News and Research
Catherine Iacobo named industry co-director for MIT Leaders for Global Operations

Catherine Iacobo named industry co-director for MIT Leaders for Global Operations

Cathy Iacobo, a lecturer at the MIT Sloan School of Management, has been named the new industry co-director for the MIT Leaders for Global Operations (LGO) program. Read more

Lgo

LGO Best Thesis 2019 for Big Data Analysis at Amgen, Inc.

After the official MIT commencement ceremonies, Thomas Roemer, LGO’s executive director, announced the best thesis winner at LGO’s annual post-graduation celebration. This year’s winner was Maria Emilia Lopez Marino (Emi), who developed a predictive framework to evaluate and assess the impact of raw material attributes on the manufacturing process at Amgen. Thesis readers described Marino’s project as an “extremely well-written thesis.  Excellent coverage of not only the project, but also the industry as a whole.”

Applying MIT knowledge in the real world

LGO Best Thesis Award 2019
Maria Emilia Lopez Marino won the 2019 LGO best thesis award for her work developing predictive framework using machine learning techniques for manufacturing at Amgen

Marino, who earned her MBA and SM in Civil and Environmental Engineering, completed her six-month LGO internship project at Amgen, Inc. For her project, Marino developed a new predictive framework through machine learning techniques to assess the impact of raw material variability on the performance of several commercial processes of biologics manufacturing.  Finding this solution represents a competitive advantage for biopharmaceutical leaders. The results from her analysis showed an 80% average accuracy on predictions for new data. Additionally, the framework she developed is the starting point of a new methodology towards material variability understanding in the manufacturing process for the pharmaceutical industry.

Each year, the theses are nominated by faculty advisors and then reviewed by LGO alumni readers to determine the winner. Thesis advisor and Professor Roy Welsch stated Emi “understood variation both in a statistical sense and in manufacturing in the biopharmaceutical industry and left behind highly accurate and interpretable models in a form that others can use and expand. We hope she will share her experiences with us in the future at LGO alumni reunions and on DPT visits.”

Marino, who earned her undergraduate degree Chemical Engineering from the National University of Mar Del Plata in Argentina, has accepted a job offer with Amgen in Puerto Rico.

 

June 11, 2019 | More

The tenured engineers of 2019

The School of Engineering has announced that 17 members of its faculty have been granted tenure by MIT, including 3 LGO advisors: Saurabh Amin, Kerri Cahoy, and Julie Shah.

“The tenured faculty in this year’s cohort are a true inspiration,” said Anantha Chandrakasan, dean of the School of Engineering. “They have shown exceptional dedication to research and teaching, and their innovative work has greatly advanced their fields.”

This year’s newly tenured associate professors are:

Antoine Allanore, in the Department of Materials Science and Engineering, develops more sustainable technologies and strategies for mining, metal extraction, and manufacturing, including novel methods of fertilizer production.

Saurabh Amin, in the Department of Civil and Environmental Engineering, focuses on the design and implementation of network inspection and control algorithms for improving the resilience of large-scale critical infrastructures, such as transportation systems and water and energy distribution networks, against cyber-physical security attacks and natural events.

Emilio Baglietto, in the Department of Nuclear Science and Engineering, uses computational modeling to characterize and predict the underlying heat-transfer processes in nuclear reactors, including turbulence modeling, unsteady flow phenomena, multiphase flow, and boiling.

Paul Blainey, the Karl Van Tassel (1925) Career Development Professor in the Department of Biological Engineering, integrates microfluidic, optical, and molecular tools for application in biology and medicine across a range of scales.

Kerri Cahoy, the Rockwell International Career Development Professor in the Department of Aeronautics and Astronautics, develops nanosatellites that demonstrate weather sensing using microwave radiometers and GPS radio occultation receivers, high data-rate laser communications with precision time transfer, and active optical imaging systems using MEMS deformable mirrors for exoplanet exploration applications.

Juejun Hu, in the Department of Materials Science and Engineering, focuses on novel materials and devices to exploit interactions of light with matter, with applications in on-chip sensing and spectroscopy, flexible and polymer photonics, and optics for solar energy.

Sertac Karaman, the Class of 1948 Career Development Professor in the Department of Aeronautics and Astronautics, studies robotics, control theory, and the application of probability theory, stochastic processes, and optimization for cyber-physical systems such as driverless cars and drones.

R. Scott Kemp, the Class of 1943 Career Development Professor in the Department of Nuclear Science and Engineering, combines physics, politics, and history to identify options for addressing nuclear weapons and energy. He investigates technical threats to nuclear-deterrence stability and the information theory of treaty verification; he is also developing technical tools for reconstructing the histories of secret nuclear-weapon programs.

Aleksander Mądry, in the Department of Electrical Engineering and Computer Science, investigates topics ranging from developing new algorithms using continuous optimization, to combining theoretical and empirical insights, to building a more principled and thorough understanding of key machine learning tools. A major theme of his research is rethinking machine learning from the perspective of security and robustness.

Frances Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering, performs research on nanostructures using transmission electron microscopes that allow researchers to see, in real-time, how structures form and develop in response to changes in temperature, environment, and other variables. Understanding crystal growth at the nanoscale is helpful in creating precisely controlled materials for applications in microelectronics and energy conversion and storage.

Daniel Sanchez, in the Department of Electrical Engineering and Computer Science, works on computer architecture and computer systems, with an emphasis on large-scale multi-core processors, scalable and efficient memory hierarchies, architectures with quality-of-service guarantees, and scalable runtimes and schedulers.

Themistoklis Sapsis, the Doherty Career Development Professor in the Department of Mechanical Engineering, develops analytical, computational, and data-driven methods for the probabilistic prediction and quantification of extreme events in high-dimensional nonlinear systems such as turbulent fluid flows and nonlinear mechanical systems.

Julie Shah, the Boeing Career Development Professor in the Department of Aeronautics and Astronautics, develops innovative computational models and algorithms expanding the use of human cognitive models for artificial intelligence. Her research has produced novel forms of human-machine teaming in manufacturing assembly lines, healthcare applications, transportation, and defense.

Hadley Sikes, the Esther and Harold E. Edgerton Career Development Professor in the Department of Chemical Engineering, employs biomolecular engineering and knowledge of reaction networks to detect epigenetic modifications that can guide cancer treatment, induce oxidant-specific perturbations in tumors for therapeutic benefit, and improve signaling reactions and assay formats used in medical diagnostics.

William Tisdale, the ARCO Career Development Professor in the Department of Chemical Engineering, works on energy transport in nanomaterials, nonlinear spectroscopy, and spectroscopic imaging to better understand and control the mechanisms by which excitons, free charges, heat, and reactive chemical species are converted to more useful forms of energy, and on leveraging this understanding to guide materials design and process optimization.

Virginia Vassilevska Williams, the Steven and Renee Finn Career Development Professor in the Department of Electrical Engineering and Computer Science, applies combinatorial and graph theoretic tools to develop efficient algorithms for matrix multiplication, shortest paths, and a variety of other fundamental problems. Her recent research is centered on proving tight relationships between seemingly different computational problems. She is also interested in computational social choice issues, such as making elections computationally resistant to manipulation.

Amos Winter, the Tata Career Development Professor in the Department of Mechanical Engineering, focuses on connections between mechanical design theory and user-centered product design to create simple, elegant technological solutions for applications in medical devices, water purification, agriculture, automotive, and other technologies used in highly constrained environments.

June 7, 2019 | More

MIT team places second in 2019 NASA BIG Idea Challenge

An MIT student team, including LGO ’20 Hans Nowak, took second place for its design of a multilevel greenhouse to be used on Mars in NASA’s 2019 Breakthrough, Innovative and Game-changing (BIG) Idea Challenge last month.

Each year, NASA holds the BIG Idea competition in its search for innovative and futuristic ideas. This year’s challenge invited universities across the United States to submit designs for a sustainable, cost-effective, and efficient method of supplying food to astronauts during future crewed explorations of Mars. Dartmouth College was awarded first place in this year’s closely contested challenge.

“This was definitely a full-team success,” says team leader Eric Hinterman, a graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). The team had contributions from 10 undergraduates and graduate students from across MIT departments. Support and assistance were provided by four architects and designers in Italy. This project was completely voluntary; all 14 contributors share a similar passion for space exploration and enjoyed working on the challenge in their spare time.

The MIT team dubbed its design “BEAVER” (Biosphere Engineered Architecture for Viable Extraterrestrial Residence). “We designed our greenhouse to provide 100 percent of the food requirements for four active astronauts every day for two years,” explains Hinterman.

The ecologists and agriculture specialists on the MIT team identified eight types of crops to provide the calories, protein, carbohydrates, and oils and fats that astronauts would need; these included potatoes, rice, wheat, oats, and peanuts. The flexible menu suggested substitutes, depending on astronauts’ specific dietary requirements.

“Most space systems are metallic and very robotic,” Hinterman says. “It was fun working on something involving plants.”

Parameters provided by NASA — a power budget, dimensions necessary for transporting by rocket, the capacity to provide adequate sustenance — drove the shape and the overall design of the greenhouse.

Last October, the team held an initial brainstorming session and pitched project ideas. The iterative process continued until they reached their final design: a cylindrical growing space 11.2 meters in diameter and 13.4 meters tall after deployment.

An innovative design

The greenhouse would be packaged inside a rocket bound for Mars and, after landing, a waiting robot would move it to its site. Programmed with folding mechanisms, it would then expand horizontally and vertically and begin forming an ice shield around its exterior to protect plants and humans from the intense radiation on the Martian surface.

Two years later, when Earth and Mars orbits were again in optimal alignment for launching and landing, a crew would arrive on Mars, where they would complete the greenhouse setup and begin growing crops. “About every two years, the crew would leave and a new crew of four would arrive and continue to use the greenhouse,” explains Hinterman.

To maximize space, BEAVER employs a large spiral that moves around a central core within the cylinder. Seedlings are planted at the top and flow down the spiral as they grow. By the time they reach the bottom, the plants are ready for harvesting, and the crew enters at the ground floor to reap the potatoes and peanuts and grains. The planting trays are then moved to the top of the spiral, and the process begins again.

“A lot of engineering went into the spiral,” says Hinterman. “Most of it is done without any moving parts or mechanical systems, which makes it ideal for space applications. You don’t want a lot of moving parts or things that can break.”

The human factor

“One of the big issues with sending humans into space is that they will be confined to seeing the same people every day for a couple of years,” Hinterman explains. “They’ll be living in an enclosed environment with very little personal space.”

The greenhouse provides a pleasant area to ensure astronauts’ psychological well-being. On the top floor, just above the spiral, a windowed “mental relaxation area” overlooks the greenery. The ice shield admits natural light, and the crew can lounge on couches and enjoy the view of the Mars landscape. And rather than running pipes from the water tank at the top level down to the crops, Hinterman and his team designed a cascading waterfall at

May 24, 2019 | More

MIT team places first in U.S. Air Force virtual reality competition

When the United States Air Force put out a call for submissions for its first-ever Visionary Q-Prize competition in October 2018, a six-person team of 3 MIT students and 3 LGO alumni took up the challenge. Last month, they emerged as a first-place winner for their prototype of a virtual reality tool they called CoSMIC (Command, Sensing, and Mapping Information Center).

The challenge was hosted by the Air Force Research Labs Space Vehicles Directorate and the Wright Brothers Institute to encourage nontraditional sources with innovative products and ideas to engage with military customers to develop solutions for safe and secure operations in space.

April 12, 2019 | More

MIT graduate engineering, business programs earn top rankings from U.S. News for 2020

Graduate engineering program is No. 1 in the nation; MIT Sloan is No. 3.

MIT’s graduate program in engineering has again earned a No. 1 spot in U.S. News and Word Report’s annual rankings, a place it has held since 1990, when the magazine first ranked such programs.

The MIT Sloan School of Management also placed highly, occupying the No. 3 spot for the best graduate business program, which it shares with Harvard University and the University of Chicago.

March 22, 2019 | More

Leading to Green

More efficient or more sustainable? Janelle Heslop, LGO ’19, helps businesses achieve both. Heslop is no shrinking violet. She found a voice for herself and the environment when she was in middle school, volunteering as a junior docent for the Hudson River Museum. “I was a 12-year-old giving tours, preaching to people: we’ve got to protect our resources,” Heslop says. “At a very early age, I learned to have a perspective, and assert it.”

February 22, 2019 | More

Winners of inaugural AUS New Venture Challenge Announced

Danielle Castley, Dartmouth PhD Candidate, Jordan Landis, LGO ’20, and Ian McDonald, PhD, of Neutroelectric LLC won the inaugural American University of Sharjah New Ventures Challenge, winning the Chancellor’s Prize of $50,000 with radiation shielding materials  developed to improve safety margins and reduce costs for both nuclear power plant operations and transport and storage of spent nuclear waste.

February 20, 2019 | More

Tackling greenhouse gases

While a number of other MIT researchers are developing capture and reuse technologies to minimize greenhouse gas emissions, Professor Timothy Gutowski, frequent LGO advisor, is approaching climate change from a completely different angle: the economics of manufacturing.

Gutowski understands manufacturing. He has worked on both the industry and academic side of manufacturing, was the director of MIT’s Laboratory for Manufacturing and Productivity for a decade, and currently leads the Environmentally Benign Manufacturing research group at MIT. His primary research focus is assessing the environmental impact of manufacturing.

January 11, 2019 | More

Department of Mechanical Engineering announces new leadership team

Pierre Lermusiaux, LGO thesis advisor and professor of mechanical engineering and ocean science and engineering will join on the MechE department’s leadership team. Prof Lermusiaux will serve as associate department head for operations.

Evelyn Wang, the Gail E. Kendall Professor, who began her role as head of MIT’s Department of Mechanical Engineering (MechE) on July 1, has announced that Pierre Lermusiaux, professor of mechanical engineering and ocean science and engineering, and Rohit Karnik, associate professor of mechanical engineering, will join her on the department’s leadership team. Lermusiaux will serve as associate department head for operations and Karnik will be the associate department head for education.

“I am delighted to welcome Pierre and Rohit to the department’s leadership team,” says Wang. “They have both made substantial contributions to the department and are well-suited to ensure that it continues to thrive.”

Pierre Lermusiaux, associate department head for operations

Pierre Lermusiaux has been instrumental in developing MechE’s strategic plan over the past several years. In 2015, with Evelyn Wang, he was co-chair of the mechanical engineering strategic planning committee. They were responsible for interviewing individuals across the MechE community, determining priority “grand challenge” research areas, investigating new educational models, and developing mechanisms to enhance community and departmental operations. The resulting strategic plan will inform the future of MechE for years to come.

“Pierre is an asset to our department,” adds Wang. “I look forward to working with him to lead our department toward new research frontiers and cutting-edge discoveries.”

Lermusiaux joined MIT as associate professor in 2007 after serving as a research associate at Harvard University, where he also received his PhD. He is an internationally recognized thought leader at the intersection of ocean modeling and observing. He has developed new uncertainty quantification and data assimilation methods. His research has improved real-time data-driven ocean modeling and has had important implications for marine industries, fisheries, energy, security, and our understanding of human impact on the ocean’s health.

Lermusiaux’s talent as an educator has been recognized with the Ruth and Joel Spira Award for Teaching Excellence. He has been the chair of the graduate admissions committee since 2014. He has served on many MechE and institute committees and is also active in MIT-Woods Hole Oceanographic Institution Joint Program committees.

“Working for the department, from our graduate admission to the strategic planning with Evelyn, has been a pleasure,” says Lermusiaux. “I am thrilled to be continuing such contributions as associate department head for research and operations. I look forward to developing and implementing strategies and initiatives that help our department grow and thrive.”

Lermusiaux succeeds Evelyn Wang, who previously served as associate department head for operations under the former department head Gang Chen.

Rohit Karnik, associate department head for education

Over the past two years, Rohit Karnik has taken an active role in shaping the educational experience at MechE. As the undergraduate officer, he has overseen the operations of the department’s undergraduate office and chaired the undergraduate programs committee. This position has afforded Karnik the opportunity to evaluate and refine the department’s course offerings each year and work closely with undergraduate students to provide the best education.

“Rohit is a model citizen and has provided dedicated service to our department,” says Wang. “I look forward to working with him to create new education initiatives and continue to provide a world-class education for our students.”

Prior to joining MIT as a postdoc in 2006, Karnik received his PhD from the University of California at Berkeley. In 2006, he joined the faculty as an assistant professor of mechanical engineering. He is recognized as a leader in the field of micro-and-nanofluidics and has made a number of seminal contributions in the fundamental understanding of nanoscale fluid transport. He has been recognized by an National Science Foundation CAREER Award and a Department of Energy Early Career Award.

Karnik’s dedication to his students have been recognized by the Keenan Award for Innovation in Education and the Ruth and Joel Spira Award for Teaching Excellence. He has also served on the graduate admissions committee and various faculty search committees.

“It is a tremendous honor and responsibility to take this position in the top mechanical engineering department in the world,” says Karnik. “I will strive to ensure that we maintain excellence in mechanical engineering education and adapt to the changing times to offer strong and comprehensive degree programs and the best possible experience for our students.”

Karnik succeeds Professor John Brisson who previously served as associate department head for education.

August 3, 2018 | More

Boeing will be Kendall Square Initiative’s first major tenant

Boeing, the world’s largest aerospace company, and LGO Partner Company has announced they will be part MIT’s Kendall Square Initiative. The company has agreed to lease approximately 100,000 square feet at MIT’s building to be developed at 314 Main St., in the heart of Kendall Square in Cambridge.

MIT’s Kendall Square Initiative, includes six sites slated for housing, retail, research and development, office, academic, and open space uses. The building at 314 Main St. (“Site 5” on the map above) is located between the MBTA Red Line station and the Kendall Hotel. Boeing is expected to occupy its new space by the end of 2020.

“Our focus on advancing the Kendall Square innovation ecosystem includes a deep and historic understanding of what we call the ‘power of proximity’ to address pressing global challenges,” MIT Executive Vice President and Treasurer Israel Ruiz says. “MIT’s president, L. Rafael Reif, has made clear his objective of reducing the time it takes to move ideas from the classroom and lab out to the market. The power of proximity is a dynamic that propels this concept forward: Just as pharmaceutical, biotech, and tech sector scientists in Kendall Square work closely with their nearby MIT colleagues, Boeing and MIT researchers will be able to strengthen their collaborative ties to further chart the course of the aerospace industry.”

Boeing was founded in 1916 — the same year that MIT moved to Cambridge — and marked its recent centennial in a spirit similar to the Institute’s 100-year celebration in 2016, with special events, community activities, and commemorations. That period also represents a century-long research relationship between Boeing and MIT that has helped to advance the global aerospace industry.

Some of Boeing’s founding leaders, as well as engineers, executives, Boeing Technical Fellows, and student interns, are MIT alumni.

Earlier this year, Boeing announced that it will serve as the lead donor for MIT’s $18 million project to replace its 80-year-old Wright Brothers Wind Tunnel. This pledge will help to create, at MIT, the world’s most advanced academic wind tunnel.

In 2017, Boeing acquired MIT spinout Aurora Flight Sciences, which develops advanced aerospace platforms and autonomous systems. Its primary research and development center is located at 90 Broadway in Kendall Square. In the new facility at 314 Main St., Boeing will establish the Aerospace and Autonomy Center, which will focus on advancing enabling technologies for autonomous aircraft.

“Boeing is leading the development of new autonomous vehicles and future transportation systems that will bring flight closer to home,” says Greg Hyslop, Boeing chief technology officer. “By investing in this new research facility, we are creating a hub where our engineers can collaborate with other Boeing engineers and research partners around the world and leverage the Cambridge innovation ecosystem.”

“It’s fitting that Boeing will join the Kendall/MIT innovation family,” MIT Provost Martin Schmidt says. “Our research interests have been intertwined for over 100 years, and we’ve worked together to advance world-changing aerospace technologies and systems. MIT’s Department of Aeronautics and Astronautics is the oldest program of its kind in the United States, and excels at its mission of developing new air transportation concepts, autonomous systems, and small satellites through an intensive focus on cutting-edge education and research. Boeing’s presence will create an unprecedented opportunity for new synergies in this industry.”

The current appearance of the 314 Main St. site belies its future active presence in Kendall Square. The building’s foundation and basement level — which will house loading infrastructure, storage and mechanical space, and bicycle parking — is currently in construction. Adjacent to those functions is an underground parking garage, a network of newly placed utilities, and water and sewer infrastructure. Vertical construction of the building should begin in September.

August 3, 2018 | More

Sloan

Engineering standards have long supported the world economy. But future challenges abound.

As you read this sentence on a computer or smartphone screen, you benefit from a wide variety of international standards.

From software languages to computer batteries, from small screws in electronic devices to large nuts and bolts in power plants, from electrical impulses to transmission lines – the use and design of all these elements has been defined by standards set by international non-governmental organizations that often operate under the radar.

June 7, 2019 | More

Bias and belief in meritocracy in AI and engineering

As artificial intelligence (AI) and machine learning techniques increasingly leave engineering laboratories to be deployed as decision-making tools in Human Resources (HR) and related contexts, recognition of and concerns about the potential biases of these tools grows. These tools first learn and then uncritically and mechanically reproduce existing inequalities. Recent research shows that this uncritical reproduction is not a new problem. The same has been happening among human decision-makers, particularly those in the engineering profession. In AI and engineering, the consequences are insidious, but both cases also point toward similar solutions. Bias in AI One common form of AI works by training computer algorithms on data sets with hundreds of thousands of cases, events, or persons, with millions of discrete bits of information. Using known outcomes or decisions (what is called the training set) and the range of available variables, AI learns how to use these … Read More »

The post Bias and belief in meritocracy in AI and engineering – Susan Silbey, Brian Rubineau, Erin Cech, Carroll Seron appeared first on MIT Sloan Experts.

June 5, 2019 | More

Rethinking financial risk in the era of climate change

Rethinking financial risk in the era of climate change

Floods. Fire. Extreme heat. Drought.

Those are just some of the manifestations of climate change — growing fiercer by the year — that threaten not just lives and livelihoods, but capital markets as well. A recent panel hosted by the Golub Center for Finance and Policy at MIT Sloan put weather events into financial perspective.

May 31, 2019 | More

What makes someone a great leader in the digital economy?

What makes someone a great leader in the digital economy?

What will great leadership look like in five years? What about in 10? Douglas Ready, a senior lecturer in organizational effectiveness at MIT Sloan and an expert on executive development, has lately been considering these questions as part of a Big Ideas research initiative with MIT Sloan Management Review and Cognizant. Ready has been thinking, too, about why they matter: we are becoming an ever more digital economy, and leadership must adapt.

May 29, 2019 | More

It’s All About Business Model Innovation, not New Technology

It’s all about business model innovation, not new technology

To survive in today’s fast changing marketplace, every business–large or small, startup or long established–must be capable of a continual process of transformation and renewal. Surveys show that most executives agree, and in fact, many believe that business model innovation is even more important to their company’s success than product or service innovation. But other studies have determined that no more than 10% of innovation investments at established companies are focused on creating transformative business models. This is not surprising. Most successful new business models come from startups. Despite the talent and resources at their disposal, business model success stories from well-established companies are relatively rare. “Building a great business and operating it well no longer guarantees you’ll be around in a hundred years, or even twenty,” notes business model expert Mark Johnson in his new book, “Reinvent Your Business Model.” Examples abound. In the 1970s, Xerox PARC famously developed, … Read More »

The post It’s all about business model innovation, not new technology – Irving Wladawsky-Berger appeared first on MIT Sloan Experts.

May 28, 2019 | More

A former Walmart exec on 3 ways retail supply chains are changing

A former Walmart exec on 3 ways retail supply chains are changing

When Walmart opened in 1962, its first warehouse was founder Sam Walton’s garage in the northwest tip of Arkansas. The supply chain for the low-cost retailer was Walton’s pickup truck, which he used to transport merchandise.

Nearly 60 years later, Walmart has more than 150 distribution centers across America — each of which ships more than 200 trailers per day with the help of the company’s private fleet of 8,000 drivers.

“Sam made the determination that you have to own the supply chain,” said Chris Sultemeier, former executive vice president of logistics. “The only way this works is if you own the supply chain.”

May 24, 2019 | More

Digital platforms: High valuations, but high risk of failure

Digital platforms: High valuations, but high risk of failure

The world’s largest companies are digital platforms. Amazon, Apple, and Microsoft have each temporarily surpassed $1 trillion in value. Uber’s upcoming initial public offering is expected to value the 10-year-old firm at $90 billion – roughly the same value as Dow DuPont, a conglomerate of two firms founded in the 19th century.

Companies from startups to enterprises have tried to emulate the rapid success of platforms, which achieve faster growth with fewer employees and more R&D spending than the non-platform firms. But these companies have often learned the hard way that, despite being easy to build and easy to scale, platforms are difficult to sustain.

“There are hundreds – and across the world thousand

May 10, 2019 | More

Every leader’s guide to the ethics of AI – Tom Davenport and Vivek Katyal

Every leader’s guide to the ethics of AI

From the MIT Sloan Management Review  As artificial intelligence-enabled products and services enter our everyday consumer and business lives, there’s a big gap between how AI can be used and how it should be used. Until the regulatory environment catches up with technology (if it ever does), leaders of all companies are on the hook for making ethical decisions about their use of AI applications and products. Ethical issues with AI can have a broad impact. They can affect the company’s brand and reputation, as well as the lives of employees, customers, and other stakeholders. One might argue that it’s still early to address AI ethical issues, but our surveys and others suggest that about 30% of large companies in the U.S. have undertaken multiple AI projects with smaller percentages outside the U.S., and there are now more than 2,000 AI startups. These companies are already building and deploying AI applications that could have ethical effects. Many executives are … Read More »

The post Every leader’s guide to the ethics of AI – Tom Davenport and Vivek Katyal appeared first on MIT Sloan Experts.

May 2, 2019 | More

Legacy code and the other AI – Tara Swart

Legacy code and the other AI

Have you heard of legacy code? In her article in the Financial Times, Lisa Pollack reveals how this has become a growing issue for businesses engaged in a process of modernizing their software and systems, with many large organizations and government departments’ websites relying on code headed for the “digital dustbin.” Archaic languages, such as Cobol, created 50 years ago, are a threat to progress not only for the direct and obvious inconvenience of them (Pollack describes how the Pentagon was relying on the use of eight-inch floppy disks to coordinate its intercontinental ballistic missiles and nuclear bombers, for example)  but also for all the indirect cascade of side effects that legacy code may trigger unpredictably in other parts of a system when you try to change or overwrite any part of it. To give an example, this could mean that a successful update, or overwriting, of some code for the … Read More »

The post Legacy code and the other AI – Tara Swart appeared first on MIT Sloan Experts.

May 1, 2019 | More

How a Fortune 500 CEO avoids mediocrity

How a Fortune 500 CEO avoids mediocrity

Mike McNamara has nearly 40 years of experience under his belt, but he can’t get his head around all the bad coffee out there.

“Somebody opens up a coffee shop down the street; the cappuccino isn’t better than the other guy,” said McNamara, the former CEO of tech company Flex. “If your cappuccino isn’t better, why are you opening?”

McNamara’s made a name for himself outside of the caffeine industry — he helped grow Flex, a 200,000-employee electronics and original design manufacturer from $150 million in revenue to $25 billion — and he’s

April 26, 2019 | More

Engineering

How to speed up the discovery of new solar cell materials

A broad class of materials called perovskites is considered one of the most promising avenues for developing new, more efficient solar cells. But the virtually limitless number of possible combinations of these materials’ constituent elements makes the search for promising new perovskites slow and painstaking.

Now, a team of researchers at MIT and several other institutions has accelerated the process of screening new formulations, achieving a roughly ten-fold improvement in the speed of the synthesis and analysis of new compounds. In the process, they have already discovered two sets of promising new perovskite-inspired materials that are worthy of further study.

Their findings are described this week in the journal Joule, in a paper by MIT research scientist Shijing Sun, professor of mechanical engineering Tonio Buonassisi, and 16 others at MIT, in Singapore, and at the National Institute of Standards and Technology in Maryland.

Somewhat surprisingly, although partial automation was employed, most of the improvements in throughput speed resulted from workflow ergonomics, says Buonassisi. That involves more traditional systems efficiencies, often derived by tracking and timing the many steps involved: synthesizing new compounds, depositing them on a substrate to crystallize, and then observing and classifying the resulting crystal formations using multiple techniques.

“There’s a need for accelerated development of new materials,” says Buonassisi, as the world continues to move toward solar energy, including in regions with limited space for solar panels. But the typical system for developing new energy-conversion materials can take 20 years, with significant upfront capital costs, he says. His team’s aim is to cut that development time to under two years.

Essentially, the researchers developed a system that allows a wide variety of materials to be made and tested in parallel. “We’re now able to access a large range of different compositions, using the same materials synthesis platform. It allows us to explore a vast range of parameter space,” he says.

Perovskite compounds consist of three separate constituents, traditionally labeled as A, B, and X site ions, each of which can be any one of a list of candidate elements, forming a very large structural family with diverse physical properties. In the field of perovskite and perovskite-inspired materials for photovoltaic applications, the B-site ion is typically lead, but a major effort in perovskite research is to find viable lead-free versions that can match or exceed the performance of the lead-based varieties.

While more than a thousand potentially useful perovskite formulations have been predicted theoretically, out of millions of theoretically possible combinations, only a small fraction of those has been produced experimentally so far, highlighting the need for an accelerated process, the researchers say.

For the experiments, the team selected a variety of different compositions, each of which they mixed in a solution and then deposited on a substrate, where the material crystallized into a thin film. The film was then examined using a technique called X-ray diffraction, which can reveal details of how the atoms are arranged in the crystal structure. These X-ray diffraction patterns were then initially classified with the help of a convolutional neural network system to speed up that part of the process. That classification step alone, Buonassisi says, initially took three to five hours, but by applying machine learning, this was slashed to 5.5 minutes while maintaining 90 percent accuracy.

Already, in their initial testing of the system, the team explored 75 different formulations in about a tenth of the time it previously would have taken to synthesize and characterize that many. Among those 75, they found two new lead-free perovskite systems that exhibit promising properties that might have potential for high-efficiency solar cells.

In the process, they produced four compounds in thin-film form for the first time; thin films are the desirable form for use in solar cells. They also found examples of “nonlinear bandgap tunability” in some of the materials, an unexpected characteristic that relates to the energy level needed to excite an electron in the material, which they say opens up new pathways for potential solar cells.

The team says that with further automation of parts of the process, it should be possible to continue to increase the processing speed, making it anywhere from 10 to 100 times as fast. Ultimately, Buonassisi says, it’s all about getting solar power to be as inexpensive as possible, continuing the technology’s already remarkable plunge. The aim is to bring economically sustainable prices below 2 cents per kilowatt-hour, he says, and getting there could be the result of a single breakthrough in materials: “All you have to do is make one material” that has just the right combination of properties — including ease of manufacture, low cost of materials, and high efficiency at converting sunlight.

“We’re putting all the experimental pieces in place so we can explore faster,” he says.

The work was supported by Total SA through the MIT Energy Initiative, by the National Science Foundation, and Singapore’s National Research Foundation through the Singapore-MIT Alliance for Research and Technology.

June 5, 2019 | More

Autonomous boats can target and latch onto each other

The city of Amsterdam envisions a future where fleets of autonomous boats cruise its many canals to transport goods and people, collect trash, or self-assemble into floating stages and bridges. To further that vision, MIT researchers have given new capabilities to their fleet of robotic boats — which are being developed as part of an ongoing project — that lets them target and clasp onto each other, and keep trying if they fail.

About a quarter of Amsterdam’s surface area is water, with 165 canals winding alongside busy city streets. Several years ago, MIT and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) teamed up on the “Roboat” project. The idea is to build a fleet of autonomous robotic boats — rectangular hulls equipped with sensors, thrusters, microcontrollers, GPS modules, cameras, and other hardware — that provides intelligent mobility on water to relieve congestion in the city’s busy streets.

One of project’s objectives is to create roboat units that provide on-demand transporation on waterways. Another objective is using the roboat units to automatically form “pop-up” structures, such as foot bridges, performance stages, or even food markets. The structures could then automatically disassemble at set times and reform into target structures for different activities. Additionally, the roboat units could be used as agile sensors to gather data on the city’s infrastructure, and air and water quality, among other things.

In 2016, MIT researchers tested a roboat prototype that cruised around Amsterdam’s canals, moving forward, backward, and laterally along a preprogrammed path. Last year, researchers designed low-cost, 3-D-printed, one-quarter scale versions of the boats, which were more efficient and agile, and came equipped with advanced trajectory-tracking algorithms.

In a paper presented at the International Conference on Robotics and Automation, the researchers describe roboat units that can now identify and connect to docking stations. Control algorithms guide the roboats to the target, where they automatically connect to a customized latching mechanism with millimeter precision. Moreover, the roboat notices if it has missed the connection, backs up, and tries again.

The researchers tested the latching technique in a swimming pool at MIT and in the Charles River, where waters are rougher. In both instances, the roboat units were usually able to successfully connect in about 10 seconds, starting from around 1 meter away, or they succeeded after a few failed attempts. In Amsterdam, the system could be especially useful for overnight garbage collection. Roboat units could sail around a canal, locate and latch onto platforms holding trash containers, and haul them back to collection facilities.

“In Amsterdam, canals were once used for transportation and other things the roads are now used for. Roads near canals are now very congested — and have noise and pollution — so the city wants to add more functionality back to the canals,” says first author Luis Mateos, a graduate student in the Department of Urban Studies and Planning (DUSP) and a researcher in the MIT Senseable City Lab. “Self-driving technologies can save time, costs and energy, and improve the city moving forward.”

“The aim is to use roboat units to bring new capabilities to life on the water,” adds co-author Daniela Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “The new latching mechanism is very important for creating pop-up structures. Roboat does not need latching for autonomous transporation on water, but you need the latching to create any structure, whether it’s mobile or fixed.”

Joining Mateos on the paper are: Wei Wang, a joint postdoc in CSAIL and the Senseable City Lab; Banti Gheneti, a graduate student in the Department of Electrical Engineering and Computer Science; Fabio Duarte, a DUSP and Senseable City Lab research scientist; and Carlo Ratti, director of the Senseable City Lab and a principal investigator and professor of the practice in DUSP.

Making the connection

Each roboat is equipped with latching mechanisms, including ball and socket components, on its front, back, and sides. The ball component resembles a badminton shuttlecock — a cone-shaped, rubber body with a metal ball at the end. The socket component is a wide funnel that guides the ball component into a receptor. Inside the funnel, a laser beam acts like a security system that detects when the ball crosses into the receptor. That activates a mechanism with three arms that closes around and captures the ball, while also sending a feedback signal to both roboats that the connection is complete.

On the software side, the roboats run on custom computer vision and control techniques. Each roboat has a LIDAR system and camera, so they can autonomously move from point to point around the canals. Each docking station — typically an unmoving roboat — has a sheet of paper imprinted with an augmented reality tag, called an AprilTag, which resembles a simplified QR code. Commonly used for robotic applications, AprilTags enable robots to detect and compute their precise 3-D position and orientation relative to the tag.

Both the AprilTags and cameras are located in the same locations in center of the roboats. When a traveling roboat is roughly one or two meters away from the stationary AprilTag, the roboat calculates its position and orientation to the tag. Typically, this would generate a 3-D map for boat motion, including roll, pitch, and yaw (left and right). But an algorithm strips away everything except yaw. This produces an easy-to-compute 2-D plane that measures the roboat camera’s distance away and distance left and right of the tag. Using that information, the roboat steers itself toward the tag. By keeping the camera and tag perfectly aligned, the roboat is able to precisely connect.

The funnel compensates for any misalignment in the roboat’s pitch (rocking up and down) and heave (vertical up and down), as canal waves are relatively small. If, however, the roboat goes beyond its calculated distance, and doesn’t receive a feedback signal from the laser beam, it knows it has missed. “In challenging waters, sometimes roboat units at the current one-quarter scale, are not strong enough to overcome wind gusts or heavy water currents,” Mateos says. “A logic component on the roboat says, ‘You missed, so back up, recalculate your position, and try again.’”

Future iterations

The researchers are now designing roboat units roughly four times the size of the current iterations, so they’ll be more stable on water. Mateos is also working on an update to the funnel that includes tentacle-like rubber grippers that tighten around the pin — like a squid grasping its prey. That could help give the roboat units more control when, say, they’re towing platforms or other roboats through narrow canals.

In the works is also a system that displays the AprilTags on an LCD monitor that changes codes to signal multiple roboat units to assemble in a given order. At first, all roboat units will be given a code to stay exactly a meter apart. Then, the code changes to direct the first roboat to latch. After, the screen switches codes to order the next roboat to latch, and so on. “It’s like the telephone game. The changing code passes a message to one roboat at a time, and that message tells them what to do,” Mateos says.

Darwin Caldwell, the research director of Advanced Robotics at the Italian Institute of Technology, envisions even more possible applications for the autonomous latching capability. “I can certainly see this type of autonomous docking being of use in many areas of robotic ‘refuelling’ and docking … beyond aquatic/naval systems,” he says, “including inflight refuelling, space docking, cargo container handling, [and] robot in-house recharging.”

The research was funded by the AMS Institute and the City of Amsterdam.

June 5, 2019 | More

Bringing human-like reasoning to driverless car navigation Autonomous control system 'learns' to use simple maps and image data to navigate new, complex routes

Bringing human-like reasoning to driverless car navigation

With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments.

Human drivers are exceptionally good at navigating roads they haven’t driven on before, using observation and simple tools. We simply match what we see around us to what we see on our GPS devices to determine where we are and where we need to go. Driverless cars, however, struggle with this basic reasoning. In every new area, the cars must first map and analyze all the new roads, which is very time consuming. The systems also rely on complex maps — usually generated by 3-D scans — which are computationally intensive to generate and process on the fly.

In a paper being presented at this week’s International Conference on Robotics and Automation, MIT researchers describe an autonomous control system that “learns” the steering patterns of human drivers as they navigate roads in a small area, using only data from video camera feeds and a simple GPS-like map. Then, the trained system can control a driverless car along a planned route in a brand-new area, by imitating the human driver.

Similarly to human drivers, the system also detects any mismatches between its map and features of the road. This helps the system determine if its position, sensors, or mapping are incorrect, in order to correct the car’s course.

To train the system initially, a human operator controlled an automated Toyota Prius — equipped with several cameras and a basic GPS navigation system — to collect data from local suburban streets including various road structures and obstacles. When deployed autonomously, the system successfully navigated the car along a preplanned path in a different forested area, designated for autonomous vehicle tests.

“With our system, you don’t need to train on every road beforehand,” says first author Alexander Amini, an MIT graduate student. “You can download a new map for the car to navigate through roads it has never seen before.”

“Our objective is to achieve autonomous navigation that is robust for driving in new environments,” adds co-author Daniela Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “For example, if we train an autonomous vehicle to drive in an urban setting such as the streets of Cambridge, the system should also be able to drive smoothly in the woods, even if that is an environment it has never seen before.”

Joining Rus and Amini on the paper are Guy Rosman, a researcher at the Toyota Research Institute, and Sertac Karaman, an associate professor of aeronautics and astronautics at MIT.

Point-to-point navigation

Traditional navigation systems process data from sensors through multiple modules customized for tasks such as localization, mapping, object detection, motion planning, and steering control. For years, Rus’s group has been developing “end-to-end” navigation systems, which process inputted sensory data and output steering commands, without a need for any specialized modules.

Until now, however, these models were strictly designed to safely follow the road, without any real destination in mind. In the new paper, the researchers advanced their end-to-end system to drive from goal to destination, in a previously unseen environment. To do so, the researchers trained their system to predict a full probability distribution over all possible steering commands at any given instant while driving.

The system uses a machine learning model called a convolutional neural network (CNN), commonly used for image recognition. During training, the system watches and learns how to steer from a human driver. The CNN correlates steering wheel rotations to road curvatures it observes through cameras and an inputted map. Eventually, it learns the most likely steering command for various driving situations, such as straight roads, four-way or T-shaped intersections, forks, and rotaries.

“Initially, at a T-shaped intersection, there are many different directions the car could turn,” Rus says. “The model starts by thinking about all those directions, but as it sees more and more data about what people do, it will see that some people turn left and some turn right, but nobody goes straight. Straight ahead is ruled out as a possible direction, and the model learns that, at T-shaped intersections, it can only move left or right.”

What does the map say?

In testing, the researchers input the system with a map with a randomly chosen route. When driving, the system extracts visual features from the camera, which enables it to predict road structures. For instance, it identifies a distant stop sign or line breaks on the side of the road as signs of an upcoming intersection. At each moment, it uses its predicted probability distribution of steering commands to choose the most likely one to follow its route.

Importantly, the researchers say, the system uses maps that are easy to store and process. Autonomous control systems typically use LIDAR scans to create massive, complex maps that take roughly 4,000 gigabytes (4 terabytes) of data to store just the city of San Francisco. For every new destination, the car must create new maps, which amounts to tons of data processing. Maps used by the researchers’ system, however, captures the entire world using just 40 gigabytes of data.

During autonomous driving, the system also continuously matches its visual data to the map data and notes any mismatches. Doing so helps the autonomous vehicle better determine where it is located on the road. And it ensures the car stays on the safest path if it’s being fed contradictory input information: If, say, the car is cruising on a straight road with no turns, and the GPS indicates the car must turn right, the car will know to keep driving straight or to stop.

“In the real world, sensors do fail,” Amini says. “We want to make sure that the system is robust to different failures of different sensors by building a system that can accept these noisy inputs and still navigate and localize itself correctly on the road.”

May 23, 2019 | More

New surface treatment could improve refrigeration efficiency A slippery surface for liquids with very low surface tension promotes droplet formation, facilitating heat transfer

New surface treatment could improve refrigeration efficiency

Unlike water, liquid refrigerants and other fluids that have a low surface tension tend to spread quickly into a sheet when they come into contact with a surface. But for many industrial processes it would be better if the fluids formed droplets, which could roll or fall off the surface and carry heat away with them.

Now, researchers at MIT have made significant progress in promoting droplet formation and shedding in such fluids. This approach could lead to efficiency improvements in many large-scale industrial processes including refrigeration, thus saving energy and reducing greenhouse gas emissions.

The new findings are described in the journal Joule, in a paper by graduate student Karim Khalil, professor of mechanical engineering Kripa Varanasi, professor of chemical engineering and Associate Provost Karen Gleason, and four others.

Over the years, Varanasi and his collaborators have made great progress in improving the efficiency of condensation systems that use water, such as the cooling systems used for fossil-fuel or nuclear power generation. But other kinds of fluids — such as those used in refrigeration systems, liquification, waste heat recovery, and distillation plants, or materials such as methane in oil and gas liquifaction plants — often have very low surface tension compared to water, meaning that it is very hard to get them to form droplets on a surface. Instead, they tend to spread out in a sheet, a property known as wetting.

But when these sheets of liquid coat a surface, they provide an insulating layer that inhibits heat transfer, and easy heat transfer is crucial to making these processes work efficiently. “If it forms a film, it becomes a barrier to heat transfer,” Varanasi says. But that heat transfer is enhanced when the liquid quickly forms droplets, which then coalesce and grow and fall away under the force of gravity. Getting low-surface-tension liquids to form droplets and shed them easily has been a serious challenge.

In condensing systems that use water, the overall efficiency of the process can be around 40 percent, but with low-surface-tension fluids, the efficiency can be limited to about 20 percent. Because these processes are so widespread in industry, even a tiny improvement in that efficiency could lead to dramatic savings in fuel, and therefore in greenhouse gas emissions, Varanasi says.

By promoting droplet formation, he says, it’s possible to achieve a four- to eightfold improvement in heat transfer. Because the condensation is just one part of a complex cycle, that translates into an overall efficiency improvement of about 2 percent. That may not sound like much, but in these huge industrial processes even a fraction of a percent improvement is considered a major achievement with great potential impact. “In this field, you’re fighting for tenths of a percent,” Khalil says.

Unlike the surface treatments Varanasi and his team have developed for other kinds of fluids, which rely on a liquid material held in place by a surface texture, in this case they were able to accomplish the fluid-repelling effect using a very thin solid coating — less than a micron thick (one millionth of a meter). That thinness is important, to ensure that the coating itself doesn’t contribute to blocking heat transfer, Khalil explains.

The coating, made of a specially formulated polymer, is deposited on the surface using a process called initiated chemical vapor deposition (iCVD), in which the coating material is vaporized and grafts onto the surface to be treated, such as a metal pipe, to form a thin coating. This process was developed at MIT by Gleason and is now widely used.

The authors optimized the iCVD process by tuning the grafting of coating molecules onto the surface, in order to minimize the pinning of condensing droplets and facilitate their easy shedding. The process could be carried out on location in industrial-scale equipment, and could be retrofitted into existing installations to provide a boost in efficiency. The process is “materials agnostic,” Khalil says, and can be applied on either flat surfaces or tubing made of stainless steel, copper, titanium, or other metals commonly used in evaporative heat-transfer processes that involve these low-surface-tension fluids. “Whatever material you come up with, it tends to be scalable with this process,” he adds.

Video shows the condensation of pentane, a low-surface-tension fluid. On the left, the new coating shows high droplet formation and good heat transfer, while a different coating, at right, leads to streaking of drops and worse heat transfer.

The net result is that on these surfaces, condensing fluids such as liquid methane will readily form small droplets that quickly fall off the surface, making room for more to form, and in the process shedding heat from the metal to the droplets that fall away. Without the coating, the fluid would spread out over the whole surface and resist falling away, forming a kind of heat-retaining blanket. But with it, “the heat transfer improves by almost eight times,” Khalil says.

One area where such coatings could play a useful role, Varanasi says, is in organic Rankine cycle systems, which are widely used for generating power from waste heat in a variety of industrial processes. “These are inherently inefficient systems,” he says, “but this could make them more efficient.”

The new coating is shown promoting condensation on a titanium surface, a material widely used in industrial heat exchangers.

“This new approach to condensation is significant because it promotes drop formation (rather than film formation) even for low-surface-tension fluids, which significantly improves the heat transfer efficiency,” says Jonathan Boreyko, an assistant professor of mechanical engineering at Virginia Tech, who was not connected to this research. While the iCVD process itself is not new, he says, “showing here that it can be used even for the condensation of low-surface-tension fluids is of significant practical importance, as many real-life phase-change systems do not use water.”

Saying the work is “of very high quality,” Boreyko adds that “simply showing for the first time that a thin, durable, and dry coating can promote the dropwise condensation of low-surface-tension fluids is very important for a wide variety of practical condenser systems.”

The research was supported by the Shell-MIT Energy Initiative partnership.

May 15, 2019 | More

Wireless movement-tracking system could collect health and behavioral data In some cases, radio frequency signals may be more useful for caregivers than cameras or other data-collection methods

Wireless movement-tracking system could collect health and behavioral data

We live in a world of wireless signals flowing around us and bouncing off our bodies. MIT researchers are now leveraging those signal reflections to provide scientists and caregivers with valuable insights into people’s behavior and health.

The system, called Marko, transmits a low-power radio-frequency (RF) signal into an environment. The signal will return to the system with certain changes if it has bounced off a moving human. Novel algorithms then analyze those changed reflections and associate them with specific individuals.

The system then traces each individual’s movement around a digital floor plan. Matching these movement patterns with other data can provide insights about how people interact with each other and the environment.

In a paper being presented at the Conference on Human Factors in Computing Systems this week, the researchers describe the system and its real-world use in six locations: two assisted living facilities, three apartments inhabited by couples, and one townhouse with four residents. The case studies demonstrated the system’s ability to distinguish individuals based solely on wireless signals — and revealed some useful behavioral patterns.

In one assisted living facility, with permission from the patient’s family and caregivers, the researchers monitored a patient with dementia who would often become agitated for unknown reasons. Over a month, they measured the patient’s increased pacing between areas of their unit — a known sign of agitation. By matching increased pacing with the visitor log, they determined the patient was agitated more during the days following family visits. This shows Marko can provide a new, passive way to track functional health profiles of patients at home, the researchers say.

“These are interesting bits we discovered through data,” says first author Chen-Yu Hsu, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We live in a sea of wireless signals, and the way we move and walk around changes these reflections. We developed the system that listens to those reflections … to better understand people’s behavior and health.”

The research is led by Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of the MIT Center for Wireless Networks and Mobile Computing (Wireless@MIT). Joining Katabi and Hsu on the paper are CSAIL graduate students Mingmin Zhao and Guang-He Lee and alumnus Rumen Hristov SM ’16.

Predicting “tracklets” and identities

When deployed in a home, Marko shoots out an RF signal. When the signal rebounds, it creates a type of heat map cut into vertical and horizontal “frames,” which indicates where people are in a three-dimensional space. People appear as bright blobs on the map. Vertical frames capture the person’s height and build, while horizontal frames determine their general location. As individuals walk, the system analyzes the RF frames — about 30 per second — to generate short trajectories, called tracklets.

A convolutional neural network — a machine-learning model commonly used for image processing — uses those tracklets to separate reflections by certain individuals. For each individual it senses, the system creates two “filtering masks,” which are small circles around the individual. These masks basically filter out all signals outside the circle, which locks in the individual’s trajectory and height as they move. Combining all this information — height, build, and movement — the network associates specific RF reflections with specific individuals.

But to tag identities to those anonymous blobs, the system must first be “trained.” For a few days, individuals wear low-powered accelerometer sensors, which can be used to label the reflected radio signals with their respective identities. When deployed in training, Marko first generates users’ tracklets, as it does in practice. Then, an algorithm correlates certain acceleration features with motion features. When users walk, for instance, the acceleration oscillates with steps, but becomes a flat line when they stop. The algorithm finds the best match between the acceleration data and tracklet, and labels that tracklet with the user’s identity. In doing so, Marko learns which reflected signals correlate to specific identities.

The sensors never have to be charged, and, after training, the individuals don’t need to wear them again. In home deployments, Marko was able to tag the identities of individuals in new homes with between 85 and 95 percent accuracy.

Striking a good (data-collection) balance

The researchers hope health care facilities will use Marko to passively monitor, say, how patients interact with family and caregivers, and whether patients receive medications on time. In an assisted living facility, for instance, the researchers noted specific times a nurse would walk to a medicine cabinet in a patient’s room and then to the patient’s bed. That indicated that the nurse had, at those specific times, administered the patient’s medication.

The system may also replace questionnaires and diaries currently used by psychologists or behavioral scientists to capture data on their study subjects’ family dynamics, daily schedules, or sleeping patterns, among other behaviors. Those traditional recording methods can be inaccurate, contain bias, and aren’t well-suited for long-term studies, where people may have to recall what they did days or weeks ago. Some researchers have started equipping people with wearable sensors to monitor movement and biometrics. But elderly patients, especially, often forget to wear or charge them. “The motivation here is to design better tools for researchers,” Hsu says.

Why not just install cameras? For starters, this would require someone watching and manually recording all necessary information. Marko, on the other hand, automatically tags behavioral patterns — such as motion, sleep, and interaction — to specific areas, days, and times.

Also, video is just more invasive, Hsu adds: “Most people aren’t that comfortable with being filmed all the time, especially in their own home. Using radio signals to do all this work strikes a good balance between getting some level of helpful information, but not making people feel uncomfortable.”

Katabi and her students also plan to combine Marko with their prior work on inferring breathing and heart rate from the surrounding radio signals. Marko will then be used to associate those biometrics with the corresponding individuals. It could also track people’s walking speeds, which is a good indicator of functional health in elderly patients.

“The potential here is immense,” says Cecilia Mascolo, a professor of mobile systems in the Department of Computer Science and Technology at Cambridge University. “With respect to imaging through cameras, it offers a less data-rich and more targeted model of collecting information, which is very welcome from the user privacy perspective. The data collected, however, is still very rich, and the paper evaluation shows accuracy which can enable a number of very useful applications, for example in elderly care, medical adherence monitoring, or even hospital care.”

“Yet, as a community, we need to aware of the privacy risks that this type of technology bring,” Mascolo adds. Certain computation techniques, she says, should be considered to ensure the data remains private.

May 8, 2019 | More

Study demonstrates seagrass’ strong potential for curbing erosion Ubiquitous marine plants dissipate wave energy and could help protect vulnerable shorelines.

Study demonstrates seagrass’ strong potential for curbing erosion

Most people’s experience with seagrass, if any, amounts to little more than a tickle on their ankles while wading in shallow coastal waters. But it turns out these ubiquitous plants, varieties of which exist around the world, could play a key role in protecting vulnerable shores as they face onslaughts from rising sea levels.

New research for the first time quantifies, through experiments and mathematical modelling, just how large and how dense a continuous meadow of seagrass must be to provide adequate damping of waves in a given geographic, climatic, and oceanographic setting.

In a pair of papers appearing in the May issues of two research journals, Coastal Engineering and the Journal of Fluids and Structures, MIT professor of civil and environmental engineering Heidi Nepf and doctoral student Jiarui Lei describe their findings and the significant environmental benefits seagrass offers. These include not only preventing beach erosion and protecting seawalls and other structures, but also improving water quality and sequestering carbon to help limit future climate change.

Those services, coupled with better-known services such as providing habitat for fish and food for other marine creatures, mean that submerged aquatic vegetation including seagrass provides an overall value of more than $4 trillion globally every year, as earlier studies have shown. Yet today, some important seagrass areas such as the Chesapeake Bay are down to about half of their historic seagrass coverage (having rebounded from a low of just 2 percent), thus limiting the availability of these valuable services.

Nepf and Lei recreated artificial versions of seagrass, assembled from materials of different stiffness to reproduce the long, flexible blades and much stiffer bases that are typical of seagrass plants such as Zostera marina, also known as common eelgrass. They set up a meadow-like collection of these artificial plants in a 79-foot-long (24-meter) wave tank in MIT’s Parsons Laboratory, which can mimic conditions of natural waves and currents. They subjected the meadow to a variety of conditions, including still water, strong currents, and wave-like sloshing back and forth. Their results validated predictions made earlier using a computerized model of individual plants.

Researchers used a74-foot-long wave tank at MIT, loaded with simulated seagrass plants, to study how seagrass acts to attenuate waves under various conditions. In this video, the simulated plants are exposed to strong waves.

In further tests in the MIT tank, simulated seagrass plants are subjected to very low-velocity waves.

The researchers used the physical and numerical models to analyze how the seagrass and waves interact under a variety of conditions of plant density, blade lengths, and water motions. The study describes how the motion of the plants changes with blade stiffness, wave period, and wave amplitude, providing a more precise prediction of wave damping over seagrass meadows. While other research has modeled some of these conditions, the new work more faithfully reproduces real-world conditions and provides a more realistic platform for testing ideas about seagrass restoration or ways of optimizing the beneficial effects of such submerged meadows, they say.

To test the validity of the model, the team then did a comparison of the predicted effects of seagrass on waves, looking at one specific seagrass meadow off the shore of the Spanish island of Mallorca, in the Mediterranean Sea, which is known to attenuate the force of incoming waves by a factor of about 50 percent on average. Using measurements of meadow morphology and wave velocities collected in a previous study led by Professor Eduardo Infantes, currently of Gothenburg University, Lei was able to confirm the predictions made by the model, which analyzed the way the tips of the grass blades and particles suspended in the water both tend to follow circular paths as waves go by, forming circles of motion known as orbitals.

The observations there matched the predictions very well, Lei says, showing the way wave strength and seagrass motion varied with distance from the edge of the meadow to its interior agreed with the model. So, “with this model the engineers and practitioners can assess different scenarios for seagrass restoration projects, which is a big deal right now,” he says That could make a significant difference, he says, because now some restoration projects are considered too expensive to undertake, whereas a better analysis could show that a smaller area, less expensive to restore, might be capable of providing the desired level of protection. In other areas, the analysis might show that a project is not worth doing at all, because the characteristics of the local waves or currents would limit the grasses’ effectiveness.

The particular seagrass meadow in Mallorca that they studied is known to be very dense and uniform, so one future project is to extend the comparison to other seagrass areas, including those that are patchier or less thickly vegetated, Nepf says, to demonstrate that the model can indeed be useful under a variety of conditions.

By attenuating the waves and thus providing protection against erosion, the seagrass can trap fine sediment on the seabed. This can significantly reduce or prevent runaway growth of algae fed by the nutrients associated with the fine sediment, which in turn causes a depletion of oxygen that can kill off much of the marine life, a process called eutrophication.

Seagrass also has significant potential for sequestering carbon, both through its own biomass and by filtering out fine organic material from the surrounding water, according to Nepf, and this is a focus of her and Lei’s ongoing research. An acre of seagrass can store about three times as much carbon as an acre of rainforest, and Lei says preliminary calculations suggest that globally, seagrass meadows are responsible for more than 10 percent of carbon buried in the ocean, even though they occupy just 0.2 percent of the area.

While other researchers have studied the effects of seagrass in steady currents, or in oscillating waves, “they are the first to combine these two types of flows, which are what real plants are typically subjected to. Despite the added complexity, they really sort out the physics and define different flow regimes with different behaviours,” says Frédérick Gosselin, professor of mechanical engineering at Polytechnique Montréal, in Canada, who was not connected to this research.

Gosselin adds, “This line of research is critical. Land developers are quick to fill and dredge wetlands without much thinking about the role these humid environments play.” This study “demonstrates how submerged vegetation has a precisely quantifiable effect on damping incoming waves. This means we can now evaluate exactly how much a meadow protects the coast from erosion. … This information would allow better decisions by our lawmakers.”

The work was funded by the U.S. National Science Foundation.

May 3, 2019 | More

Three from MIT elected to the National Academy of Sciences for 2019 Faculty members Edward Boyden, Paula Hammond, and Aviv Regev recognized for “distinguished and continuing achievements in original research.”

Three from MIT elected to the National Academy of Sciences for 2019

Three MIT professors — Edward Boyden, Paula Hammond, and Aviv Regev — are among the 100 new members and 25 foreign associates elected to the National Academy of Sciences on April 30. Forty percent of the newly elected members are women, the most ever elected in any one year to date.

Membership to the National Academy of Sciences is considered one of the highest honors that a scientist or engineer can receive. Current membership totals approximately 2,380 members and nearly 485 foreign associates.

Edward S. Boyden is the Y. Eva Tan Professor in Neurotechnology at MIT; leader of the Synthetic Neurobiology Group in the MIT Media Lab; associate professor of biological engineering and of brain and cognitive sciences; a McGovern Institute investigator; co-director of the MIT Center for Neurobiological Engineering; and a member of the MIT Center for Environmental Health Sciences, Computational and Systems Biology Initiative, and Koch Institute for Integrative Cancer Research at MIT.

Boyden develops new tools for probing, analyzing, and engineering brain circuits. He uses a range of approaches, including synthetic biology, nanotechnology, chemistry, electrical engineering, and optics to develop tools capable of revealing fundamental mechanisms underlying complex brain processes. He pioneered the development of optogenetics, a powerful method that enables neuronal activity to be controlled with light. He also led the team that invented expansion microscopy, in which a specimen is embedded in a gel that swells as it absorbs water, thereby expanding nanoscale features to a size where they can be seen using conventional microscopes. He is now seeking to systematically integrate these technologies to create detailed maps and models of brain circuitry.

Paula T. Hammond is the David H. Koch Chair Professor of Engineering and the head of the Department of Chemical Engineering; a founding member of the MIT Institute for Soldier Nanotechnology; and a member of the MIT Energy Initiative and Koch Institute.

Hammond’s research in nanomedicine encompasses the development of new biomaterials to enable drug delivery from surfaces with spatio-temporal control. She also investigates novel responsive polymer architectures for targeted nanoparticle drug and gene delivery, and has developed self-assembled materials systems for electrochemical energy devices. She has designed multilayered nanoparticles to deliver a synergistic combination of siRNA or inhibitors with chemotherapy drugs in a staged manner to tumors, leading to significant decreases in tumor growth and a great lowering of toxicity.

Aviv Regev is a professor of biology; a core member of the Broad Institute of Harvard and MIT; and aHoward Hughes Medical Institute investigator.

Regev studies the molecular circuitry that governs the function of mammalian cells in health and disease and has pioneered many leading experimental and computational methods for the reconstruction of circuits, including in single-cell genomics. Her work focuses on dissecting complex molecular networks to determine how they function and evolve in the face of genetic and environmental changes, as well as during differentiation, evolution and disease.

The National Academy of Sciences is a private, non-profit society of distinguished scholars. Established in 1863 by an Act of Congress, signed by President Abraham Lincoln, the academy was charged with “providing independent, objective advice to the nation on matters related to science and technology.” Scientists are elected by their peers to membership for outstanding contributions to research. The NAS is committed to furthering science in America, and its members are active contributors to the international scientific community.

May 1, 2019 | More

Designing ocean ecological systems in the lab Associate Professor Otto Cordero and colleagues discover simple assembly rules for marine microbiomes

Designing ocean ecological systems in the lab

Researchers from MIT have discovered simple rules of assembly of ocean microbiomes that degrade complex polysaccharides in coastal environments. Microbiomes, or microbial communities, are composed of hundreds or thousands of diverse species, making it a challenge to identify the principles that govern their structure and function.

The findings indicate that marine microbiomes can be simplified by grouping species into two types of functional modules. The first type contain polysaccharide specialists that produce the enzymes required to break down the complex sugars. The second type contains species that consume simple metabolic byproducts released by the specialist degraders and are therefore independent of the polysaccharide. This partitioning reveals a simple design for the microbiome: a trophic network in which energy is funneled from degraders to consumers.

“Our work reveals fundamental principles of microbial community assembly that can help us make sense of the vast diversity of microbes in the environment,” states Otto X. Cordero, principal investigator on the research and associate professor in the Department of Civil and Environmental Engineering (CEE).

Cordero’s co-authors on the paper include CEE research affiliates Tim Enke and Manoshi S. Datta, CEE postdoc Julia Schwartzman, and Computational and Systems Biology Program research affiliate Nathan Cermak, as well as researchers from science and technology university ETH Zurich in Switzerland.

The simple trophic organization revealed by this study allowed Cordero and colleagues to predict microbiome species composition based on the profile of energy resources available to the community.

“The significance of these discoveries is that we have identified simple rules of assembly, which allows us to predict community composition and rationally design ecological systems in the lab,” emphasizes Cordero.

In order to investigate the modular organization of the microbial communities, the researchers conducted fieldwork with synthetic marine particles made of polysaccharides that are abundant in marine environments, such as chitin, alginate, agarose and carrageenan, as well as combinations of these substrates.

The team immersed the microscopic particles in natural samples of seawater and studied the colonization dynamics of bacteria using genome sequencing. This analysis allowed the researchers to disentangle the effect of polysaccharide composition on microbiome assembly.

“A promising application of this work is to apply these principles in order to design synthetic communities that degrade complex biological materials, such as those found in agricultural waste and animal feed,” says Cordero.

April 25, 2019 | More

Improving security as artificial intelligence moves to smartphones Researchers unveil a tool for making compressed deep learning models less vulnerable to attack.

Improving security as artificial intelligence moves to smartphones

Smartphones, security cameras, and speakers are just a few of the devices that will soon be running more artificial intelligence software to speed up image- and speech-processing tasks. A compression technique known as quantization is smoothing the way by making deep learning models smaller to reduce computation and energy costs. But smaller models, it turns out, make it easier for malicious attackers to trick an AI system into misbehaving — a concern as more complex decision-making is handed off to machines.

In a new study, MIT and IBM researchers show just how vulnerable compressed AI models are to adversarial attack, and they offer a fix: add a mathematical constraint during the quantization process to reduce the odds that an AI will fall prey to a slightly modified image and misclassify what they see.

When a deep learning model is reduced from the standard 32 bits to a lower bit length, it’s more likely to misclassify altered images due to an error amplification effect: The manipulated image becomes more distorted with each extra layer of processing. By the end, the model is more likely to mistake a bird for a cat, for example, or a frog for a deer.

Models quantized to 8 bits or fewer are more susceptible to adversarial attacks, the researchers show, with accuracy falling from an already low 30-40 percent to less than 10 percent as bit width declines. But controlling the Lipschitz constraint during quantization restores some resilience. When the researchers added the constraint, they saw small performance gains in an attack, with the smaller models in some cases outperforming the 32-bit model.

“Our technique limits error amplification and can even make compressed deep learning models more robust than full-precision models,” says Song Han, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of MIT’s Microsystems Technology Laboratories. “With proper quantization, we can limit the error.”

The team plans to further improve the technique by training it on larger datasets and applying it to a wider range of models. “Deep learning models need to be fast and secure as they move into a world of internet-connected devices,” says study coauthor Chuang Gan, a researcher at the MIT-IBM Watson AI Lab. “Our Defensive Quantization technique helps on both fronts.”

The researchers, who include MIT graduate student Ji Lin, present their results at the International Conference on Learning Representations in May.

In making AI models smaller so that they run faster and use less energy, Han is using AI itself to push the limits of model compression technology. In related recent work, Han and his colleagues show how reinforcement learning can be used to automatically find the smallest bit length for each layer in a quantized model based on how quickly the device running the model can process images. This flexible bit width approach reduces latency and energy use by as much as 200 percent compared to a fixed, 8-bit model, says Han. The researchers will present their results at the Computer Vision and Pattern Recognition conference in June.

April 23, 2019 | More

Robots that can sort recycling

Every year trash companies sift through an estimated 68 million tons of recycling, which is the weight equivalent of more than 30 million cars.

A key step in the process happens on fast-moving conveyor belts, where workers have to sort items into categories like paper, plastic and glass. Such jobs are dull, dirty, and often unsafe, especially in facilities where workers also have to remove normal trash from the mix.

With that in mind, a team led by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a robotic system that can detect if an object is paper, metal, or plastic.

The team’s “RoCycle” system includes a soft Teflon hand that uses tactile sensors on its fingertips to detect an object’s size and stiffness. Compatible with any robotic arm, RoCycle was found to be 85 percent accurate at detecting materials when stationary, and 63 percent accurate on an actual simulated conveyer belt. (Its most common error was identifying paper-covered metal tins as paper, which the team says would be improved by adding more sensors along the contact surface.)

“Our robot’s sensorized skin provides haptic feedback that allows it to differentiate between a wide range of objects, from the rigid to the squishy,” says MIT Professor Daniela Rus, senior author on a related paper that will be presented in April at the IEEE International Conference on Soft Robotics (RoboSoft) in Seoul, South Korea. “Computer vision alone will not be able to solve the problem of giving machines human-like perception, so being able to use tactile input is of vital importance.”

A collaboration with Yale University, RoCycle directly demonstrates the limits of sight-based sorting: It can reliably distinguish between two visually similar Starbucks cups, one made of paper and one made of plastic, that would give vision systems trouble.

Incentivizing recycling

Rus says that the project is part of her larger goal to reduce the back-end cost of recycling, in order to incentivize more cities and countries to create their own programs. Today recycling centers aren’t particularly automated; their main kinds of machinery include optical sorters that use different wavelength light to distinguish between plastics, magnetic sorters that separate out iron and steel products, and aluminum sorters that use eddy currents to remove non-magnetic metals.

This is a problem for one very big reason: just last month China raised its standards for the cleanliness of recycled goods it accepts from the United States, meaning that some of the country’s single-stream recycling is now sent to landfills.

“If a system like RoCycle could be deployed on a wide scale, we’d potentially be able to have the convenience of single-stream recycling with the lower contamination rates of multi-stream recycling,” says PhD student Lillian Chin, lead author on the new paper.

It’s surprisingly hard to develop machines that can distinguish between paper, plastic, and metal, which shows how impressive a feat it is for humans. When we pick up an object, we can immediately recognize many of its qualities even with our eyes closed, like whether it’s large and stiff or small and soft. By feeling the object and understanding how that relates to the softness of our own fingertips, we are able to learn how to handle a wide range of objects without dropping or breaking them.

This kind of intuition is tough to program into robots. Traditional hard (“rigid”) robot hands have to know an object’s exact location and size to be able to calculate a precise motion path. Soft hands made of materials like rubber are much more flexible, but have a different problem: Because they’re powered by fluidic forces, they have a balloon-like structure that can puncture quite easily.

How RoCycle works

Rus’ team used a motor-driven hand made of a relatively new material called “auxetics.” Most materials get narrower when pulled on, like a rubber band when you stretch it; auxetics, meanwhile, actually get wider. The MIT team took this concept and put a twist on it, quite literally: They created auxetics that, when cut, twist to either the left or right. Combining a “left-handed” and “right-handed” auxetic for each of the hand’s two large fingers makes them interlock and oppose each other’s rotation, enabling more dynamic movement. (The team calls this “handed-shearing auxetics”, or HSA.)

“In contrast to soft robots, whose fluid-driven approach requires air pumps and compressors, HSA combines twisting with extension, meaning that you’re able to use regular motors,” says Chin.

The team’s gripper first uses its “strain sensor” to estimate an object’s size, and then uses its two pressure sensors to measure the force needed to grasp an object. These metrics — along with calibration data on the size and stiffnesses of objects of different material types — are what gives the gripper a sense of what material the object is made. (Since the tactile sensors are also conductive, they can detect metal by how much it changes the electrical signal.)

“In other words, we estimate the size and measure the pressure difference between the current closed hand and what a normal open hand should look like,” says Chin. “We use this pressure difference and size to classify the specific object based on information about different objects that we’ve already measured.”

RoCycle builds on an set of sensors that detect the radius of an object to within 30 percent accuracy, and tell the difference between “hard” and “soft” objects with 78 percent accuracy. The team’s hand is also almost completely puncture resistant: It was able to be scraped by a sharp lid and punctured by a needle more than 20 times, with minimal structural damage.

As a next step, the researchers plan to build out the system so that it can combine tactile data with actual video data from a robot’s cameras. This would allow the team to further improve its accuracy and potentially allow for even more nuanced differentiation between different kinds of materials.

Chin and Rus co-wrote the RoCycle paper alongside MIT postdoc Jeffrey Lipton, as well as PhD student Michelle Yuen and Professor Rebecca Kramer-Bottiglio of Yale University.

This project was supported in part by Amazon, JD.com, the Toyota Research Institute, and the National Science Foundation.

April 16, 2019 | More