Monthly Archives: April 2020

Six MIT faculty members are among more than 250 leaders from academia, business, public affairs, the humanities, and the arts elected to the American Academy of Arts and Sciences, the academy announced Thursday.

One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

Those elected from MIT this year are:

• Robert C. Armstrong, Chevron Professor in Chemical Engineering;

• Dave L. Donaldson, professor of economics;

• Catherine L. Drennan, professor of biology and chemistry;

• Ronitt Rubinfeld, professor of electrical engineering and computer science;

• Joshua B. Tenenbaum, professor of brain and cognitive sciences; and

• Craig Steven Wilder, Barton L. Weller Professor of History.

“The members of the class of 2020 have excelled in laboratories and lecture halls, they have amazed on concert stages and in surgical suites, and they have led in board rooms and courtrooms,” said academy President David W. Oxtoby. “With today’s election announcement, these new members are united by a place in history and by an opportunity to shape the future through the academy’s work to advance the public good.”

Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners.

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As cases of Covid-19 continue to climb in parts of the United States, the number of people seeking treatment is threatening to overwhelm many hospitals, forcing some facilities to ration their care and reserve ventilators, hospital beds, and other limited medical resources for the sickest patients. 

Having a handle on local hospitals’ capacity and resource availability could help balance the load of Covid-19 patients requiring hospitalization across a region, for instance allowing an EMT to send a patient to a facility where they are more likely to be treated quickly. But many states lack real-time data on their current capacity to treat Covid-19 patients. 

A group of researchers in MIT’s Computer Science and Intelligence Laboratory (CSAIL), working with the MIT spinoff Mobi Systems, are aiming to help level demand across the entire health care network by providing real-time updates of hospital resources, which they hope will help patients, EMTs, and physicians quickly decide which facility is best equipped to handle a new patient at any given time. 

The team has developed a web app which is now publicly accessible at: https://Covid19hospitalstatus.com. The interface allows users such as patients, nurses, and doctors to report a hospital’s current status in a number of metrics, from the average wait time (something that a patient may get a sense for as they spend time in a waiting room), to the number of ventilators and ICU beds, which doctors and nurses may be able to approximate.

EMTS can use the app as a map, zooming in by state, county, or city to quickly gauge hospital capacity, and decide which nearby hospitals have available beds where they can send a patient requiring hospitalization. The app can also generate a list of hospitals, prioritized by availability, time of travel, and most recently updated data. 

“We want to flatten the Covid curve by physical distancing over the course of months,” says MIT graduate Anna Jaffe ’07, CEO of Mobi Systems. “But there’s another curve to flatten, which is this real-time challenge of getting the right patient to the right hospital, in the right moment, to level the load on hospitals and health care workers.”

“Do something”

As the pandemic began to unfold around the world, Jaffe was intrigued by the results of a short hackathon that one Mobi member, Julius Pätzold, recently attended in Germany. The weekend challenge, sponsored by the German government, included a problem to match supply and demand, for instance in a hospital facing a surge in patient visits. 

His team mapped the German hospital infrastructure, including the status of individual hospitals’ capacity, then simulated dispatching patients to hospitals according to a hospital’s capacity, its relative location to a patient, and a patient’s medical needs. The real-time maps developed over this short time suggested such tools would have a positive impact on a patient’s quality of care, specifically in decreasing death rates.

“That intersected with my feeling that I think everyone wants to do something around Covid-19 in response to the current crisis, and not just be cooped up in our respective homes,” says Jaffe, whose company, Mobi Systems, develops tools for large-scale network optimization problems surrounding mobility and hospitality. 

Mobi originally grew out of CSAIL’s Model-based Embedded Robotic Systems group, led by MIT Professor Brian Williams, whose work involves developing autonomous planning tools to help individuals make complex, real-time decisions in the face of uncertainty and risk. 

Jaffe reached out to Williams to help develop a web-based reporting tool for hospitals, to similarly help patients and medical professionals make critical, real-time decisions of where best to send a patient, based on resource availability. 

“Our question was, how can the resources statewide or nationwide be used most effectively, in order to keep the most people healthy,” Williams says. “And for the individual, which hospital will meet their needs, and how do they get there. That’s the exercise we’re tackling here.”

Crowd power

The team’s app is heavily dependent on crowdsourced data, and the willingness of patients and medical professionals to report on various metrics, from a hospital’s current wait time to the approximate number of ICU beds and ventilators available. 

“The reporting options right now are very specific,” Jaffe says. “But what we really want to know is, can your hospital accept a patient right now?” 

A user can enter their role — patient, nurse, or physician — then report on, for instance, a hospital’s average wait time. With a sliding scale, they can rate their confidence in their report before submitting it. 

But what if those users are reporting false or inaccurate data, whether intentionally or not? 

Williams says in order to guard against such uncertainty, the team takes a probabilistic approach. For instance, the app assumes that one user’s reporting of a hospital’s status is one of low confidence, which is initially not weighed heavily in the overall estimation for that metric. They can then incorporate this one data point into all the other reports they’ve received for that metric. If most of those reports have also been rated with low confidence, but report the same result, that estimate, such as of wait time, is automatically weighed more heavily, and therefore rated at a higher confidence overall.  

Additionally, he says if the app receives reports from more trusted sources — for instance, if hospitals make in-house, aggregated data available to the app — those sources would “swamp out” or take higher priority over low-confidence reports of the same metric. 

The team is testing the app with just such a trustworthy dataset, from the state of Pennsylvania, which for the last several years has had a system in place for hospitals to report resource availability, that is updated at least twice a day. The team has used data from the last week to track Covid-19 visits across the state’s hospital system.

“In this data, you can see that not all hospitals are overrun — there are clear differences in availability,” says MIT graduate Peng Yu ’SM 13, ’PhD 17, chief technology officer at Mobi, highlighting the potential for distributing patients across a region’s hospitals, to balance resources across a hospital network. 

However, most states lack such aggregated, updated information. In most other states, for instance, EMTs either have a handful of default facilities where they typically send patients, or they have to call around to surrounding hospitals to check availability. 

“It’s really about word of mouth — who do you know, and who do you call up,” says Williams, whose nephew is an EMT who has worked in regions with varying decision-making practices. “We’re trying to aggregate that information, to make these recommendations much faster.

The team is now reaching out to thousands of medical professionals to test-drive the reporting tool, in hopes of boosting the crowdsourcing component for the app, which is now available on any internet-enabled device. To address the pandemic, the team believes that data need to be made available at a faster rate than the virus’ spread. Their hope is that states will follow in Pennsylvania’s footsteps and, for instance, mandate that hospitals report resource data, and provide reporting tools such as the new app to doctors and EMTs. 

“This project is very much for the people, by the people, and will be kept open and free,” Williams says.  

“Unfortunately, it doesn’t feel like this is a flash pandemic,” Jaffe says. “Even in a recovery period, hospitals will have to resume normal care, concurrently with treating Covid-19 over time. Our app may help load balance in that way as well, so hospitals can more effectively predict how many floors they need to quarantine for Covid-19, so that the rest of the hospital can go back to things like having families around a mother giving birth. We aim to really understand how to bring things back to a more normal operational status, while still handling the crisis.

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Marija Ilic — a senior research scientist at the Laboratory for Information and Decision Systems, affiliate of the MIT Institute for Data, Systems, and Society, senior staff in MIT Lincoln Laboratory’s Energy Systems Group, and Carnegie Mellon University professor emerita — is a researcher on a mission: making electric energy systems future-ready.

Since the earliest days of streetcars and public utilities, electric power systems have had a fairly standard structure: for a given area, a few large generation plants produce and distribute electricity to customers. It is a one-directional structure, with the energy plants being the only source of power for many end users.

Today, however, electricity can be generated from many and varied sources — and move through the system in multiple directions. An electric power system may include stands of huge turbines capturing wild ocean winds, for instance. There might be solar farms of a hundred megawatts or more, or houses with solar panels on their roofs that some days make more electricity than occupants need, some days much less. And there are electric cars, their batteries hoarding stored energy overnight. Users may draw electricity from one source or another, or feed it back into the system, all at the same time. Add to that the trend toward open electricity markets, where end users like households can pick and choose the electricity services they buy depending on their needs. How should systems operators integrate all these while keeping the grid stable and ensuring power gets to where it is needed?

To explore this question, Ilic has developed a new way to model complex power systems.

Electric power systems, even traditional ones, are complex and heterogeneous to begin with. They cover wide geographical areas and have legal and political barriers to contend with, such as state borders and energy policies. In addition, all electric power systems have inherent physical limitations. For instance, power does not flow in a set path in an electric grid, but rather along all possible paths connecting supply to demand. To maintain grid stability and quality of service, then, the system must control for the impact of interconnections: a change in supply and demand at one point in a system changes supply and demand for the other points in the system. This means there is much more complexity to manage as new sources of energy (more interconnections) with sometimes unpredictable supply (such as wind or solar power) come into play. Ultimately, however, to maintain stability and quality of service, and to balance supply and demand within the system, it comes down to a relatively simple concept: the power consumed and the rate at which it is consumed (plus whatever is lost along the way), must always equal the power produced and the rate at which it is produced.

Using this simpler concept to manage the complexities and limitations of electric power systems, Ilic is taking a non-traditional approach: She models the systems using information about energy, power, and ramp rate (the rate at which power can increase over time) for each part of the system — distributing decision-making calculations into smaller operational chunks. Doing this streamlines the model but retains information about the system’s physical and temporal structure. “That’s the minimal information you need to exchange. It’s simple and technology-agnostic, but we don’t teach systems that way.”

She believes regulatory organizations such as the Federal Energy Regulatory Commission and North American Energy Reliability Corporation should have standard protocols for such information exchanges, just as internet protocols govern how data is exchanged on the internet. “If you were to [use a standard set of] specifications like: what is your capacity, how much does it vary over time, how much energy do you need and within what power range — the system operator could integrate different sources in a much simpler way than we are doing now.” 

Another important aspect of Ilic’s work is that her models lend themselves to controlling the system with a layer of sensor and communications technologies. This uses a framework she developed called Dynamic Monitoring and Decision Systems framework, or DyMonDS. The data-enabled decision-making concept has been tested using real data from Portugal’s Azores Islands, and since applied to real-world challenges. After so many years it appears that her new modeling approach fittingly supports DyMonDS design, including systematic use of many theoretical concepts used by the LIDS community in their research.

One such challenge included work on Puerto Rico’s power grid. Ilic was the technical lead on a Lincoln Laboratory project on designing future architectures and software to make Puerto Rico’s electric power grid more resilient without adding much more production capacity or cost. Typically, a power grid’s generation capacity is scheduled in a simple, brute-force way, based on weather forecasts and the hottest and coldest days of the year, that doesn’t respond sensitively to real-time needs. Making such a system more resilient would mean spending a lot more on generation and transmission and distribution capacity, whereas a more dynamic system that integrates distributed microgrids could tame the cost, Ilic says: “What we are trying to do is to have systematic frameworks for embedding intelligence into small microgrids serving communities, and having them interact with large-scale power grids. People are realizing that you can make many small microgrids to serve communities rather than relying only on large scale electrical power generation.”

Although this is one of Ilic’s most recent projects, her work on DyMonDS can be traced back four decades, to when she was a student at the University of Belgrade in the former country of Yugoslavia, which sent her to the United States to learn how to use computers to prevent blackouts.

She ended up at Washington University in St. Louis, Missouri, studying with applied mathematician John Zaborszky, a legend in the field who was originally chief engineer of Budapest’s municipal power system before moving to the United States. (“The legend goes that in the morning he would teach courses, and in the afternoon he would go and operate Hungarian power system protection by hand.”) Under Zaborszky, a systems and control expert, Ilic learned to think in abstract terms as well as in terms of physical power systems and technologies. She became fascinated by the question of how to model, simulate, monitor, and control power systems — and that’s where she’s been ever since. (Although, she admits as she uncoils to her full height from behind her desk, her first love was actually playing basketball.)

Ilic first arrived at MIT in 1987 to work with the late professor Fred Schweppe on connecting electricity technologies with electricity markets. She stayed on as a senior research scientist until 2002, when she moved to Carnegie Mellon University (CMU) to lead the multidisciplinary Electric Energy Systems Group there. In 2018, after her consulting work for Lincoln Lab ramped up, she retired from CMU to move back to the familiar environs of Cambridge, Massachusetts. CMU’s loss has been MIT’s gain: In fall 2019, Ilic taught a course in modeling, simulation, and control of electric energy systems, applying her work on streamlined models that use pared-down information.

Addressing the evolving needs of electric power systems has not been a “hot” topic, historically. Traditional power systems are often seen by the academic community as legacy technology with no fundamentally new developments. And yet when new software and systems are developed to help integrate distributed energy generation and storage, commercial systems operators regard them as untested and disruptive. “I’ve always been a bit on the sidelines from mainstream power and electrical engineering because I’m interested in some of these things,” she remarks.

However, Ilic’s work is becoming increasingly urgent. Much of today’s power system is physically very old and will need to be retired and replaced over the next decade. This presents an opportunity for innovation: the next generation of electric energy systems could be built to integrate renewable and distributed energy resources at scale — addressing the pressing challenge of climate change and making way for further progress.

“That’s why I’m still working, even though I should be retired.” She smiles. “It supports the evolution of the system to something better.”

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Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. 

Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. That’s equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing.

This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. 

MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved — in some cases, down to low triple digits. 

The researchers’ system, which they call a once-for-all network, trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining. This dramatically reduces the energy usually required to train each specialized neural network for new platforms — which can include billions of internet of things (IoT) devices. Using the system to train a computer-vision model, they estimated that the process required roughly 1/1,300 the carbon emissions compared to today’s state-of-the-art neural architecture search approaches, while reducing the inference time by 1.5-2.6 times. 

“The aim is smaller, greener neural networks,” says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. “Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.”

The work was carried out on Satori, an efficient computing cluster donated to MIT by IBM that is capable of performing 2 quadrillion calculations per second. The paper is being presented next week at the International Conference on Learning Representations. Joining Han on the paper are four undergraduate and graduate students from EECS, MIT-IBM Watson AI Lab, and Shanghai Jiao Tong University. 

Creating a “once-for-all” network

The researchers built the system on a recent AI advance called AutoML (for automatic machine learning), which eliminates manual network design. Neural networks automatically search massive design spaces for network architectures tailored, for instance, to specific hardware platforms. But there’s still a training efficiency issue: Each model has to be selected then trained from scratch for its platform architecture. 

“How do we train all those networks efficiently for such a broad spectrum of devices — from a $10 IoT device to a $600 smartphone? Given the diversity of IoT devices, the computation cost of neural architecture search will explode,” Han says.   

The researchers invented an AutoML system that trains only a single, large “once-for-all” (OFA) network that serves as a “mother” network, nesting an extremely high number of subnetworks that are sparsely activated from the mother network. OFA shares all its learned weights with all subnetworks — meaning they come essentially pretrained. Thus, each subnetwork can operate independently at inference time without retraining. 

The team trained an OFA convolutional neural network (CNN) — commonly used for image-processing tasks — with versatile architectural configurations, including different numbers of layers and “neurons,” diverse filter sizes, and diverse input image resolutions. Given a specific platform, the system uses the OFA as the search space to find the best subnetwork based on the accuracy and latency tradeoffs that correlate to the platform’s power and speed limits. For an IoT device, for instance, the system will find a smaller subnetwork. For smartphones, it will select larger subnetworks, but with different structures depending on individual battery lifetimes and computation resources. OFA decouples model training and architecture search, and spreads the one-time training cost across many inference hardware platforms and resource constraints. 

This relies on a “progressive shrinking” algorithm that efficiently trains the OFA network to support all of the subnetworks simultaneously. It starts with training the full network with the maximum size, then progressively shrinks the sizes of the network to include smaller subnetworks. Smaller subnetworks are trained with the help of large subnetworks to grow together. In the end, all of the subnetworks with different sizes are supported, allowing fast specialization based on the platform’s power and speed limits. It supports many hardware devices with zero training cost when adding a new device.
 
In total, one OFA, the researchers found, can comprise more than 10 quintillion — that’s a 1 followed by 19 zeroes — architectural settings, covering probably all platforms ever needed. But training the OFA and searching it ends up being far more efficient than spending hours training each neural network per platform. Moreover, OFA does not compromise accuracy or inference efficiency. Instead, it provides state-of-the-art ImageNet accuracy on mobile devices. And, compared with state-of-the-art industry-leading CNN models , the researchers say OFA provides 1.5-2.6 times speedup, with superior accuracy. 
    
“That’s a breakthrough technology,” Han says. “If we want to run powerful AI on consumer devices, we have to figure out how to shrink AI down to size.”

“The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices,” says Chuang Gan, a researcher at the MIT-IBM Watson AI Lab and co-author of the paper.

“If rapid progress in AI is to continue, we need to reduce its environmental impact,” says John Cohn, an IBM fellow and member of the MIT-IBM Watson AI Lab. “The upside of developing methods to make AI models smaller and more efficient is that the models may also perform better.”

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