming stages, and has visited India twice since the field work began.

“You have to go there to see how operations are going, see the actual participants, because it's really hard to get everything from calls. You have people implementing the project, but usually the people who have designed the questions or are initiating the idea are not full-time in the field because they are professors so they have to teach,” Vautrey explains.

Field visits are also important in order to see whether the research objective and the information gathered are consistent with each other.

“You have to design questions that are qualitative, that are verbal, but are going to generate numerical outcomes that you can analyze. It’s a back-and-forth between sociological-style research, when you talk to people and try to understand what they think, and how you go from there to build quantitative measures. You have to be on the field; you have to be face-to-face to understand whether your numeric outcome is consistent with what you want it to mean,” he says.

Traveling is important to executing research, and Vautrey enjoys that aspect of the job. He has loved traveling since his youth and has taken as many opportunities as he could to do so.

Beyond the project in India, Vautrey is working on a few other projects, two more in progress and two in their preliminary stages. In the former two, he is studying how people choose biased information sources and how people are influenced by news repetition. In another project with MIT economics doctoral student Charlie Rafkin, Vautrey is investigating unsafe driving patterns in developing countries and how drivers’ motivated reasoning about road safety leads to more risk taking that could be easily avoided by correcting drivers’ beliefs and overconfidence.

Vautrey’s newest endeavor is taking him to Colombia with Pedro Bessone Tepedino, another MIT economics doctoral student, for preliminary research for a new project centered around crime and teenage involvement in gangs.

While he enjoys doing all of his research, Vautrey finds that the work can make life a bit unstructured at times. He grounds himself by staying active with activities such as biking and rock climbing.

In the future, Vautrey hopes to work in academia. As a professor, he isn’t sure what specifically he wants to specialize in quite yet, but he says that it will likely have something to do with using psychology and economics to answer specific questions linked to poverty and development. He found a love for teaching through his work as a teaching assistant at MIT this past semester. It requires patience, but Vautrey finds the work rewarding.

“It’s a really nice feeling when you manage to get someone to understand something you said. When you have a class, it’s almost impossible to get everyone to understand everything you want,” he says, adding, “To me, if I get half of the class to understand something and to learn something they really value, I’m already happy.”

de MIT News http://bit.ly/2EEHR67

Empowering African farmers with data

With a couple billion more people estimated to join the global population in the next few decades, world food production could use an upgrade. Africa has a key role to play: Agriculture is Africa’s biggest industry, but much of Africa’s agricultural land is currently underutilized. Crop yields could be increased with more efficient farming techniques and new equipment — but that would require investment capital, which is often an obstacle for farmers.

A new research collaboration at the MIT Institute for Data, Systems, and Society (IDSS) aims to address this challenge with data. The group plans to use data from technologically advanced farms to better predict the value of intervention in underperforming farms. Ultimately, the goal is to create a platform for sharing data and risk among invested parties, from farmers and lenders to insurers and equipment manufacturers.

Sharing data, sharing risk

Many African farmers lack the capital to invest in yield-increasing upgrades like new irrigation systems, new machinery, new fertilizers, and technology for sensing and tracking crop growth. The most common path to capital is bank loans, with land as collateral. This is an unattractive proposition for farmers, who already bear the many risks of production, including bad weather, changing market prices, or even the shocks of geopolitical events.

Lenders, on the other hand, have an incomplete assessment of their risk, especially with potential borrowers who have no credit history. Lenders also lack data and tools to predict their return on investment.

“Building a platform for risk-sharing is key to upgrading farming practices,” says Munther Dahleh, a professor of electrical engineering and computer science at MIT and director of IDSS. In order to create such a platform, Dahleh and the IDSS team aim to better predict the value of employing advanced farming practices on the production of individual farms. This prediction needs to be accurate enough to incentivize investment from economic stakeholders and the farmers themselves, who are in competition with each other and may be reluctant to share information.

The IDSS approach proposes a data-sharing platform that incentivizes all parties to participate: Technologically advanced farms are rewarded for their valuable data, bankers benefit from data that support their credit risk models, farmers get better loan terms and recommendations that increase their profits and production, and technology companies get recommendations on how to best support the needs of their farmer customers. “Such a platform has to have the correct incentives to engage everyone to participate, have sufficient protection from players with market power, and ultimately provide valuable data for farmers and creditors alike,” says Dahleh.

The absence of data from underperforming farms presents a challenge to extrapolating the value of intervention and assessing the uncertainty in such predictions. With sparse available data, researchers are looking to conduct experiments in strategically selected farms to provide valuable new data for the rest. Researchers will use advanced machine learning, including active learning methodology, to try to achieve both a quantification of the predicted value of intervention and a quantification of the uncertainty of that prediction to a degree of confidence. Once more data is available, IDSS researchers intend to refine their calculations and develop new techniques for extrapolating the value of intervention in less-advanced farms.

Engaging stakeholders

One likely intervention for many African farmers involves using different fertilizers. Many farmers aren’t currently using fertilizers targeted to specific soil or various stages of farming — so fertilizer producers are another vested interest in this agriculture economy.

To help these farmers get access to better loan terms, Moroccan phosphate company OCP is funding a collaboration between IDSS researchers and Mohammed VI Polytechnic University (UM6P) in Morocco. This research collaboration with OCP, a leading global company in the phosphate fertilizer industry, includes building the data- and risk-sharing platform as well as other foundational research in agriculture. The collaboration has the potential to engage other stakeholders working or investing in African agriculture.

“This collaboration will help accelerate our efforts to develop pertinent solutions for African agriculture using high-level agri-tech tools,” says Fassil Kebede, professor of soil science and head of the Center for Soil and Fertilizer Research in Africa. “This will offer farmers possibilities for better production and growth, which is part of our mission to contribute to Africa’s food-security objectives.”

“African farmers are at the heart of the OCP Group’s mission and strategy, while data analytics and predictive tools are today essential for agriculture development in Africa,” adds Mostafa Terrab, OCP Group chair and CEO. “This collaboration with IDSS will help us bring together new technology and analytical methods from one side, and our expertise with African farmers and their challenges from the other side. It will reinforce our capabilities to offer adapted solutions to African farmers, especially small holders, to enable them to make more precise and timely decisions.”

Ultimately, IDSS aims to bring wins across an entire economic ecosystem, from insurers to lenders to equipment and fertilizer companies. But most importantly, boosting this ecosystem could help lift many farmers out of poverty — and bring about a much-needed increase in the world’s aggregate food production.

Says Dahleh: “To accomplish this mission, this project will demonstrate the power of data coupled with advanced tools from predictive analytics, machine learning, reinforcement learning, and data sharing markets.”

de MIT News http://bit.ly/2I7NTwV

Q&A: Phillip Isola on the art and science of generative models

If you’ve ever wondered what a loaf of bread would look like as a cat, edges2cats is for you. The program that turns sketches into pictures of cats is one of many whimsical creations inspired by Phillip Isola’s image-to-image translation software released in the early days of generative adversarial networks, or GANs. In a 2016 paper, Isola and his colleagues showed how a new type of GAN could transform a hand-drawn shoe into its fashion-photo equivalent, or turn an aerial photo into a grayscale map. Later, the researchers showed how landscape photos could be reimagined in the impressionist brushstrokes of Monet or Van Gogh. Now an assistant professor in MIT’s Department of Electrical Engineering and Computer Science, Isola continues to explore what GANs can do. 

GANs work by pairing two neural networks, trained on a large set of images. One network, the generator, outputs an image patterned after the training examples. The other network, the discriminator, rates how well the generator’s output image resembles the training data. If the discriminator can tell it’s a fake, the generator tries again and again until its output images are indistinguishable from the examples. When Isola first heard of GANs, he was experimenting with nearest-neighbor algorithms to try to infer the underlying structure of objects and scenes.

GANs have an uncanny ability to get at the essential structure of a place, face, or object, making structured prediction easier. Introduced five years ago, GANs have been used to visualize the ravages of climate change, produce more realistic computer simulations, and protect sensitive data, among other applications.

To connect the growing number of GAN enthusiasts at MIT and beyond, Isola has recently helped to organize GANocracy, a day of talks, tutorials, and posters being held at MIT on May 31 that is co-sponsored by the MIT Quest for Intelligence and MIT-IBM Watson AI Lab. Isola recently spoke about the future of GANs.

Q: Your image-to-image translation paper has more than 2,000 citations. What made it so popular?

A: It was one of the earliest papers to show that GANs are useful for predicting visual data. We showed that this setting is very general, and can be thought of as translating between different visualizations of the world, which we called image-to-image translation. GANs were originally proposed as a model for producing realistic images from scratch. But the most useful application may be structured prediction, which is what GANs are mostly being used for these days.

Q: GANs are easily customized and shared on social media. Any favorites among these projects?

A: #Edges2cats is probably my favorite, and it helped to popularize the framework early on. Architect Nono Martínez Alonso has used pix2pix for exploring interesting tools for sketch-based design. I like everything by Mario Klingemann; Alternative Face is especially thought-provoking. It puts one person’s words into someone else’s mouth, hinting at a potential future of “alternative facts.” Scott Eaton is pushing the limits of GANs by translating sketches into 3-D sculptures. 

Q: What other GAN art grabs you?

A: I really like all of it. One remarkable example is GANbreeder. It’s a human-curated evolution of GAN-generated images. The crowd chooses which images to breed or kill off. Over many generations, we end up with beautiful and unexpected images.

Q: How are GANs being used beyond art? 

A: In medical imaging, they’re being used to generate CT scans from MRIs. There’s potential there, but it can be easy to misinterpret the results: GANs are making predictions, not revealing the truth. We don't yet have good ways to measure the uncertainty of their predictions. I'm also excited about the use of GANs for simulations. Robots are often trained in simulators to reduce costs, creating complications when we deploy them in the real world. GANs can help bridge the gap between simulation and reality.

Q: Will GANs redefine what it means to be an artist?

A: I don't know, but it's a super-interesting question. Several of our GANocracy speakers are artists, and I hope will touch on this. GANs and other generative models are different than other kinds of algorithmic art. They are trained to imitate, so the people being imitated probably deserve some credit. The art collective, Obvious, recently sold a GAN image at Christie's for $432,500. Obvious selected the image, signed and framed it, but the code was derived by then-17-year-old Robbie Barrat. Ian Goodfellow helped develop the underlying algorithm. 

Q: Where is the field heading?

A: As amazing as GANs are, they are just one type of generative model. GANs might eventually fade in popularity, but generative models are here to stay. As models of high-dimensional structured data, generative models get close to what we mean when we say “create,” “visualize,” and “imagine.” I think they will be used more and more to approximate capabilities that still seem uniquely human. But GANs do have some unique properties. For one, they solve the generative modeling problem via a two-player competition, creating a generator-discriminator arms race that leads to emergent complexity. Arms races show up across machine learning, including in the AI that achieved superhuman abilities in the game Go.

Q: Are you worried about the potential abuse of GANs?

A: I’m definitely concerned about the use of GANs to generate and spread misleading content, or so-called fake news. GANs make it a lot easier to create doctored photos and videos, where you no longer have to be a video editing expert to make it look like a politician is saying something they never actually said.

Q: You and the other GANocracy organizers are advocating for so-called GANtidotes. Why?

A: We would like to inoculate society against the misuse of GANs. Everyone could just stop trusting what we see online, but then we’d risk losing touch with reality. I’d like to preserve a future in which “seeing is believing.” Luckily, many people are working on technical antidotes that range from detectors that seek out the telltale artifacts in a GAN-manipulated image to cryptographic signatures that verify that a photo has not been edited since it was taken. There are a lot of ideas out there, so I’m optimistic it can be solved.

de MIT News http://bit.ly/2MeT17I

Ultra-Quantum Matter research gets $8 million boost

MIT professors Senthil Todadri and Xiao-Gang Wen are members of the newly established Simons Collaboration on Ultra-Quantum Matter. The effort, funded by the Simons Foundation, is an $8 million four-year award, renewable for three additional years, and will support theoretical physics research across 12 institutions, including MIT.

The science of the collaboration is based on a series of recent developments in theoretical physics, revealing that even large macroscopic systems that consist of many atoms or electrons — matter — can behave in an essentially quantum way. Such ultra-quantum matter (UQM) allows for quantum phenomena beyond what can be realized by individual atoms or electrons, including distributed storage of quantum information, fractional quantum numbers, and perfect conducting boundary. 

While some examples of UQM have been experimentally established, many more have been theoretically proposed, ranging from highly entangled topological states to unconventional metals that behave like a complex soup. The Simons Collaboration on Ultra-Quantum Matter will classify possible forms of UQM, understand their physical properties, and provide the key ideas to enable new realizations of UQM in the lab. 

Ultra dream team

In particular, the collaboration will draw upon lessons from recently discovered connections between topological states of matter and unconventional metals, and seeks to develop a new theoretical framework for those phases of ultra-quantum matter. Achieving these goals requires ideas and tools from multiple areas of theoretical physics, and accordingly the collaboration brings together experts in condensed matter physics, quantum field theory, quantum information, and atomic physics to forge a new interdisciplinary approach.
 
Directed by Professor Ashvin Vishwanath at Harvard University, the collaboration comprises researchers at MIT, Harvard, Caltech, the Institute for Advanced Study, Stanford University, University of California at Santa Barbara, University of California at San Diego, University of Chicago, University of Colorado at Boulder, University of Innsbruck, University of Maryland, and University of Washington.  
 
“I am looking forward to scientific interactions with MIT theorists Senthil and Wen, who are key members of our Simons collaboration on Ultra-Quantum Matter, and hope this will further strengthen collaborations within the Cambridge area and beyond. Their research on highly entangled quantum materials is of fundamental significance, and may provide new directions for device applications, quantum computing, and high-temperature superconductors,” says collaboration director Ashvin Vishwanath of Harvard University. 

“They have also been mentors for several collaboration members,” says Vishwanath, who worked with Senthil as a Pappalardo Fellow in physics from 2001 to 2004.

Senthil has played a leading role in the field of non-Fermi liquids, in the classification of strongly interacting topological insulators and related topological phases, and in the development of field theory dualities with diverse applications in condensed matter physics.

Wen is one of the founders of the field of topological phases of matter, introducing the concept of topological order in 1989 and opening up a new research direction in condensed matter physics. Wen’s research has often exposed mathematical structures that have not appeared before in condensed matter physics problems.

MIT-grown

Of the 17 faculty members who are participating in the collaboration, more than half, including Senthil, Wen, and Vishwanath, have MIT affiliations. 

Michael Hermele, the collaboration’s deputy director and an associate professor at the University of Colorado at Boulder, was a postdoc in the MIT Condensed Matter Theory group. 

Associate professors Xie Chen PhD ’12 and Michael Levin PhD ’06, at Caltech and the University of Chicago, respectively, earned their doctorates at MIT under Wen. 

Other principal investigators include alumni Subir Sachdev ’82, now chair of the Department of Physics at Harvard, and Leon Balents ’89, a physics professor at UC Santa Barbara's Kavli Institute for Theoretical Physics. John McGreevy, a string theorist who conducted research in the Center for Theoretical Physics (CTP), is now a professor of physics at UC San Diego. Dam Thanh Son and Andreas Karch, former CTP postdocs, are now with the University of Chicago and the University of Washington, respectively. 

The collaboration is part of the Simons Collaborations in Mathematics and Physical Sciences program, which aims to “stimulate progress on fundamental scientific questions of major importance in mathematics, theoretical physics and theoretical computer science.” The Simons Collaboration on Ultra-Quantum Matter is one of 12 such collaborative grants ranging across these fields.

The first meeting of the newly established collaboration will take place Sept. 12-13 in Cambridge, Massachusetts.

de MIT News http://bit.ly/2XfGIJp

J-WAFS announces seven new seed grants

Agricultural productivity technologies for small-holder farmers; food safety solutions for everyday consumers; sustainable supply chain interventions in the palm oil industry; water purification methods filtering dangerous micropollutants from industrial and wastewater streams — these are just a few of the research-based solutions being supported by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) at MIT. J-WAFS is funding these and other projects through its fifth round of seed grants, providing over $1 million in funding to the MIT research community. These grants, which are funded competitively to MIT principal investigators (PIs) across all five schools at the Institute, exemplify the ambitious goals of MIT’s Institute-wide effort to address global water and food systems challenges through research and innovation. 

This year, seven new projects led by nine faculty PIs across all five schools will be funded with two-year grants of up to $150,000, overhead-free. Interest in water and food systems research at MIT is substantial, and growing. By the close of this grant cycle, over 12 percent of MIT faculty will have submitted J-WAFS grant proposals. Thirty-four principal investigators submitted proposals to this latest call, nearly one third of whom were proposing to J-WAFS for the first time. “The broad range of disciplines that this applicant pool represents demonstrates how meeting today’s water and food challenges is motivating many diverse researchers in our community," comments Renee Robins, executive director of J-WAFS. "Our reach across all of MIT’s schools further attests to the strength of the Institute’s capabilities that can be applied to the search for solutions to pressing water and food sector challenges.” The nine faculty who were funded represent eight departments and labs, including the departments of Civil and Environmental Engineering, Mechanical Engineering, Chemical Engineering, Chemistry, and Economics, as well as the Media Lab (School of Architecture and Planning), MIT D-Lab (Office of the Vice Chancellor), and the Sloan School of Management.

New approaches to ensure safe drinking water

Nearly 1 billion people worldwide receive their drinking water through underground pipes that only operate intermittently. In contrast to continuous water supplies, pipes like these that are only filled with water during limited supply periods are vulnerable to contamination. However, it is challenging to quantify the quality of water that comes out of these pipes because of the vast differences in how the pipe networks are arranged and where they are located, especially in dense urban settings. Andrew J. Whittle, the Edmund K. Turner Professor in Civil Engineering, seeks to address this problem by gathering and making available more precise data on how water quality is affected by how the pipe is used — i.e., during periods of filling, flushing, or stagnation. Supported by the seed grant, he and his research team will perform tests in a section of abandoned pipe in Singapore, one that is still connected to the urban water pipe network there. By controlling flushing rates, monitoring stagnation, and measuring contamination, the study will analyze how variances in flow affect water quality, and evaluate how these data might be able to inform future water quality studies in cities with similar piped water challenges.

Patrick Doyle, the Robert T. Haslam (1911) Professor of Chemical Engineering, is taking a different approach to water quality: creating a filter to remove micropollutants. Wastewater from industrial and agricultural processes often contains solvents, petrochemicals, lubricants, pharmaceuticals, hormones, and pesticides, which can enter natural water systems. While these micropollutants may be present at low concentrations, they can still have a significant negative impact on aquatic ecosystems, as well as human health. The challenge is in detecting and removing these micropollutants, because of the low concentrations in which they occur. For this project, Doyle and his team will develop a system to remove a variety of micropollutants, at even the smallest concentrations, using a special hydrogel particle that can be “tuned” to fit the size and shape of particular particles. Leveraging the flexibility of these hydrogels, this technology can improve the speed, precision, efficiency, and environmental sustainability of industrial water purification systems, and improve the health of the natural water systems upon which humans and our surrounding ecosystems rely.

Developing support tools for small-holder farmers

More than half of food calories consumed globally — and 70 percent of food calories consumed in developing countries — are supplied by approximately 475 million small-holder households in developing and emerging economies. These farmers typically operate through informal contracts and processes, which can lead to large economic inefficiencies and lack of traceability in the supply chains that they are a part of. Joann de Zegher, the Maurice F. Strong Career Development Professor in the operations management program at the MIT Sloan School of Management, seeks to address these challenges by developing a mobile-based trading platform that links small-holder farmers, middlemen, and mills in the palm oil supply chain in Indonesia. Rapid growth in demand in this industry has led to high environmental costs, and recently pressure from consumers and nongovernmental organizations is motivating producers to employ more sustainable practices. However, these pressures deepen market access challenges for small-holder palm oil farmers. Her project seeks to improve the efficiency and effectiveness of the current supply chain, and create transparency as a byproduct.

Another small-holder farmer intervention is being developed by Robert M. Townsend, the Elizabeth and James Killian Professor of Economics. He is leading a research effort to improve access to crop insurance for small-holder farmers, who are particularly vulnerable to weather-related crop failures. Crop cultivation worldwide is highly vulnerable to unfavorable weather. In developing countries, farmers bear the financial burden of their crops’ exposure to weather ravages, the extent of which will only increase due to the effects of climate change. As a result, they rely on low-risk, low-yield cultivation practices that do not allow for the food and financial gains that can be possible when favorable weather supports higher yields. While crop insurance can help, it is often prohibitively expensive for these small-scale producers. Townsend and his research team seek to make crop insurance more accessible and affordable for farmers in developing regions by developing a new system of insurance pricing and payoff schedules that takes into account the widely varying ways through which weather affects crop’s development and yield throughout the growth cycle. Their goal is to provide a new, personalized insurance tool that improves farmers’ ability to protect their yields, invest in their crops, and adapt to climate change in order to stabilize food supply and farmer livelihoods worldwide. 

Access to affordable fertilizer is another challenge that small holders face. Ammonia is the key ingredient in fertilizers; however, most of the world’s supply is produced by the Haber-Bosch process, which directly converts nitrogen and hydrogen gas to ammonia in a highly capital-intensive process that is difficult to downscale. Finding an alternative way to synthesize ammonia could transform access to fertilizer and improve food security, particularly in the developing world where current fertilizers are prohibitively expensive. For this seed grant project, Yogesh Surendranath, Paul M Cook Career Development Assistant Professor in the Department of Chemistry, will develop an electrochemical process to synthesize ammonia, one that can be powered using renewable energy sources such as solar or wind. Designed to be implemented in a decentralized way, this technology could enable fertilizer production directly in the fields where it is needed, and would be especially beneficial in developing regions without access to existing ammonia production infrastructure.

Even when crops produce high yields, post-harvest preservation is a challenge, especially to fruit and vegetable farmers on small plots of land in developing regions. The lack of affordable and effective post-harvest vegetable cooling and storage poses a significant challenge for them, and can lead to vegetable spoilage, reduced income, and lost time. Most techniques for cooling and storing vegetables rely on electricity, which is either unaffordable or unavailable for many small-holder farmers, especially those living on less than $3 per day in remote areas. The solution posed by an interdisciplinary team led by Daniel Frey, professor in the Department of Mechanical Engineering and D-Lab faculty director, along with Leon Glicksman, professor of architecture and mechanical engineering, is a storage technology that uses the natural evaporation of water to create a cool and humid environment that prevents rot and dehydration, all without the need for electricity. This system is particularly suited for hot, dry regions such as Kenya, where the research team will be focusing their efforts. The research will be conducted in partnership with researchers from University of Nairobi’s Department of Plant Science and Crop Protection, who have extensive experience working with low-income rural communities on issues related to horticulture and improving livelihoods. The team will build and test evaporative cooling chambers in rural Kenya to optimize the design for performance, practical construction, and user preferences, and will build evidence for funders and implementing organizations to support the dissemination of these systems to improve post-harvest storage challenges.

Combatting food safety challenges through wireless sensors

Food safety is a matter of global concern, and a subject that several J-WAFS-funded researchers seek to tackle with innovative technologies. And for good reason: Food contamination and foodborne pathogens cause sickness and even death, as well as significant economic costs including the wasted labor and resources that occur when a contaminated product is disposed of, the lost profit to affected companies, and the lost food products that could have nourished a number of people. Fadel Adib, an assistant professor at the MIT Media Lab, will receive a seed grant to develop a new tool that quickly and accurately assesses whether a given food product is contaminated. This food safety sensor uses wireless signals to determine the quality and safety of packaged food using a radio-frequency identification sticker placed on the product’s container. The system turns off-the-shelf RFID tags into spectroscopes which, when read, can measure the material contents of a product without the need to open its package. The sensor can also identify the presence of contaminants — pathogens as well as adulterants that affect the nutritional quality of the food product. If successful, this research, and the technology that results, will pave the way for wireless sensing technologies that can inform their users about the health and safety of their food and drink.

With these seven newly funded projects, J-WAFS will have funded 37 total seed research projects since its founding in 2014. These grants serve as important catalysts of new water and food sector research at MIT, resulting in publications, patents, and other significant research support. To date, J-WAFS’ seed grant PIs have been awarded over $11M in follow-on funding. J-WAFS’ director, Professor John Lienhard, commented on the influence of this grant program: “The betterment of society drives our research community at MIT. Water and food, our world’s most vital resources, are currently put at great risk by a variety of global-scale challenges, and MIT researchers are responding forcefully. Through this, and J-WAFS’ other grant programs, we see MIT's creative innovations and actionable solutions that will help to ensure a sustainable future.”

J-WAFS Seed Grants, 2019

• Learning Food and Water Contaminants using Wireless Signals

PI: Fadel Adib, assistant professor, MIT Media Lab

• Designing Supply Chain Platforms for Smallholders in Indonesia

PI: Joann de Zegher, Maurice F. Strong Career Development Professor, Sloan School of Management

• Microparticle Systems for the Removal of Organic Micropollutants

PI: Patrick Doyle, Robert T. Haslam (1911) Professor of Chemical Engineering, Department of Chemical Engineering

• Evaporative Cooling Technologies for Vegetable Preservation in Kenya

PIs: Daniel Frey, professor, Department of Mechanical Engineering, and faculty research director, MIT D-Lab; Leon Glicksman, professor of building technology and mechanical engineering, Department of Mechanical Engineering

• Electrocatalytic Ammonia Synthesis for Distributed Agriculture

PI: Yogesh Surendranath, Paul M Cook Career Development Assistant Professor, Department of Chemistry

• Designing Purely Weather-Contingent Crop Insurance with Personalized Coverage to Improve Farmers’ Investments in their Crops for Higher Yields

PI:  Robert M. Townsend, Elizabeth and James Killian Professor of Economics, Department of Economics

• Understanding Effects of Intermittent Flow on Drinking Water Quality

PI: Andrew J. Whittle, Edmund K. Turner Professor in Civil Engineering, Department of Civil and Environmental Engineering

de MIT News http://bit.ly/2Z19LB3

Teaching language models grammar really does make them smarter

Voice assistants like Siri and Alexa can tell the weather and crack a good joke, but any 8-year-old can carry on a better conversation.

The deep learning models that power Siri and Alexa learn to understand our commands by picking out patterns in sequences of words and phrases. Their narrow, statistical understanding of language stands in sharp contrast to our own creative, spontaneous ways of speaking, a skill that starts developing even before we are born, while we're still in the womb. 

To give computers some of our innate feel for language, researchers have started training deep learning models on the grammatical rules that most of us grasp intuitively, even if we never learned how to diagram a sentence in school. Grammatical constraints seem to help the models learn faster and perform better, but because neural networks reveal very little about their decision-making process, researchers have struggled to confirm that the gains are due to the grammar, and not the models’ expert ability at finding patterns in sequences of words. 

Now psycholinguists have stepped in to help. To peer inside the models, researchers have taken psycholinguistic tests originally developed to study human language understanding and adapted them to probe what neural networks know about language. In a pair of papers to be presented in June at the North American Chapter of the Association for Computational Linguistics conference, researchers from MIT, Harvard University, University of California, IBM Research, and Kyoto University have devised a set of tests to tease out the models’ knowledge of specific grammatical rules. They find evidence that grammar-enriched deep learning models comprehend some fairly sophisticated rules, performing better than models trained on little-to-no grammar, and using a fraction of the data.

“Grammar helps the model behave in more human-like ways,” says Miguel Ballesteros, an IBM researcher with the MIT-IBM Watson AI Lab, and co-author of both studies. “The sequential models don’t seem to care if you finish a sentence with a non-grammatical phrase. Why? Because they don’t see that hierarchy.”

As a postdoc at Carnegie Mellon University, Ballesteros helped develop a method for training modern language models on sentence structure called recurrent neural network grammars, or RNNGs. In the current research, he and his colleagues exposed the RNNG model, and similar models with little-to-no grammar training, to sentences with good, bad, or ambiguous syntax. When human subjects are asked to read sentences that sound grammatically off, their surprise is registered by longer response times. For computers, surprise is expressed in probabilities; when low-probability words appear in the place of high-probability words, researchers give the models a higher surprisal score.

They found that the best-performing model — the grammar-enriched RNNG model — showed greater surprisal when exposed to grammatical anomalies; for example, when the word “that” improperly appears instead of “what” to introduce an embedded clause; “I know what the lion devoured at sunrise” is a perfectly natural sentence, but “I know that the lion devoured at sunrise” sounds like it has something missing — because it does.

Linguists call this type of construction a dependency between a filler (a word like who or what) and a gap (the absence of a phrase where one is typically required). Even when more complicated constructions of this type are shown to grammar-enriched models, they — like native speakers of English — clearly know which ones are wrong. 

For example, “The policeman who the criminal shot the politician with his gun shocked during the trial” is anomalous; the gap corresponding to the filler “who” should come after the verb, “shot,” not “shocked.” Rewriting the sentence to change the position of the gap, as in “The policeman who the criminal shot with his gun shocked the jury during the trial,” is longwinded, but perfectly grammatical.

“Without being trained on tens of millions of words, state-of-the-art sequential models don’t care where the gaps are and aren’t in sentences like those,” says Roger Levy, a professor in MIT’s Department of Brain and Cognitive Sciences, and co-author of both studies. “A human would find that really weird, and, apparently, so do grammar-enriched models.”

Bad grammar, of course, not only sounds weird, it can turn an entire sentence into gibberish, underscoring the importance of syntax in cognition, and to psycholinguists who study syntax to learn more about the brain’s capacity for symbolic thought.“Getting the structure right is important to understanding the meaning of the sentence and how to interpret it,” says Peng Qian, a graduate student at MIT and co-author of both studies. 

The researchers plan to next run their experiments on larger datasets and find out if grammar-enriched models learn new words and phrases faster. Just as submitting neural networks to psychology tests is helping AI engineers understand and improve language models, psychologists hope to use this information to build better models of the brain. 

“Some component of our genetic endowment gives us this rich ability to speak,” says Ethan Wilcox, a graduate student at Harvard and co-author of both studies. “These are the sorts of methods that can produce insights into how we learn and understand language when our closest kin cannot.”

de MIT News http://bit.ly/2MhfL7g

Sensor-packed glove learns signatures of the human grasp

Wearing a sensor-packed glove while handling a variety of objects, MIT researchers have compiled a massive dataset that enables an AI system to recognize objects through touch alone. The information could be leveraged to help robots identify and manipulate objects, and may aid in prosthetics design.

The researchers developed a low-cost knitted glove, called “scalable tactile glove” (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to “learn” a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed.

In a paper published today in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects — including a soda can, scissors, tennis ball, spoon, pen, and mug. Using the dataset, the system predicted the objects’ identities with up to 76 percent accuracy. The system can also predict the correct weights of most objects within about 60 grams.

Similar sensor-based gloves used today run thousands of dollars and often contain only around 50 sensors that capture less information. Even though STAG produces very high-resolution data, it’s made from commercially available materials totaling around $10.

The tactile sensing system could be used in combination with traditional computer vision and image-based datasets to give robots a more human-like understanding of interacting with objects.

“Humans can identify and handle objects well because we have tactile feedback. As we touch objects, we feel around and realize what they are. Robots don’t have that rich feedback,” says Subramanian Sundaram PhD ’18, a former graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We’ve always wanted robots to do what humans can do, like doing the dishes or other chores. If you want robots to do these things, they must be able to manipulate objects really well.”

The researchers also used the dataset to measure the cooperation between regions of the hand during object interactions. For example, when someone uses the middle joint of their index finger, they rarely use their thumb. But the tips of the index and middle fingers always correspond to thumb usage. “We quantifiably show, for the first time, that, if I’m using one part of my hand, how likely I am to use another part of my hand,” he says.

Prosthetics manufacturers can potentially use information to, say, choose optimal spots for placing pressure sensors and help customize prosthetics to the tasks and objects people regularly interact with.

Joining Sundaram on the paper are: CSAIL postdocs Petr Kellnhofer and Jun-Yan Zhu; CSAIL graduate student Yunzhu Li; Antonio Torralba, a professor in EECS and director of the MIT-IBM Watson AI Lab; and Wojciech Matusik, an associate professor in electrical engineering and computer science and head of the Computational Fabrication group.  

STAG is laminated with an electrically conductive polymer that changes resistance to applied pressure. The researchers sewed conductive threads through holes in the conductive polymer film, from fingertips to the base of the palm. The threads overlap in a way that turns them into pressure sensors. When someone wearing the glove feels, lifts, holds, and drops an object, the sensors record the pressure at each point.

The threads connect from the glove to an external circuit that translates the pressure data into “tactile maps,” which are essentially brief videos of dots growing and shrinking across a graphic of a hand. The dots represent the location of pressure points, and their size represents the force — the bigger the dot, the greater the pressure.

From those maps, the researchers compiled a dataset of about 135,000 video frames from interactions with 26 objects. Those frames can be used by a neural network to predict the identity and weight of objects, and provide insights about the human grasp.

To identify objects, the researchers designed a convolutional neural network (CNN), which is usually used to classify images, to associate specific pressure patterns with specific objects. But the trick was choosing frames from different types of grasps to get a full picture of the object.

The idea was to mimic the way humans can hold an object in a few different ways in order to recognize it, without using their eyesight. Similarly, the researchers’ CNN chooses up to eight semirandom frames from the video that represent the most dissimilar grasps — say, holding a mug from the bottom, top, and handle.

But the CNN can’t just choose random frames from the thousands in each video, or it probably won’t choose distinct grips. Instead, it groups similar frames together, resulting in distinct clusters corresponding to unique grasps. Then, it pulls one frame from each of those clusters, ensuring it has a representative sample. Then the CNN uses the contact patterns it learned in training to predict an object classification from the chosen frames.

“We want to maximize the variation between the frames to give the best possible input to our network,” Kellnhofer says. “All frames inside a single cluster should have a similar signature that represent the similar ways of grasping the object. Sampling from multiple clusters simulates a human interactively trying to find different grasps while exploring an object.”

For weight estimation, the researchers built a separate dataset of around 11,600 frames from tactile maps of objects being picked up by finger and thumb, held, and dropped. Notably, the CNN wasn’t trained on any frames it was tested on, meaning it couldn’t learn to just associate weight with an object. In testing, a single frame was inputted into the CNN. Essentially, the CNN picks out the pressure around the hand caused by the object’s weight, and ignores pressure caused by other factors, such as hand positioning to prevent the object from slipping. Then it calculates the weight based on the appropriate pressures.

The system could be combined with the sensors already on robot joints that measure torque and force to help them better predict object weight. “Joints are important for predicting weight, but there are also important components of weight from fingertips and the palm that we capture,” Sundaram says.

de MIT News http://bit.ly/2YZH9rV

Call for nominations: MIT Media Lab’s Disobedience Award

The MIT Media Lab has opened the call for nominations for its third annual Disobedience Award. The $250,000 cash prize will go to a person or group to recognize individuals and groups who engage in ethical, nonviolent acts of disobedience in service of society. The award is open to nominations for anyone still living and active in any field, including the arts, academia, law, politics, science, and social advocacy. A diverse selection committee composed of experts in a wide range of fields will choose the winner(s) and finalist(s), who will be announced in November.

The criteria for the Disobedience Award include nonviolence, creativity, and personal responsibility: It’s about speaking truth to power, taking responsibility, and demanding systemic change.

“Disobedience can mean different things in different spaces,” says Media Lab Director Joi Ito. “Defying a formal process or deeply ingrained culture, such as we might see in academia and the sciences, looks very different from staging a nonviolent civil protest, or resisting political pressure. What these things have in common is moral courage, a willingness to take personal risk, and a commitment to a goal beyond personal gain.”

As head of the selection committee, Ito hopes to see nominations from around the world — from expected as well as unexpected quarters. Although the Disobedience Award was not intended to function as a popularity contest or commentary on specific controversies, Ito says, the annual nature of the award means that it will often reflect the zeitgeist of any given year.

Previous winners and finalists have included Mona Hanna-Attisha and Marc Edwards, physicians who fought to expose and correct the water crisis in Flint, Michigan; Tarana Burke, BethAnn McLaughlin, and Sherry Marts, three leaders of the #MeToo and #MeTooStem movements; the Standing Rock water protectors; a representative of the 2018 West Virginia teachers’ strike; and numerous advocates and defenders of immigrants’ rights and environmental protection.

Nominations can be submitted at mitdisobedienceaward.fluidreview.com.

de MIT News http://bit.ly/2KcfoIi

Pantry ingredients can help grow carbon nanotubes

Baking soda, table salt, and detergent are surprisingly effective ingredients for cooking up carbon nanotubes, researchers at MIT have found.

In a study published this week in the journal Angewandte Chemie, the team reports that sodium-containing compounds found in common household ingredients are able to catalyze the growth of carbon nanotubes, or CNTs, at much lower temperatures than traditional catalysts require.

The researchers say that sodium may make it possible for carbon nanotubes to be grown on a host of lower-temperature materials, such as polymers, which normally melt under the high temperatures needed for traditional CNT growth.

“In aerospace composites, there are a lot of polymers that hold carbon fibers together, and now we may be able to directly grow CNTs on polymer materials, to make stronger, tougher, stiffer composites,” says Richard Li, the study’s lead author and a graduate student in MIT’s Department of Aeronautics and Astronautics. “Using sodium as a catalyst really unlocks the kinds of surfaces you can grow nanotubes on.”

Li’s MIT co-authors are postdocs Erica Antunes, Estelle Kalfon-Cohen, Luiz Acauan, and Kehang Cui; alumni Akira Kudo PhD ’16, Andrew Liotta ’16, and Ananth Govind Rajan SM ’16, PhD ’19; professor of chemical engineering Michael Strano, and professor of aeronautics and astronautics Brian Wardle, along with collaborators at the National Institute of Standards and Technology and Harvard University.

Peeling onions

Under a microscope, carbon nanotubes resemble hollow cylinders of chicken wire. Each tube is made from a rolled up lattice of hexagonally arranged carbon atoms. The bond between carbon atoms is extraordinarily strong, and when patterned into a lattice, such as graphene, or as a tube, such as a CNT, such structures can have exceptional stiffness and strength, as well as unique electrical and chemical properties. As such, researchers have explored coating various surfaces with CNTs to produce stronger, stiffer, tougher materials.

Researchers typically grow CNTs on various materials through a process called chemical vapor deposition. A material of interest, such as carbon fibers, is coated in a catalyst — usually an iron-based compound — and placed in a furnace, through which carbon dioxide and other carbon-containing gases flow. At temperatures of up to 800 degrees Celsius, the iron starts to draw carbon atoms out of the gas, which glom onto the iron atoms and to each other, eventually forming vertical tubes of carbon atoms around individual carbon fibers. Researchers then use various techniques to dissolve the catalyst, leaving behind pure carbon nanotubes.

Li and his colleagues were experimenting with ways to grow CNTs on various surfaces by coating them with different solutions of iron-containing compounds, when the team noticed the resulting carbon nanotubes looked different from what they expected.

“The tubes looked a little funny, and Rich and the team carefully peeled the onion back, as it were, and it turns out a small quantity of sodium, which we suspected was inactive, was actually causing all the growth,” Wardle says.

Tuning sodium’s knobs

For the most part, iron has been the traditional catalyst for growing CNTs. Wardle says this is the first time that researchers have seen sodium have a similar effect.

“Sodium and other alkali metals have not been explored for CNT catalysis,” Wardle says. “This work has led us to a different part of the periodic table.”

To make sure their initial observation wasn’t just a fluke, the team tested a range of sodium-containing compounds. They initially experimented with commercial-grade sodium, in the form of baking soda, table salt, and detergent pellets, which they obtained from the campus convenience store. Eventually, however, they upgraded to purified versions of those compounds, which they dissolved in water. They then immersed a carbon fiber in each compound’s solution, coating the entire surface in sodium. Finally, they placed the material in a furnace and carried out the typical steps involved in the chemical vapor deposition process to grow CNTs.

In general, they found that, while iron catalysts form carbon nanotubes at around 800 degrees Celsius, the sodium catalysts were able to form short, dense forests of CNTs at much lower temperatures, of around 480 C. What’s more, after surfaces spent about 15 to 30 minutes in the furnace, the sodium simply vaporized away, leaving behind hollow carbon nanotubes.

“A large part of CNT research is not on growing them, but on cleaning them —getting the different metals used to grow them out of the product,” Wardle says. “The neat thing with sodium is, we can just heat it and get rid of it, and get pure CNT as product, which you can’t do with traditional catalysts.”

Li says future work may focus on improving the quality of CNTs that are grown using sodium catalysts. The researchers observed that while sodium was able to generate forests of carbon nanotubes, the walls of the tubes were not perfectly aligned in perfectly hexagonal patterns — crystal-like configurations that give CNTs their characteristic strength. Li plans to “tune various knobs” in the CVD process, changing the timing, temperature, and environmental conditions, to improve the quality of sodium-grown CNTs.

“There are so many variables you can still play with, and sodium can still compete pretty well with traditional catalysts,” Li says. “We anticipate with sodium, it is possible to get high quality tubes in the future. And we have pretty high confidence that, even if you were to use regular Arm and Hammer baking soda, it should work.”

For Shigeo Maruyama, professor of mechanical engineering at the University of Tokyo, the ability to cook up CNTs from such a commonplace ingredient as sodium should reveal new insights into the way the exceptionally strong materials grow.

“It is a surprise that we can grow carbon nanotubes from table salt!” says Maruyama, who was not involved in the research. “Even though chemical vapor deposition (CVD) growth of carbon nanotubes has been studied for more than 20 years, nobody has tried to use alkali group metal as catalyst. This will be a great hint for the fully new understanding of growth mechanism of carbon nanotubes.”

This research was supported, in part, by Airbus, Boeing, Embraer, Lockheed Martin, Saab AB, ANSYS, Saertex, and TohoTenax through MIT’s Nano-Engineered Composite aerospace STructures (NECST) Consortium.

de MIT News http://bit.ly/2YYqsx1

MIT students organize FAIL! ― an initiative to destigmatize failure and build resilience

Many members of the science and technology community are inspired by the startup mantra “fail fast and fail often.” They aim to remain calm and resolute when their experiments go awry, startups dissolve, and problem sets occasionally go unfinished.

When it comes to the lived experience of navigating setbacks, however, many end up failing at failing. They internalize the experience and treat failure as a reflection of their abilities, rather than an unavoidable part of life, necessary for personal growth.

This very human tendency was the inspiration for FAIL!, an event series committed to destigmatizing failure. MIT graduate and visiting students Francesco Benedetti, Chengzhao Zhang, Giannandrea Inchingolo, David Rolnick, Tanja Mueller, Simone Bruno, Luca Alfeo, Stefano Deluca, and Sandra Rothenbuecher founded FAIL! in spring 2018. To date, there have been three FAIL! conferences held at MIT, drawing sold-out crowds of 350-400 people. 

At each conference, prominent scholars from MIT and Harvard University share 10-minute stories of personal, academic, and professional failures, followed by a Q&A session with the audience. By learning of the challenges and missteps of highly successful people, the organizers hope to reduce the discouragement and isolation attendees may feel when confronted with their own failures.

Fail! was funded by the MindHandHeart Innovation Fund, a grant program supporting projects that advance mental health, community, diversity, and inclusion at MIT. The series was also supported by the Division of Student Life, MIT Sandbox, MIT VISTA, MIT Graduate Student Council, and MITell. 

What it means to fail

MIT professor of computer science Daniel Jackson, who recently published a book on resilience at MIT, opened this April’s FAIL! Conference by reflecting on the different types of failure. “There’s what I call ‘little-f failure’ and ‘big-F failure,’” he said. “Little-f failure is when you do something and you screw it up … Big-F failure is when your whole life comes to nothing.”

Big-F failures, he noted, are relatively rare, although fear of them can lead people to avoid taking worthwhile risks and limit their ability to lead full, meaningful lives. “Talking about fear and failure is the key to changing ourselves and the culture in which we live,” said Jackson, emphasizing the importance of events like FAIL! that create spaces to explore these topics.

Professor of humanities, sociology, and anthropology Susan Silbey, who was recently awarded MIT’s highest faculty honor, the Killian Award, spoke after Jackson. Although Silbey has had a celebrated career with seemingly few little-f failures, she struggled to find direction and mentorship as a graduate student.

“I started my PhD two months after I graduated college,” said Silbey. “In 1962 there weren’t very many women who joined PhD programs at the University of Chicago. That was quite extraordinary in that year. What was more extraordinary is that I did not graduate until 1978. Sixteen years. That is not the career of a star: That is a failure.” Silbey credited her eventual success to her love of learning and research, regardless of the topic she was studying.

Harvard Medical School professor of genetics George Church spoke at the FAIL! Conference held in November 2018. Those who know him as a founding father of synthetic biology would be surprised to learn that he spent six months homeless and failed out of graduate school at Duke University prior to being accepted to a PhD program at Harvard University, where he later graduated.

Church encouraged the audience to not only embrace their own failures, but to learn from the failures of others. “I’ve learned as much from my negative role models as I did from my positive ones,” he said. “They had trouble, and you’re trying to learn from their trouble without personally experiencing it.”

The success of FAIL!

A survey of the first two FAIL! conferences showed a satisfaction rate above 90 percent. “We were able to start a community,” says Francesco Benedetti, one of the FAIL! organizers and a postdoc in chemical engineering at MIT. “People started conversations about failure and made friends because of the experiences they had in common.”

In February, FAIL! was awarded first prize in the “Live” category of the BetterMIT Innovation Challenge for successfully “expanding study spaces and student life.” The challenge was organized by the Undergraduate Association Committee on Innovation and Technology.

This spring, FAIL! organizers piloted a workshop series on the topic of failure to complement their conferences. Ten graduate students met on a monthly basis with a FAIL! faculty presenter to discuss times they had failed and what they had learned from their experiences.

“These workshops help bridge the gap between inspiring speakers and students who would like to change their relationship with failure,” says Kanika Gakhar, a first-year graduate student and lead organizer of the FAIL! workshop. “By sharing personal experiences and coping strategies, students have an opportunity to feel accepted and learn from each other.”

The FAIL! initiative is also expanding beyond MIT. In March, a FAIL! Conference was held at the International Institute of Information Technology in Hyderabad, India and drew a crowd of 350 people.

Starting over

When confronted with failure, it can be hard to know how to start again. Among the FAIL! Conference speakers, and those who organized the series, there was no one path forward. The only thing that was true for all of them was that they did start again.

FAIL! organizer Chengzhao Zhang, who is pursuing a PhD in mathematics at MIT, reflects: “I’ve failed at so many things; I don’t know where to begin. When I was an undergrad, I scored 35/120 on a partial differential equations midterm. I had never scored so low on a test. But afterwards, I still stuck with the field because of the beauty of math and its ability to model physical and engineering phenomena. Now I am able to do PhD-level research at one of the best institutes in the world.” 

“It’s scary to fail,” Zhang acknowledges. “You’ll doubt your ability, your worthiness, and your intelligence in confronting it. But failure is no reason to stop trying. Reflect upon the mistakes you made and learn a lesson from them.”

“FAIL! is about being human,” adds Benedetti. “We all need inspiring and realistic role models. By sharing the challenges and vulnerabilities that many people try to hide, our brave speakers are helping to create an environment where students feel comfortable being themselves and expressing their creativity. We believe that FAIL! is providing a model of thoughtfulness and humility, which will inspire attendees to be better leaders.”

Other prominent speakers at the MIT FAIL! Conferences include: Allan Adams, physicist and principal investigator of the Future Ocean Lab at MIT; Amanda Bosh, astronomer and planetary scientist at MIT; Amy Edmonson, professor of leadership and management at the Harvard Business School; Arthur Bahr, associate professor of literature at MIT; Deborah Blum, Pulitzer Prize-winning author and director of the Knight Science Journalism Program at MIT; Mariana Castells, professor of medicine at Harvard Medical School; Mira Wilczek, president and CEO of Cogo Labs and a senior partner at Link Ventures; Muriel Medard, professor of electrical engineering at MIT; Nuno Loureiro, associate professor of physics at MIT; and Regina Bateson, assistant professor of political science at MIT. Kirsty Bennett, manager of MITell, an on-campus storytelling initiative, hosted the conferences and John Werner, curator of TEDxBeaconStreet, moderated the Q&A session at the fall 2018 conference.

The next FAIL! Conference will take place in fall 2019.

de MIT News http://bit.ly/2HFRpzz