Community forum gives insight into how The Engine will run

At a community forum on MIT’s new startup accelerator, The Engine, administrators discussed the new enterprise and fielded questions about its formation, components, mission, funding, mentor and equipment access, startup-selection process, and other issues.

The forum, held last night in Building 32, opened with remarks from MIT’s leadership who helped launch The Engine: MIT President L. Rafael Reif, professor and head of the Department of Electrical Engineering and Computer Science Anantha Chandrakasan, Provost Martin Schmidt, and Executive Vice President and Treasurer Israel Ruiz.

The floor then opened up to professors, students, alumni, and other MIT community members, who posed questions about The Engine’s selection process and components, and offered advice and potential opportunities for collaboration.

Announced in October, The Engine is a new venture aimed at supporting entrepreneurs pursuing transformative technologies that are capital- and time-intensive. The venture aims to provide those entrepreneurs hundreds of millions of dollars in funding, and make available hundreds of thousands of square feet of space in Kendall Square and nearby communities. A web-based app, called the Engine Room, will allow entrepreneurs to use or rent specialized resources from MIT, and participating companies and institutions, including office and conference spaces on and off campus, clean rooms, and other facilities and specialized equipment. The venture will also introduce entrepreneurs to peers, mentors, and established companies in innovation clusters across the region and around the world.

Within a day following the announcement, The Engine’s website received about a thousand inquiries from those wanting to become involved. In February, The Engine will host a forum for startups that have submitted or plan to submit applications to discuss the program and selection process in more detail. In the spring, The Engine program will formally be kicked off and the first startups will enter the accelerator.

Making The Engine

In his opening remarks, President Reif laid out key reasons why MIT launched The Engine. One is to provide “patient” capital to entrepreneurs developing transformative technologies, who sometimes have difficulty finding funding; another is to help keep startups in the region. A third reason, he added, is to set successful examples for aspiring entrepreneurs in developing transformative technologies.

“Many students at MIT that are interested in [pursuing] these ideas in science-based innovation see that ideas like theirs don’t make it through, because there’s no patient capital,” he said. “We’d like those ideas to have a path for success to the marketplace to inspire others that want to do something similar.”

Ruiz recalled the term “innovation orchard” from President Reif’s May 2015 op-ed in The Washington Post. The piece discussed how supporters of innovation from the public, for-profit, and nonprofit sectors could form coalitions to provide physical space, mentorship, and bridge funding to transformative startups, to support their transition from idea to investment.  

In creating MIT’s “innovation orchard,” Ruiz said, the Institute considered several realities: transformative-technology startups at MIT didn’t have a lot of early-stage resources; venture capital funding was focusing increasingly on shorter timeframes, higher valuations, and higher expectations on returns; and most MIT and local entrepreneurs had to head to the West Coast for find support. “We wanted to think about how we could … say, ‘Yes, there are the same opportunities here at MIT,’” Ruiz said, referencing the final point.        

Chandrakasan introduced the Engine Room app and discussed how The Engine will work collaboratively with MIT’s existing innovation programs to address issues with funding, scaling up, equipment usage, forming founding teams, and other matters.

In selecting startups, Chandrakasan added, The Engine’s evaluation committee will consist of experts from outside MIT to avoid any conflicts of interest. “It’s also worth pointing out that funding for this, the investors, will be those who are committed to patient capital and/or seeing regional growth,” he said.

Schmidt introduced several ad hoc working groups formed for The Engine, “which are going to be structured to look at some of the key pieces that are critical to the success of people from our campus community exercising and using The Engine and the Engine Room.”

The committees are: the Engine Advisory Committee, led by Chandrakasan and populated by the leaders of each other group; the Facilities Access Working Group, led by Martin Culpepper, a professor of mechanical engineering and the “maker czar” in MIT’s Department of Mechanical Engineering; the Technology Licensing Working Group, led by Tim Swager, the John D. MacArthur Professor of Chemistry; the Conflict of Interest Working Group, led by Klavs Jensen, a professor of chemical engineering; the Visas for Entrepreneurs Working Group, led by Dick Yue, the Philip J. Solondz Professor of Engineering; and MIT’s Innovation Ecosystem Working Group, led by MIT Innovation Initiative co-directors Fiona Murray, who is the William Porter (1967) Professor of Entrepreneurship at MIT Sloan School of Management, and Vladimir Bulovic, the Fariborz Maseeh (1990) Professor of Emerging Technology.

Addressing community needs

After remarks, Ruiz and Chandrakasan fielded questions from more than a dozen MIT community members, including entrepreneurs, students, professors, and representatives of organizations and startup incubators in the region.

Answers to many questions shed light on The Engine’s components and selection process. For example, digital resources, such as CAD and other software for hardware design, will most likely be offered through the equipment-sharing program; there are no current restrictions on the types of startups accepted into the accelerator; The Engine plans to begin talks about potential collaborations with local incubators, such as Greentown Labs; and coordinated events will connect engineering students with MIT Sloan students, other entrepreneurs, and mentors.

One commenter asked if The Engine would support clinical trials for medical-device startups, a key startup category along with biotechnology, robotics, manufacturing, and energy. Ruiz noted MIT has strong existing partnerships with Boston hospitals, which could help startups transition more easily into clinical trials. But, he added, the venture only aims to carry startups through early stages. “At some point, The Engine will not carry you through clinical trials, but we will facilitate early on [resources for] the development, which is the most crucial aspect,” Ruiz said.

An alumna from the MIT Media Lab who founded an education-hardware startup asked if there was a place in The Engine for startups that may not turn much profit, even in the long-run. Noting that the question is very important, Ruiz said, “There’s one word that drives MIT and that we want to translate into The Engine: ‘impact.’ The Engine would indeed be interested in, say, low-cost diagnostic technologies for developing countries, that don’t generate much profit. Certainly, we’re interested in social entrepreneurs with the opportunity to create a much wider impact.”

A few commenters offered advice and opportunities for collaboration with The Engine. MIT Sloan alumnus Peter Rothstein, now president of the Northeast Clean Energy Council, which works with startup incubators across the region, said his organization could discuss collaborating with The Engine on shared equipment, mentorship, and other resources. His recommendation for administrators was to form committees designated for facilitating partnerships with investors, industry, customers, and other incubators.

“Great ideas,” Chandrakasan replied. “That’s exactly the type of things we’re thinking about.”

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A radiation-free approach to imaging molecules in the brain

Scientists hoping to get a glimpse of molecules that control brain activity have devised a new probe that allows them to image these molecules without using any chemical or radioactive labels.

Currently the gold standard approach to imaging molecules in the brain is to tag them with radioactive probes. However, these probes offer low resolution and they can’t easily be used to watch dynamic events, says Alan Jasanoff, an MIT professor of biological engineering.

Jasanoff and his colleagues have developed new sensors consisting of proteins designed to detect a particular target, which causes them to dilate blood vessels in the immediate area. This produces a change in blood flow that can be imaged with magnetic resonance imaging (MRI) or other imaging techniques.

“This is an idea that enables us to detect molecules that are in the brain at biologically low levels, and to do that with these imaging agents or contrast agents that can ultimately be used in humans,” Jasanoff says. “We can also turn them on and off, and that’s really key to trying to detect dynamic processes in the brain.”

In a paper appearing in the Dec. 2 issue of Nature Communications, Jasanoff and his colleagues used these probes to detect enzymes called proteases, but their ultimate goal is to use them to monitor the activity of neurotransmitters, which act as chemical messengers between brain cells.

The paper’s lead authors are postdoc Mitul Desai and former MIT graduate student Adrian Slusarczyk. Recent MIT graduate Ashley Chapin and postdoc Mariya Barch are also authors of the paper.

Indirect imaging

To make their probes, the researchers modified a naturally occurring peptide called calcitonin gene-related peptide (CGRP), which is active primarily during migraines or inflammation. The researchers engineered the peptides so that they are trapped within a protein cage that keeps them from interacting with blood vessels. When the peptides encounter proteases in the brain, the proteases cut the cages open and the CGRP causes nearby blood vessels to dilate. Imaging this dilation with MRI allows the researchers to determine where the proteases were detected.

“These are molecules that aren’t visualized directly, but instead produce changes in the body that can then be visualized very effectively by imaging,” Jasanoff says.

Proteases are sometimes used as biomarkers to diagnose diseases such as cancer and Alzheimer’s disease. However, Jasanoff’s lab used them in this study mainly to demonstrate the validity their approach. Now, they are working on adapting these imaging agents to monitor neurotransmitters, such as dopamine and serotonin, that are critical to cognition and processing emotions.

To do that, the researchers plan to modify the cages surrounding the CGRP so that they can be removed by interaction with a particular neurotransmitter.

“What we want to be able to do is detect levels of neurotransmitter that are 100-fold lower than what we’ve seen so far. We also want to be able to use far less of these molecular imaging agents in organisms. That’s one of the key hurdles to trying to bring this approach into people,” Jasanoff says.

Jeff Bulte, a professor of radiology and radiological science at the Johns Hopkins School of Medicine, described the technique as “original and innovative,” while adding that its safety and long-term physiological effects will require more study.

“It’s interesting that they have designed a reporter without using any kind of metal probe or contrast agent,” says Bulte, who was not involved in the research. “An MRI reporter that works really well is the holy grail in the field of molecular and cellular imaging.”

Tracking genes

Another possible application for this type of imaging is to engineer cells so that the gene for CGRP is turned on at the same time that a gene of interest is turned on. That way, scientists could use the CGRP-induced changes in blood flow to track which cells are expressing the target gene, which could help them determine the roles of those cells and genes in different behaviors. Jasanoff’s team demonstrated the feasibility of this approach by showing that implanted cells expressing CGRP could be recognized by imaging.

“Many behaviors involve turning on genes, and you could use this kind of approach to measure where and when the genes are turned on in different parts of the brain,” Jasanoff says.

His lab is also working on ways to deliver the peptides without injecting them, which would require finding a way to get them to pass through the blood-brain barrier. This barrier separates the brain from circulating blood and prevents large molecules from entering the brain.

The research was funded by the National Institutes of Health BRAIN Initiative, the MIT Simons Center for the Social Brain, and fellowships from the Boehringer Ingelheim Fonds and the Friends of the McGovern Institute.

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Computer learns to recognize sounds by watching video

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.

But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.

Sound recognition may be catching up, however, thanks to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). At the Neural Information Processing Systems conference next week, they will present a sound-recognition system that outperforms its predecessors but didn’t require hand-annotated data during training.

Instead, the researchers trained the system on video. First, existing computer vision systems that recognize scenes and objects categorized the images in the video. The new system then found correlations between those visual categories and natural sounds.

“Computer vision has gotten so good that we can transfer it to other domains,” says Carl Vondrick, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. “We’re capitalizing on the natural synchronization between vision and sound. We scale up with tons of unlabeled video to learn to understand sound.”

The researchers tested their system on two standard databases of annotated sound recordings, and it was between 13 and 15 percent more accurate than the best-performing previous system. On a data set with 10 different sound categories, it could categorize sounds with 92 percent accuracy, and on a data set with 50 categories it performed with 74 percent accuracy. On those same data sets, humans are 96 percent and 81 percent accurate, respectively.

“Even humans are ambiguous,” says Yusuf Aytar, the paper’s other first author and a postdoc in the lab of MIT professor of electrical engineering and computer science Antonio Torralba. Torralba is the final co-author on the paper.

“We did an experiment with Carl,” Aytar says. “Carl was looking at the computer monitor, and I couldn’t see it. He would play a recording and I would try to guess what it was. It turns out this is really, really hard. I could tell indoor from outdoor, basic guesses, but when it comes to the details — ‘Is it a restaurant?’ — those details are missing. Even for annotation purposes, the task is really hard.”

Complementary modalities

Because it takes far less power to collect and process audio data than it does to collect and process visual data, the researchers envision that a sound-recognition system could be used to improve the context sensitivity of mobile devices.

When coupled with GPS data, for instance, a sound-recognition system could determine that a cellphone user is in a movie theater and that the movie has started, and the phone could automatically route calls to a prerecorded outgoing message. Similarly, sound recognition could improve the situational awareness of autonomous robots.

“For instance, think of a self-driving car,” Aytar says. “There’s an ambulance coming, and the car doesn’t see it. If it hears it, it can make future predictions for the ambulance — which path it’s going to take — just purely based on sound.”

Visual language

The researchers’ machine-learning system is a neural network, so called because its architecture loosely resembles that of the human brain. A neural net consists of processing nodes that, like individual neurons, can perform only rudimentary computations but are densely interconnected. Information — say, the pixel values of a digital image — is fed to the bottom layer of nodes, which processes it and feeds it to the next layer, which processes it and feeds it to the next layer, and so on. The training process continually modifies the settings of the individual nodes, until the output of the final layer reliably performs some classification of the data — say, identifying the objects in the image.

Vondrick, Aytar, and Torralba first trained a neural net on two large, annotated sets of images: one, the ImageNet data set, contains labeled examples of images of 1,000 different objects; the other, the Places data set created by Torralba’s group, contains labeled images of 401 different scene types, such as a playground, bedroom, or conference room.

Once the network was trained, the researchers fed it the video from 26 terabytes of video data downloaded from the photo-sharing site Flickr. “It’s about 2 million unique videos,” Vondrick says. “If you were to watch all of them back to back, it would take you about two years.” Then they trained a second neural network on the audio from the same videos. The second network’s goal was to correctly predict the object and scene tags produced by the first network.

The result was a network that could interpret natural sounds in terms of image categories. For instance, it might determine that the sound of birdsong tends to be associated with forest scenes and pictures of trees, birds, birdhouses, and bird feeders.

Benchmarking

To compare the sound-recognition network’s performance to that of its predecessors, however, the researchers needed a way to translate its language of images into the familiar language of sound names. So they trained a simple machine-learning system to associate the outputs of the sound-recognition network with a set of standard sound labels.

For that, the researchers did use a database of annotated audio — one with 50 categories of sound and about 2,000 examples. Those annotations had been supplied by humans. But it’s much easier to label 2,000 examples than to label 2 million. And the MIT researchers’ network, trained first on unlabeled video, significantly outperformed all previous networks trained solely on the 2,000 labeled examples.

“With the modern machine-learning approaches, like deep learning, you have many, many trainable parameters in many layers in your neural-network system,” says Mark Plumbley, a professor of signal processing at the University of Surrey. “That normally means that you have to have many, many examples to train that on. And we have seen that sometimes there’s not enough data to be able to use a deep-learning system without some other help. Here the advantage is that they are using large amounts of other video information to train the network and then doing an additional step where they specialize the network for this particular task. That approach is very promising because it leverages this existing information from another field.”

Plumbley says that both he and colleagues at other institutions have been involved in efforts to commercialize sound recognition software for applications such as home security, where it might, for instance, respond to the sound of breaking glass. Other uses might include eldercare, to identify potentially alarming deviations from ordinary sound patterns, or to control sound pollution in urban areas. “I really think that there’s a lot of potential in the sound-recognition area,” he says.

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How the brain recognizes faces

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.

The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.

This property wasn’t built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar.

“This is not a proof that we understand what’s going on,” says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. “Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it’s strong evidence that we are on the right track.”

Indeed, the researchers’ new paper includes a mathematical proof that the particular type of machine-learning system they use, which was intended to offer what Poggio calls a “biologically plausible” model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.

Poggio, who is also a primary investigator at MIT’s McGovern Institute for Brain Research, is the senior author on a paper describing the new work, which appeared today in the journal Computational Biology. He’s joined on the paper by several other members of both the CBMM and the McGovern Institute: first author Joel Leibo, a researcher at Google DeepMind, who earned his PhD in brain and cognitive sciences from MIT with Poggio as his advisor; Qianli Liao, an MIT graduate student in electrical engineering and computer science; Fabio Anselmi, a postdoc in the IIT@MIT Laboratory for Computational and Statistical Learning, a joint venture of MIT and the Italian Institute of Technology; and Winrich Freiwald, an associate professor at the Rockefeller University.

Emergent properties

The new paper is “a nice illustration of what we want to do in [CBMM], which is this integration of machine learning and computer science on one hand, neurophysiology on the other, and aspects of human behavior,” Poggio says. “That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms.”

Poggio has long believed that the brain must produce “invariant” representations of faces and other objects, meaning representations that are indifferent to objects’ orientation in space, their distance from the viewer, or their location in the visual field. Magnetic resonance scans of human and monkey brains suggested as much, but in 2010, Freiwald published a study describing the neuroanatomy of macaque monkeys’ face-recognition mechanism in much greater detail.

Freiwald showed that information from the monkey’s optic nerves passes through a series of brain locations, each of which is less sensitive to face orientation than the last. Neurons in the first region fire only in response to particular face orientations; neurons in the final region fire regardless of the face’s orientation — an invariant representation.

But neurons in an intermediate region appear to be “mirror symmetric”: That is, they’re sensitive to the angle of face rotation without respect to direction. In the first region, one cluster of neurons will fire if a face is rotated 45 degrees to the left, and a different cluster will fire if it’s rotated 45 degrees to the right. In the final region, the same cluster of neurons will fire whether the face is rotated 30 degrees, 45 degrees, 90 degrees, or anywhere in-between. But in the intermediate region, a particular cluster of neurons will fire if the face is rotated by 45 degrees in either direction, another if it’s rotated 30 degrees, and so on.

This is the behavior that the researchers’ machine-learning system reproduced. “It was not a model that was trying to explain mirror symmetry,” Poggio says. “This model was trying to explain invariance, and in the process, there is this other property that pops out.”

Neural training

The researchers’ machine-learning system is a neural network, so called because it roughly approximates the architecture of the human brain. A neural network consists of very simple processing units, arranged into layers, that are densely connected to the processing units — or nodes — in the layers above and below. Data are fed into the bottom layer of the network, which processes them in some way and feeds them to the next layer, and so on. During training, the output of the top layer is correlated with some classification criterion — say, correctly determining whether a given image depicts a particular person.

In earlier work, Poggio’s group had trained neural networks to produce invariant representations by, essentially, memorizing a representative set of orientations for just a handful of faces, which Poggio calls “templates.” When the network was presented with a new face, it would measure its difference from these templates. That difference would be smallest for the templates whose orientations were the same as that of the new face, and the output of their associated nodes would end up dominating the information signal by the time it reached the top layer. The measured difference between the new face and the stored faces gives the new face a kind of identifying signature.

In experiments, this approach produced invariant representations: A face’s signature turned out to be roughly the same no matter its orientation. But the mechanism — memorizing templates — was not, Poggio says, biologically plausible.

So instead, the new network uses a variation on Hebb’s rule, which is often described in the neurological literature as “neurons that fire together wire together.” That means that during training, as the weights of the connections between nodes are being adjusted to produce more accurate outputs, nodes that react in concert to particular stimuli end up contributing more to the final output than nodes that react independently (or not at all).

This approach, too, ended up yielding invariant representations. But the middle layers of the network also duplicated the mirror-symmetric responses of the intermediate visual-processing regions of the primate brain.

“I think it’s a significant step forward,” says Christof Koch, president and chief scientific officer at the Allen Institute for Brain Science. “In this day and age, when everything is dominated by either big data or huge computer simulations, this shows you how a principled understanding of learning can explain some puzzling findings.”

“They’re very careful,” Koch adds. “They’re only looking at the feed-forward pathway — in other words, the first 80, 100 milliseconds. The monkey opens its eyes, and within 80 to 100 milliseconds, it can recognize a face and push a button signaling that. The question is what goes on in those 80 to 100 milliseconds, and the model that they have seems to explain that quite well.”

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Bold research visions recognized and rewarded

Since 2013, the Professor Amar G. Bose Research Grant has been supporting MIT faculty with big, bold, and unconventional research visions. In the latest round of grants, four proposals from six MIT faculty members — Angela Belcher, Betar Gallant, Amy Keating, Karl Berggren, Domitilla Del Vecchio, and Ron Weiss — were awarded from more than 100 project submissions. The researchers aim to make groundbreaking advances in areas of environmental bioremediation, cell reprogramming, new electrochemical reactions, and protein nanofabrication.

The researchers were honored at a Nov. 21 reception featuring past and current awardees, hosted by MIT President L. Rafael Reif.

Bose Grants are awarded to support innovative projects that may be unlikely to receive funding through traditional means but will offer fellows an exciting opportunity for exploration likely to benefit their fields of research. Grants provide up to $500,000 over three years for each selected project.

The grant program celebrates the legacy of the late Amar Bose, a longtime member of the MIT faculty and the founder of Bose Corporation, well known for his visionary and intellectually adventurous career. “My father would be very happy with the innovation and freedom of exploration that these grants have made possible as it was exactly what he was all about,” said his son Vanu Bose ’88, SM ’94, PhD ’99, at the reception. “The awards acknowledge the spirit of insatiable curiosity that my father embraced.”

“Through the Bose Research Grant program, which is now in its fourth year, we have a unique community of individuals synonymous with learning, teaching, exploration, and opportunity,” said President Reif. “MIT is about making a better world, and I cannot think of a better example of this than what the Bose research fellows and scholars are doing at MIT today.”

Toxin-eating yeast

“Our plan is to develop environmentally friendly, on-demand biological systems for cleaning up the environment,” says Angela Belcher, who is the James Mason Crafts Professor in biological engineering and materials science and engineering, and a member of the Koch Institute for Integrative Cancer Research. Her idea proposes using the humble yeast cell to act as a multifunctional, even programmable, bioremediation agent to clean up heavy metals and other environmental contaminants.

Belcher plans to design new yeast strains with genes from other organisms that have a natural inclination to ingest heavy metals and other toxins. By altering the yeast genes, Belcher can selectively program what genes to turn on and off. Much like commercially available yeast products, Belcher’s multifunctional yeast would be manufactured, packaged, stored, and shipped to environmentally affected areas as needed. “Our goal is to provide on-demand yeast products that can be used to clean up waste sites — from sources such as mining, manufacturing, agricultural runoff, and chemical disasters — that are easily used, recovered, and disposed of safely,” she adds.

Belcher has a successful history in redirecting natural biological processes for new purposes. Her team has repurposed natural biological agents to develop solar cells and battery technology. “It’s about natural evolution, which we are very good at, and getting biology to work with a new toolkit,” she says. “This grant allows us to take our expertise into a different direction, which is remediation.”

Reprogramming a cell’s fate

Using today’s technologies, researchers can reprogram cells of the body into stem cells capable of becoming any cell type. However, massive amounts of biochemical factors are required to force the change. And even when this reprogramming happens, less than 1 percent of the original cells actually make the full transformation to a stem cell. For more than 10 years, these issues have plagued practical applications of these induced stem cells in medicine.

Domitilla Del Vecchio, associate professor in the Department of Mechanical Engineering, and Ron Weiss, a professor in the departments of Biological Engineering and Electrical Engineering and Computer Science, propose a new technology that may offer the promise of substantially increased transformation efficiency, with smaller amounts of factors required. “Our project is about changing how the reprogramming process works,” says Del Vecchio. The pair proposes a feedback strategy whereby the cell itself adds in the needed factors at different times along the process transformative process. “We want to make a genetic circuit that can be inserted into the cell so that the cell automatically adjusts the level of needed factors,” she explains.

Adds Weiss, “Our approach is to push the cells to make the protein factors needed to both induce the change while also overriding the cells’ natural resistance which essentially fights the change.”

Harnessing the power of controlled explosions

Betar M. Gallant, the Esther and Harold E. Edgerton Career Development Assistant Professor in the Department of Mechanical Engineering, wants to build better energy systems. Her grant-winning proposal looks to create new dissolved-gas electrochemical reactions to provide substantially higher cell voltages and energy compared with current technology. A key feature of this project will be to learn to control high-potential, complex, multistep reactions.

“I want to develop tools that open up new reaction chemistries,” says Gallant. “The word ‘explosion’ conjures visions of poorly controlled and violent processes, yet electrochemistry provides us a unique handle to manipulate reactions by tuning all aspects of the reaction microenvironment. If we can learn how to design a stable and robust cell around a target reaction, we can harness that energy as electrical work.”

She is looking at designs involving a variety of gases whose theoretical reactions involve multiple electrons at high potentials. “This could be a great springboard to look at reactions that are starting to be conceived to push the limits of electrochemistry,” she says, adding that dissolved-gas reaction are largely unexplored. Looking ahead, she believes this approach could open up new pathways and new concepts in the design of chemistries, reactors, and processes for both stationary and portable power delivery.

Tool Kit for Novel Protein Nanofabrication

“We are looking for ways to combine two different fields — protein engineering and nanofabrication — to build a tool kit for arranging biomolecules in new ways on physical interfaces,” says Amy Keating, a professor of biology, explaining her project work with Karl Berggren, a professor of electrical engineering.

Keating, whose work centers on how proteins interact and function, will partner with Berggren, a nanofabrication and electrical engineering scientist, to explore new technologies for combining proteins with advanced silicon device surfaces. “We are hoping to find new ways of building very small-scale biological molecule complexes on surfaces,” she says. While living organisms naturally organize proteins and DNA into intricate pathways and complexes for a variety of functions, few engineering solutions are available now to provide that kind of design complexity. 

Berggren and Keating hope to create giant biomolecular systems with the complexity of integrated circuits by leveraging their expertise in designing custom proteins with nanofabrication techniques. “We are looking at ways of making scaffolds that we can attach more complex molecules to, like sensors, or for driving biological interactions,” says Berggren. Though they are not themselves focused on a particular application, the researchers imagine possible uses for this work in the life sciences, materials sciences, and in computing.

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A community of making

Making an academic makerspace isn’t easy. There’s no easy formula for setting up and operating safe, effective, readily available university environments where students can come together, make plans, get their hands dirty, create objects of their own design, and learn. But the benefits of doing it well, says Martin Culpepper, are almost impossible to calculate.

When Culpepper was named MIT’s maker czar in 2015, he started out by mapping out MIT’s own maker system, and, with help from a range of internal partners, launched several new programs to improve the making life of MIT students. At the same time, Culpepper says, he went on a national listening tour and visited his counterparts at other universities — sharing what he knew, and learning how MIT could work differently. 

In November, Culpepper brought the listening tour together and launched the International Symposium on Academic Makerspaces (ISAM). A three-day academic conference at MIT in which professors, machine operators, students, and administrators could come together and talk about their shared opportunities and distinct challenges, ISAM drew 340 attendees from 115 universities on every continent except for Antarctica. 

With sessions dedicated to culture and community, safety, faculty outreach, space planning, budgeting and fundraising, and campus politics, among others, presenters offered varied, sometimes diverging, opinions.

In a session dedicated to engaging alumni in campus maker activities, Marlo Kohn from Stanford University described the power of the connections that happen between students in maker and project spaces. “People who’ve been away for 10 years come back and say, ‘Oh my gosh, it smells exactly the same.’ The kind of experiences they have as makers — trying new things together, failing together — are just as powerful."

Matt Parkinson from Penn State University described the challenge of making 3-D printing available to anyone in the Penn State system — which enrolls nearly 100,000 students on 24 different campuses. “They used to be a bit like locusts,” he joked. One of Parkinson’s greatest allies turned out to be the university’s library system. They worked together to convert a large space on their main campus into a huge printing factory, with 32 full-time 3-D printers that can fulfill jobs 24 hours a day — and used the interlibrary loan system to ship printed objects to students who had submitted their jobs from other campuses. 

“This field is fast growing, so now is the time to gather people,” wrote Culpepper and his co-organzier, Vincent Wilczynski, a deputy dean of engineering at Yale University, about ISAM. "We believe there is not a right answer for how all makerpsaces should be set up and run, just the right answer for your university." 

ISAM was launched in partnership with MIT, Yale, Georgia Tech, Case Western University, Olin College of Engineering, Stanford, Carnegie Mellon University, and the University of California at Berkeley.

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Lincoln Laboratory's supercomputing system ranked most powerful in New England

The new TX-Green computing system at the MIT Lincoln Laboratory Supercomputing Center (LLSC) has been named the most powerful supercomputer in New England, 43rd most powerful in the U.S., and 106th most powerful in the world. A team of experts at TOP500 ranks the world's 500 most powerful supercomputers biannually. The systems are ranked based on a LINPACK Benchmark, which is a measure of a system's floating-point computing power, i.e., how fast a computer solves a dense system of linear equations.

Established in early 2016, the LLSC was developed to enhance computing power and accessibility for more than 1,000 researchers across the laboratory. The LLSC uses interactive supercomputing to augment the processing power of desktop systems to process large sets of sensor data, create high-fidelity simulations, and develop new algorithms. Located in Holyoke, Massachusetts, the new system is the only zero-carbon supercomputer on the TOP500 list; it uses energy from a mixture of hydroelectric, wind, solar, and nuclear sources.

In November, Dell EMC installed a new petaflop-scale system, which consists of 41,472 Intel processor cores and can compute 1,015 operations per second. Compared to LLSC's previous technology, the new system provides 6 times more processing power and 20 times more bandwidth. This technology enables research in several laboratory research areas, such as space observation, robotic vehicles, communications, cybersecurity, machine learning, sensor processing, electronic devices, bioinformatics, and air traffic control.

The LLSC mission is to address supercomputing needs, develop new supercomputing capabilities and technologies, and collaborate with MIT campus supercomputing initiatives. "The LLSC vision is to enable the brilliant scientists and engineers at Lincoln Laboratory to analyze and process enormous amounts of information with complex algorithms," says Jeremy Kepner, Lincoln Laboratory Fellow and head of the LLSC. "Our new system is one of the largest on the East Coast and is specifically focused on enabling new research in machine learning, advanced physical devices, and autonomous systems."

Because the new processors are similar to the prototypes developed at the laboratory more than two decades ago, the new petaflop system is compatible with all existing LLSC software. "We have had many years to prepare our computing system for this kind of processor," Kepner says. "This new system is essentially a plug-and-play solution."

After establishing the Supercomputing Center and one of the top systems in the world, the LLSC team will continue to upgrade and expand supercomputing at the laboratory. Says Kepner: "Our hope is that this system is the first of many such large-scale systems at Lincoln Laboratory."

de MIT News http://ift.tt/2gckDWs