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
Sixteen grad students named to the Siebel Scholars class of 2020
LGO ’20 Hans Nowak is among the 2020 cohort of Siebel Scholars hailing from the world’s top graduate programs in bioengineering, business, computer science, and energy science. They were recognized at a luncheon and awards ceremony on campus on Oct. 31.
“You’re among a very select group of students to receive this honor,” Anantha Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science, told the students. “Your department heads obviously think very highly of your accomplishments.”
Honored for their academic achievements, leadership, and commitments to addressing crucial global challenges, the MIT students are among 93 Siebel Scholars from 16 leading institutions in the United States, China, France, Italy, and Japan.
Siebel Scholars each receive an award of $35,000 to cover their final year of study. In addition, they will join a community of more than 1,400 past Siebel Scholars, including about 260 from MIT, who serve as advisors to the Thomas and Stacy Siebel Foundation and collaborate “to find solutions to society’s most pressing problems,” according to the foundation.
Past Siebel Scholars have launched more than 1,100 products, received at least 370 patents, published nearly 40 books, and founded at least 150 companies, among other achievements, according to the Siebel Scholars Foundation, which administers the program.
MIT’s 2020 class of Siebel Scholars includes:
- Katie Bacher, Department of Electrical Engineering and Computer Science
- Alexandra (Allie) Beizer, MIT Sloan School of Management
- Sarah Bening, Department of Biological Engineering
- Allison (Allie) Brouckman, MIT Sloan School of Management
- Enric Boix, Department of Electrical Engineering and Computer Science
- M. Doga Dogan, Department of Electrical Engineering and Computer Science
- Jared Kehe, Department of Biological Engineering
- Emma Kornetsky, MIT Sloan School of Management
- Kyungmi Lee, Department of Electrical Engineering and Computer Science
- Graham Leverick, Department of Mechanical Engineering
- Lauren Milling, Department of Biological Engineering
- Hans Nowak, MIT Sloan School of Management
- Lauren Stopfer, Department of Biological Engineering
- Jon Tham, Sloan School of Management
- Andrea Wallace, Department of Biological Engineering
- Clinton Wang, Department of Electrical Engineering and Computer Science
November 19, 2019 | More
Practicing for a voyage to Mars
If you want to make the long voyage to Mars, you first have to train and rehearse, and MIT LGO alumnus Barret Schlegelmilch SM ’18, MBA ’18 is doing just that. He recently commanded a 45-day practice mission living and working with three other would-be astronauts in a cramped simulated spaceship.
NASA’s Human Exploration Research Analog (HERA) analog mission “departed” last spring for a trip to Phobos, the larger of the two moons of Mars. It was the second of four planned missions to Phobos in the mock spacecraft located at the Johnson Space Center in Houston. The goal is to study the physiological and psychological effects of extended isolation and confinement, team dynamics, and conflict resolution.
While on the mission, Schlegelmilch and three other crew me
November 1, 2019 | More
New leadership for Bernard M. Gordon-MIT Engineering Leadership Program
Olivier de Weck, frequent LGO advisor, professor of aeronautics and astronautics and of engineering systems at MIT, has been named the new faculty co-director of the Bernard M. Gordon-MIT Engineering Leadership Program (GEL). He joins Reza Rahaman, who was appointed the Bernard M. Gordon-MIT Engineering Leadership Program industry co-director and senior lecturer on July 1, 2018.
“Professor de Weck has a longstanding commitment to engineering leadership, both as an educator and a researcher. I look forward to working with him and the GEL team as they continue to strengthen their outstanding undergraduate program and develop the new program for graduate students,” says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.
A leader in systems engineering, de Weck researches how complex human-made systems such as aircraft, spacecraft, automobiles, and infrastructures are designed, manufactured, and operated. By investigating their lifecycle properties, de Weck and members of his research group have developed a range of novel techniques broadly adopted by industry to maximize the value of these systems over time.
August 1, 2019 | More
Building the tools of the next manufacturing revolution
John Hart, an associate professor of mechanical engineering at MIT, LGO adviser, and the director of the Laboratory for Manufacturing and Productivity and the Center for Additive and Digital Advanced Production Technologies, is an expert in 3-D printing, also known as additive manufacturing, which involves the computer-guided deposition of material layer by layer into precise three-dimensional shapes. (Conventional manufacturing usually entails making a part by removing material, for example through machining, or by forming the part using a mold tool.)
Hart’s research includes the development of advanced materials — new types of polymers, nanocomposites, and metal alloys — and the development of novel machines and processes that use and shape materials, such as high-speed 3-D printing, roll-to-roll graphene growth, and manufacturing techniques for low-cost sensors and electronics.
June 19, 2019 | More
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
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
The top 10 MIT Sloan news stories of 2019
Toxic employees can have a huge impact on workplace morale, productivity, and turnover, but identifying toxic people can be difficult. Here are red flags to look for. It’s okay if your career path resembles a game of Chutes and Ladders, Apple VP Kate Bergeron told students during a talk loaded with career hacks.T
December 3, 2019 | More
AFL-CIO president: Engage workers as technologies evolve
As technology displaces workers and real wages emerge from a long period of stagnation, the future of work appears in flux. But AFL-CIO President Richard L. Trumka sees a way forward, detailed in a galvanizing speech on the future of work and labor Nov. 20 at MIT Sloan.
The AFL-CIO is the largest federation of independent unions worldwide, consisting of 55 national and international unions. It recently formed the AFL-CIO Commission on Work and Unions, designed to rethink the role of unions in the modern workforce.
Trumka offered a glimpse into that thinking.
November 25, 2019 | More
An 8-step guide for improving workplace processes
Most people know what it’s like to be overwhelmed at work: managing a chaotic to-do list and constant emails, developing a poor work/life balance, putting out fires, and responding to the loudest voice in the room.
“It’s easy to get caught up in a situation where you’re doing so much firefighting that you don’t ever have time to put out the fire permanently,” said Daniel Norton, EMBA ’19, and a co-founder of the software company LeanKit. “You don’t have time to make things better. All you’re doing is just getting up every day and trying to avoid disaster.”
This is a common scenario for knowledge-based workers. It’s difficult for workers to even acknowledge they are struggling, let alone find and fix the source of the problem. On the other hand, it is easy t
November 8, 2019 | More
New solar rebate study finds user savings but lack of competition
For years, solar leasing companies have faced claims of tricking customers out of their green subsidies. These companies, critics say, install their panels on homeowners’ rooftops, and keep most of the government rebate for themselves.
New research from an MIT Sloan economist pushes back against that notion.
MIT Sloan assistant professor Jacquelyn Pless and her co-author, Arthur A. van Benthem of The Wharton School, studied a California solar subsidy program and found that residents who leased solar panels saw a $1.53 reduction in price for every dollar of subsidy the leasing company col
October 29, 2019 | More
What is ‘data wrapping’ and how does it make products better?
How do companies derive value from data? After studying this question for 25 years, Barbara Wixom, a principal research scientist at the MIT Center for Information Systems Research, has boiled the answer down to three possibilities.
First, companies can use data to improve their processes. This is a very old practice. Second, companies can sell data. This method, too, has a rich history. Finally, companies can “wrap” their data around products and services. A tractor may be “wrapped” with a dashboard to monitor operational performance; a bank account may be “wrapped” with a budgeting tool; a cab trip may be “wrapped” with a fare estimator. These wraps can create indirect value by, say, increasing customer retention and wallet share or boosting customer satisfaction.
October 25, 2019 | More
The drastic power of a modest carbon tax
A recent study by an MIT Sloan professor finds that a federal carbon price of $7 per metric ton of carbon dioxide in 2020 could reduce emissions by the same amount as the flagship climate policies adopted by the Obama administration.
The study’s authors say that wouldn’t be enough to put the United States on a long-term path to decarbonation, but believe the results demonstrate the potential power of a carbon tax. Recent competing bills introduced in Congress, if passed and implem
October 17, 2019 | More
How to succeed in clean energy investing by really trying
Clean Energy Venture Group has invested in 39 companies since its start in 2005, and those companies have earned more than $500 million in follow-on investments from large venture capital firms and major energy companies. It counts businesses like energy software company MyEnergy — which was acquired by Nest (now part of Google) in 2013 — in its portfolio.
Not bad for an investment group that joined a high-risk industry just before the 2007-2009 recession — more than 90% of clean tech companies funded after 2007 failed to return initial capital.
October 16, 2019 | More
24 MIT startups to watch
A robotic bartender, smart clothing, a fantasy sports app, remote eye exams.
The wide-ranging ideas of this year’s MIT delta v cohort were on display during the Sept. 6 Demo Day in Cambridge, Massachusetts.
“Concepts don’t win here, ideas don’t win, it’s impact that wins,” said Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship.
Friday’s Demo Day was the culmination of delta v’s eighth cohort. Delta v runs through the summer, and takes place at the Trust Center. Seventeen teams were based at the Trust Center, while seven other teams worked out of the New York City cohort. This is the third year for the Manhattan-based accelerator.
September 11, 2019 | More
4 strategies for future-proofing your workforce
Equipping a company to excel in a changing business landscape isn’t just about technology — a successful company needs a digital-savvy workforce with the mindset to take on new challenges and embrace new ways of working.
To future-proof the workforce, companies are developing new performance, reward, and training strategies. Some embrace peer-led education while others make a game of digital knowledge. In other companies, preparing the workforce for the future means making sure digital natives are versed in business fundamentals.
Cookie-cutter approaches aren’t enough to transform companies, according to
September 9, 2019 | More
How a hybrid housing policy is opening doors to good neighborhoods
Jackie used short-term financial assistance to help pay the deposit on a new apartment for her and her 9-year-old son. With the help of a housing advocate, Dee, a mother of five, was able to compare neighborhood amenities in ways she’d never thought to do. Melinda received a list of property owners and landlords who ignored “Section 8 stereotypes” and were ready to work with her to find a new home for her and her 2-year-old son.
Through a combination of financial support and custom guidance and advocacy, these Washington state mothers were able to move to neighborhoods with more opportunities for their children. They’re three of the 420 families who participated in a regional housing mobility program that refutes the assumption that low-income families want to stay in low-opportunity areas.
September 4, 2019 | More
A new way to control microbial metabolism
Microbes can be engineered to produce a variety of useful compounds, including plastics, biofuels, and pharmaceuticals. However, in many cases, these products compete with the metabolic pathways that the cells need to fuel themselves and grow.
To help optimize cells’ ability to produce desired compounds but also maintain their own growth, MIT chemical engineers have devised a way to induce bacteria to switch between different metabolic pathways at different times. These switches are programmed into the cells and are triggered by changes in population density, with no need for human intervention.
“What we’re hoping is that this would allow more precise regulation of metabolism, to allow us to get higher productivity, but in a way where we minimize the number of interventions,” says Kristala Prather, the Arthur D. Little Professor of Chemical Engineering and the senior author of the study.
This kind of switching allowed the researchers to boost the microbial yields of two different products by up to tenfold.
MIT graduate student Christina Dinh is the lead author of the paper, which appears in the Proceedings of the National Academy of Sciences this week.
To make microbes synthesize useful compounds that they don’t normally produce, engineers insert genes for enzymes involved in the metabolic pathway — a chain of reactions that generate a specific product. This approach is now used to produce many complex products, such as pharmaceuticals and biofuels.
In some cases, intermediates produced during these reactions are also part of metabolic pathways that already exist in the cells. When cells divert these intermediates out of the engineered pathway, it lowers the overall yield of the end product.
Using a concept called dynamic metabolic engineering, Prather has previously built switches that help cells maintain the balance between their own metabolic needs and the pathway that produces the desired product. Her idea was to program the cells to autonomously switch between pathways, without the need for any intervention by the person operating the fermenter where the reactions take place.
In a study published in 2017, Prather’s lab used this approach to program E. coli to produce glucaric acid, a precursor to products such as nylons and detergents. The researchers’ strategy was based on quorum sensing, a phenomenon that bacterial cells normally use to communicate with each other. Each species of bacteria secretes particular molecules that help them sense nearby microbes and influence each other’s behavior.
The MIT team engineered their E. coli cells to secrete a quorum sensing molecule called AHL. When AHL concentrations reach a certain level, the cells shut off an enzyme that diverts a glucaric acid precursor into one of the cells’ own metabolic pathways. This allows the cells to grow and divide normally until the population is large enough to start producing large quantities of the desired product.
“That paper was the first to demonstrate that we could do autonomous control,” Prather says. “We could start the cultures going, and the cells would then sense when the time was right to make a change.”
In the new PNAS paper, Prather and Dinh set out to engineer multiple switching points into their cells, giving them a greater degree of control over the production process. To achieve that, they used two quorum sensing systems from two different species of bacteria. They incorporated these systems into E. coli that were engineered to produce a compound called naringenin, a flavonoid that is naturally found in citrus fruits and has a variety of beneficial health effects.
Using these quorum sensing systems, the researchers engineered two switching points into the cells. One switch was designed to prevent bacteria from diverting a naringenin precursor called malonyl-CoA into the cells’ own metabolic pathways. At the other switching point, the researchers delayed production of an enzyme in their engineered pathway, to avoid accumulating a precursor that normally inhibits the naringenin pathway if too much of the precursor accumulates.
“Since we took components from two different quorum sensing systems, and the regulator proteins are unique between the two systems, we can shift the switching time of each of the circuits independently,” Dinh says.
The researchers created hundreds of E. coli variants that perform these two switches at different population densities, allowing them to identify which one was the most productive. The best-performing strain showed a tenfold increase in naringenin yield over strains that didn’t have these control switches built in.
More complex pathways
The researchers also demonstrated that the multiple-switch approach could be used to double E. coli production of salicylic acid, a building block of many drugs. This process could also help improve yields for any other type of product where the cells have to balance between using intermediates for product formation or their own growth, Prather says. The researchers have not yet demonstrated that their method works on an industrial scale, but they are working on expanding the approach to more complex pathways and hope to test it at a larger scale in the future.
“We think it certainly has broader applicability,” Prather says. “The process is very robust because it doesn’t require someone to be present at a particular point in time to add something or make any sort of adjustment to the process, but rather allows the cells to be keeping track internally of when it’s time to make a shift.”
The research was funded by the National Science Foundation.
December 2, 2019 | More
Toward more efficient computing, with magnetic waves
MIT researchers have devised a novel circuit design that enables precise control of computing with magnetic waves — with no electricity needed. The advance takes a step toward practical magnetic-based devices, which have the potential to compute far more efficiently than electronics.
Classical computers rely on massive amounts of electricity for computing and data storage, and generate a lot of wasted heat. In search of more efficient alternatives, researchers have started designing magnetic-based “spintronic” devices, which use relatively little electricity and generate practically no heat.
Spintronic devices leverage the “spin wave” — a quantum property of electrons — in magnetic materials with a lattice structure. This approach involves modulating the spin wave properties to produce some measurable output that can be correlated to computation. Until now, modulating spin waves has required injected electrical currents using bulky components that can cause signal noise and effectively negate any inherent performance gains.
The MIT researchers developed a circuit architecture that uses only a nanometer-wide domain wall in layered nanofilms of magnetic material to modulate a passing spin wave, without any extra components or electrical current. In turn, the spin wave can be tuned to control the location of the wall, as needed. This provides precise control of two changing spin wave states, which correspond to the 1s and 0s used in classical computing. A paper describing the circuit design was published today in Science.
In the future, pairs of spin waves could be fed into the circuit through dual channels, modulated for different properties, and combined to generate some measurable quantum interference — similar to how photon wave interference is used for quantum computing. Researchers hypothesize that such interference-based spintronic devices, like quantum computers, could execute highly complex tasks that conventional computers struggle with.
“People are beginning to look for computing beyond silicon. Wave computing is a promising alternative,” says Luqiao Liu, a professor in the Department of Electrical Engineering and Computer Science (EECS) and principal investigator of the Spintronic Material and Device Group in the Research Laboratory of Electronics. “By using this narrow domain wall, we can modulate the spin wave and create these two separate states, without any real energy costs. We just rely on spin waves and intrinsic magnetic material.”
Joining Liu on the paper are Jiahao Han, Pengxiang Zhang, and Justin T. Hou, three graduate students in the Spintronic Material and Device Group; and EECS postdoc Saima A. Siddiqui.
Spin waves are ripples of energy with small wavelengths. Chunks of the spin wave, which are essentially the collective spin of many electrons, are called magnons. While magnons are not true particles, like individual electrons, they can be measured similarly for computing applications.
In their work, the researchers utilized a customized “magnetic domain wall,” a nanometer-sized barrier between two neighboring magnetic structures. They layered a pattern of cobalt/nickel nanofilms — each a few atoms thick — with certain desirable magnetic properties that can handle a high volume of spin waves. Then they placed the wall in the middle of a magnetic material with a special lattice structure, and incorporated the system into a circuit.
On one side of the circuit, the researchers excited constant spin waves in the material. As the wave passes through the wall, its magnons immediately spin in the opposite direction: Magnons in the first region spin north, while those in the second region — past the wall — spin south. This causes the dramatic shift in the wave’s phase (angle) and slight decrease in magnitude (power).
In experiments, the researchers placed a separate antenna on the opposite side of the circuit, that detects and transmits an output signal. Results indicated that, at its output state, the phase of the input wave flipped 180 degrees. The wave’s magnitude — measured from highest to lowest peak — had also decreased by a significant amount.
Adding some torque
Then, the researchers discovered a mutual interaction between spin wave and domain wall that enabled them to efficiently toggle between two states. Without the domain wall, the circuit would be uniformly magnetized; with the domain wall, the circuit has a split, modulated wave.
By controlling the spin wave, they found they could control the position of the domain wall. This relies on a phenomenon called, “spin-transfer torque,” which is when spinning electrons essentially jolt a magnetic material to flip its magnetic orientation.
In the researchers’ work, they boosted the power of injected spin waves to induce a certain spin of the magnons. This actually draws the wall toward the boosted wave source. In doing so, the wall gets jammed under the antenna — effectively making it unable to modulate waves and ensuring uniform magnetization in this state.
Using a special magnetic microscope, they showed that this method causes a micrometer-size shift in the wall, which is enough to position it anywhere along the material block. Notably, the mechanism of magnon spin-transfer torque was proposed, but not demonstrated, a few years ago. “There was good reason to think this would happen,” Liu says. “But our experiments prove what will actually occur under these conditions.”
The whole circuit is like a water pipe, Liu says. The valve (domain wall) controls how the water (spin wave) flows through the pipe (material). “But you can also imagine making water pressure so high, it breaks the valve off and pushes it downstream,” Liu says. “If we apply a strong enough spin wave, we can move the position of domain wall — except it moves slightly upstream, not downstream.”
Such innovations could enable practical wave-based computing for specific tasks, such as the signal-processing technique, called “fast Fourier transform.” Next, the researchers hope to build a working wave circuit that can execute basic computations. Among other things, they have to optimize materials, reduce potential signal noise, and further study how fast they can switch between states by moving around the domain wall. “That’s next on our to-do list,” Liu says.
November 28, 2019 | More
Forum addresses future of civil and environmental engineering education
Battling climate change and adapting communities to be ready for its effects on the world. Ensuring food and water security for an exploding population. Navigating ever-more congested urban landscapes.
These global concerns and others have been outlined by the National Academies and other institutions as imminent threats. One discipline in particular — civil and environmental engineering — has the history and capability to address these challenges on a large scale.
MIT’s Department of Civil and Environmental Engineering (CEE) took one of the first steps to address the question on how best to prepare a new generation of civil and environmental engineers by organizing a recent one-day workshop, entitled “CEE Education Frontiers Forum,” with invited leaders and educators from 10 leading U.S. institutions, including Stanford University, the University of California at Berkeley, Georgia Tech, and the University of Texas at Austin.
“The discipline of CEE is really at the cusp of a lot of things,” says Saurabh Amin, associate professor and undergraduate officer of CEE at MIT and one of the organizers of the event. “This exceptional group of universities is already addressing today’s challenges, but the field is changing so quickly that our educational efforts need to stay ahead of what we see CEE in need of.”
During her plenary talk, Anette “Peko” Hosoi, associate dean of engineering and professor of mechanical engineering at MIT, said that it’s not uncommon for engineering curricula to change over time. Each time the curriculum was revised, it was informed by the needs and constraints of that specific period of time, which drove the educational goals forward. Hosoi shared an historical survey of MIT engineering education which showed an overhauled curriculum approximately every 25 years to adjust to the needs of the day. From learning to design iron bridges in 1875 to electrifying the countryside in 1925, “the curriculum was turning over at a fairly rapid timescale,” she said.
“A lot of students come in wanting to change the world,” says Markus J. Buehler, department head of MIT CEE, the Jerry McAfee (1940) Professor in Engineering, and a forum co-organizer. “They want to address climate change, they want to address transportation, they want to address pollution. CEE offers a clear pathway through today’s curriculum to make contributions in these fields, and the workshop fostered discussion into how we can further strengthen our program and provide our students even better skills for their careers after graduation.”
The workshop consisted of two plenary talks, four panel discussions, and a lunch session, all focused on how to make sure CEE students understand and are well-prepared for their post-graduate opportunities, now and into the future. Two subjects addressed throughout the day were the value of an interdisciplinary education and an increased need for excellent interpersonal skills to prepare students for the real world.
As the need for a sustainable future becomes urgent, the required skillset of CEE graduates has also broadened. These skills include foundational knowledge in emerging fields such as computing and machine learning, as well as social responsibility and ethics, and leadership. For example, a well-trained civil or environmental engineer should be able to help design new solutions to make a city capable of withstanding rising sea levels associated with a changed climate, or create sustainable food, water, and energy supply chains. In an increasingly digitized world, speakers pointed out that CEE students should be able to incorporate key concepts such as data analytics and applications of artificial intelligence into their solutions.
“Civil and environmental engineers are defined by our applications … not our tools,” said Mark Stacey, department chair of CEE at UC Berkeley, during the panel on CEE Domains and Interdisciplinary Frontiers. “We bring tools from wherever they emerge.”
MIT’s CEE education is built around three central tenants: rigorous core knowledge of the science behind the discipline, fieldwork that allows students to gain insights into real-world problems, and labs designed to have students synthesize the knowledge and skills they have developed over their other coursework.
“Our curriculum is agile and designed to be adaptive to the needs of students and help them address these grand challenges,” says Amin. “The hope is that as new problems and areas of study arise, our students will be able to tackle whatever area they are interested in. We help students to tailor their coursework based on their individual goals and aspirations. This workshop identified some of the hurdles that may be coming and will help us in preparing for them.”
Nearly all in attendance agreed that the first year of a CEE curriculum was critical to demonstrate to students the possibilities of a future in the profession. One suggestion involved adding experience-based lab work during the first semester to engage students with the field from the get-go.
A similar educational reform is already underway at MIT through the Designing the First Year Experience initiative, headed by Vice Chancellor for Undergraduate and Graduate Education Ian Waitz. Waitz acknowledged the difficulty in changing a curriculum that has been in place for decades, but recognized the importance of addressing the educational and social needs of a changing student body.
“It’s not rocket science,” said Waitz during his talk. “It’s harder than that — it’s people science.”
For example, starting this year MIT CEE began offering new discovery subjects focusing on sustainable cities and climate change for first-year students. The goal is to bring these students into the discussions of grand challenges early on and equip them to make informed choices during their stay at MIT, and beyond.
A number of speakers also mentioned throughout the day that civil and environmental engineers are frequently at the center of civic problem-solving. They must be able to engage with the public, government officials, and engineers and scientists of other backgrounds. Any new curriculum should foster the ability to connect with people of different backgrounds to strengthen leadership skills. Panels on post-grad research opportunities by representatives from the National Science Foundation enforced this point.
Participants agreed the workshop was successful in moving the CEE education conversation forward.
“What we tried to do was … answer questions about what the CEE degree of the future would be,” says Desiree Plata, assistant professor in CEE at MIT and an event co-organizer. “[We] saw a lot of different opinions about that today, so that’s great for idea generation. I think there’s still a lot of work that needs to be done in terms of how to do it.”
The organizers plan to release a document summarizing the key points from the workshop to act as a jumping-off point for the next round of talks, and will seek further input from students and alumni.
“The next conversation should not start from scratch,” says Amin. “People will have their own hurdles at their own universities, but we believe that now is right time to lead this change.”
November 22, 2019 | More
Lamborghini and MIT pave the way for the electric supercar of the future
“He was here to dream, and I said ‘OK, let’s dream together,’” recalls Professor Mircea Dincă of his first encounter with Automobili Lamborghini Head of Development Riccardo Parenti in February 2017. Two years later, the team is celebrating its first major collaborative victory by filing a joint patent.
The new patented material was synthesized by Dincă’s lab in the Department of Chemistry, with the support of Automobili Lamborghini’s Concept Development Department, and will serve as the technological base for a new generation of supercapacitors. By increasing the surface area exposed to electric charge in relation to mass and volume, the patent promises to increase energy density by up to 100 percent when compared to existing technology. This is a big leap, even when compared to Lamborghini’s cutting-edge supercapacitors, and, more broadly, a game-changer in high-performance motor sport.
A second collaboration, with Professor A. John Hart’s team in the Department of Mechanical Engineering, pursues new design principles for high-performance battery materials that can be integrated into the vehicle structure, and is on schedule to deliver its first prototypes in the next year. Together, these collaborations are key in meeting the performance targets Lamborghini set for its Terzo Millennio car.
As Stefano Domenicali, chair and CEO of Automobili Lamborghini, puts it, “The joint research with MIT fully embodies our values and our vocation for anticipating the future: a future in which hybridization is increasingly desirable and inevitably necessary.”
Federica Sereni, consul general of Italy in Boston, Massachusetts, comments: “Italian companies, in particular those in the automotive industry, know how to combine passion, tradition, research, and innovation in a way that is unique in the world. Therefore, the match between Lamborghini and MIT is a perfect one, leading to an ideal combination between vision and a level of technological innovation that is among the most advanced in the world”.
Serenella Sferza, MIT-Italy Program co-director, concurs, praising the MIT-Italy-Lamborghini partnership as a perfect example of how, by acting as a bridge between MIT and Italy’s centers of excellence, the MIT-Italy Program opens avenues for research and innovation that include meaningful student experiences. In this case, after connecting Lamborghini to professors Dincă and Hart, Sferza also recruited mechanical engineering student Patricia Das ’17 and chemical engineering and chemistry student Angela Cai ’19, whose research at Santa Agata Bolognese cemented and advanced the Lamborghini-MIT collaboration.
“The Lamborghini-MIT Italy partnership exemplifies the range of MISTI activities and the symbiotic ways in which they feed on each other,” says Sferza. “I initially met Patricia and Angela when they applied to the MIT-Italy Global Teaching Labs program, and, based on their MIT academic background and their strong performance teaching STEM subjects at Italian high schools, later recruited them for the Lamborghini collaboration. Both earned high marks from Lamborghini, and learned a lot from the experience.”
“MIT-Italy has given me an invaluable chance to immerse myself in a research topic I am very passionate about in a professional setting with real, global applications,” shares Cai. “I have presented my findings and suggested future research direction to representatives from several departments at my host organization. When I finish my current work assignment, I plan on using the experience and connections gained here to pursue graduate study in this field.”
MIT-Italy and Lamborghini, the cornerstone partnership that paved the way for these initiatives, have extended their collaboration and plan to create additional student and research opportunities both on and off campus. In parallel with laboratory work, a campuswide motor sport hackathon is being considered.
“This has been such a fruitful partnership for us,” says Sferza. “There are few companies that exemplify the Italian talent for combining beautiful design with high-end technology in such a cool way. It is a joy to connect Lamborghini with MIT’s innovation community.”
The faculty, for their part, agree. “This collaboration presented us with the kind of challenges that we love at MIT. We like to understand that the work we’re doing in the lab can contribute to real, new, important technology and also have that work involve good science and engineering,” says Hart. “Our motto is ‘mind and hand,’ and this gets our minds to focus on a challenge and our hands to do something new and practical in the lab.”
“We were dreaming two years ago,” says Dincă. “Now, we really think this could be happening.”
November 19, 2019 | More
Materials Day talks examine the promises and challenges of AI and machine learning
The promises and challenges of artificial intelligence and machine learning highlighted the Oct. 9 MIT Materials Day Symposium, with presentations on new ways of forming zeolite compounds, faster drug synthesis, advanced optical devices, and more.
“Machine learning is having an impact in all areas of materials research,” Materials Research Laboratory Director Carl V. Thompson said.
“We’re increasingly able to work in tandem with machines to help us decide what materials to make,” said Elsa A. Olivetti, the Atlantic Richfield Associate Professor of Energy Studies. Machine learning is also guiding how to make those materials with new insights into synthesis methods, and, in some cases (such as with robotic systems), actually making those materials, she noted.
Keynote speaker Brian Storey, director of accelerated materials design and discovery at Toyota Research Institute, spoke about machine learning to advance the switch from the internal combustion engine to electric vehicles, and Professor Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, spoke about atomic engineering using elastic strain and radiation nudging of atoms.
Olivetti and Rafael Gomez-Bombarelli, the Toyota Assistant Professor in Materials Processing, worked together to apply machine learning to develop a better understanding of porous materials called zeolites, formed from silicon and aluminum oxide, that have a wide range of uses, from cat litter to petroleum refining.
“Essentially, the idea is that the pore has the right size to hold organic molecules,” Gomez-Bombarelli said. While only about 250 zeolites of this class are known to engineers, physicists can calculate hundreds of thousands of possible ways these structures can form. “Some of them can be converted into each other,” he said. “So, you could mine one zeolite, put it under pressure, or heat it up, and it becomes a different one that could be more valuable for a specific application.”
A traditional method was to interpret these crystalline structures as a combination of building blocks. However, when zeolite transformations were analyzed, more than half the time there were no building blocks in common between the original zeolite before the change and the new zeolite after the change. “Building block theory has some interesting ingredients, but doesn’t quite explain the rules to go from A to B,” Gomez-Bombarelli said.
Gomez-Bombarelli’s new graph-based approach finds that when each zeolite framework structure is represented as a graph, these graphs match before and after in zeolite transformation pairs. “Some classes of transformations only happen between zeolites that have the same graph,” he said.
This work evolved from Olivetti’s data mining of 2.5 million materials science journal articles to uncover recipes for making different inorganic materials. The zeolite study examined 70,000 papers. “One of the challenges in learning from the literature is we publish positive examples, we publish data of things that went well,” Olivetti said. In the zeolite community, researchers also publish what doesn’t work. “That’s a valuable dataset for us to learn from,” she said. “What we’ve been able to use this dataset for is to try to predict potential synthesis pathways for making particular types of zeolites.”
In earlier work with colleagues at the University of Massachusetts, Olivetti developed a system that identified common scientific words and techniques found in sentences across this large library and brought together similar findings. “One important challenge in natural language processing is to draw this linked information across a document,” Olivetti explained. “We are trying to build tools that are able to do that linking,” Olivetti says.
AI-assisted chemical synthesis
Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering and Professor of Materials Science and Engineering, described a chemical synthesis system that combines artificial intelligence-guided processing steps with a robotically operated modular reaction system.
For those unfamiliar with synthesis, Jensen explained that “You have reactants you start with, you have reagents that you have to add, catalysts and so forth to make the reaction go, you have intermediates, and ultimately you end up with your product.”
The artificial intelligence system combed 12.5 million reactions, creating a set of rules, or library, from about 160,000 of the most commonly used synthesis recipes, Jensen relates. This machine learning approach suggests processing conditions such as what catalysts, solvents, and reagents to use in the reaction.
“You can have the system take whatever information it got from the published literature about conditions and so on and you can use that to form a recipe,” he says. Because there is not enough data yet to inform the system, a chemical expert still needs to step in to specify concentrations, flow rates, and process stack configurations, and to ensure safety before sending the recipe to the robotic system.
The researchers demonstrated this system by predicting synthesis plans for 15 drugs or drug-like molecules — the painkiller lidocaine, for example, and several high blood pressure drugs — and then making them with the system. The flow reactor system contrasts with a batch system. “In order to be able to accelerate the reactions, we use typically much more aggressive conditions than are done in batch — high temperatures and higher pressures,” Jensen says.
The modular system consists of a processing tower with interchangeable reaction modules and a set of different reagents, which are connected together by the robot for each synthesis. These findings were reported in Science.
Former PhD students Connor W. Coley and Dale A. Thomas built the computer-aided synthesis planner and the flow reactor system, respectively, and former postdoc Justin A. M. Lummiss did the chemistry along with a large team of MIT Undergraduate Research Opportunity Program students, PhD students, and postdocs. Jensen also notes contributions from MIT faculty colleagues Regina Barzilay, William H. Green, A. John Hart, Tommi Jaakkola, and Tim Jamison. MIT has filed a patent for the robotic handling of fluid connections. The software suite that suggests and prioritizes possible synthesis routes is open source, and an online version is at the ASKCOS website.
Robustness in machine learning
Deep learning systems perform amazingly well on benchmark tasks such as images and natural language processing applications, said Professor Asu Ozdaglar, who heads MIT’s Department of Electrical Engineering and Computer Science. Still, researchers are far from understanding why these deep learning systems work, when they will work, and how they generalize. And when they get things wrong, they can go completely awry.
Ozdaglar gave an example of an image with a state-of-the-art classifier that can look at a picture of a cute pig and recognize the image as that of a pig. But, “If you add a little bit of, very little, perturbation, what happens is basically the same classifier thinks that’s an airliner,” Ozdaglar said. “So this is sort of an example where people say machine learning is so powerful, it can make pigs fly,” she said, accompanied by audience laughter. “And this immediately tells us basically we have to go beyond our standard approaches.”
A potential solution lies in an optimization formulation known as a Minimax, or MinMax, problem. Another place where MinMax formulation arises is in generative adversarial network, or GAN, training. Using an example of images of real cars and fake images of cars, Ozdaglar explained, “We would like these fake images to be drawn from the same distribution as the training set, and this is achieved using two neural networks competing with each other, a generator network and a discriminator network. The generator network creates from random noise these fake images that the discriminator network tries to pull apart to see whether this is real or fake.”
“It’s basically another MinMax problem whereby the generator is trying to minimize the distance between these two distributions, fake and real. And then the discriminator is trying to maximize that,” she said. The MinMax problem approach has become the backbone of robust training of deep learning systems, she noted.
Ozdaglar added that EECS faculty are applying machine learning to new areas, including health care, citing the work of Regina Barzilay in detecting breast cancer and David Sontag in using electronic medical records for medical diagnosis and treatment.
The EECS undergraduate machine learning course (6.036) hosted 800 students last spring, and consistently has 600 or more students enrolled, making it the most popular course at MIT. The new Stephen A. Schwarzman College of Computing provides an opportunity to create a more dynamic and adaptable structure than MIT’s traditional department structure. For example, one idea is to create several cross-departmental teaching groups. “We envision things like courses in the foundations of computing, computational science and engineering, social studies of computing, and have these courses taken by all of our students taught jointly by our faculty across MIT,” she said.
Juejun “JJ” Hu, associate professor of materials science and engineering, detailed his research coupling a silicon chip-based spectrometer for detecting infrared light wavelengths to a newly created machine learning algorithm. Ordinary spectrometers, going back to Isaac Newton’s first prism, work by splitting light, which reduces intensity, but Hu’s version collects all of the light at a single detector, which preserves light intensity but then poses the problem of identifying different wavelengths from a single capture.
“If you want to solve this trade-off between the (spectral) resolution and the signal-to-noise ratio, what you have to do is resort to a new type of spectroscopy tool called wavelength multiplexing spectrometer,” Hu said. His new spectrometer architecture, which is called digital Fourier transform spectroscopy, incorporates tunable optical switches on a silicon chip. The device works by measuring the intensity of light at different optical switch settings and comparing the results. “What you have is essentially a group of linear equations that gives you some linear combination of the light intensity at different wavelengths in the form of a detector reading,” he said.
A prototype device with six switches supports a total of 64 unique optical states, which can provide 64 independent readings. “The advantage of this new device architecture is that the performance doubles every time you add a new switch,” he said. Working with Brando Miranda at the Center for Brains Minds and Machines at MIT, he developed a new algorithm, Elastic D1, that gives a resolution down to 0.2 nanometers and gives an accurate light measurement with only two consecutive measurements.
“We believe this kind of unique combination between the hardware of a new spectrometer architecture and the algorithm can enable a wide range of applications ranging from industrial process monitoring to medical imaging,” Hu said. Hu also is applying machine learning in his work on complex optical media such as metasurfaces, which are new optical devices featuring an array of specially designed optical antennas that add a phase delay to the incoming light.
Poster session winners
Nineteen MIT postdocs and graduate students gave two-minute talks about their research during a poster session preview. At the Materials Day Poster Session immediately following the symposium, award winners were mechanical engineering graduate student Erin Looney, media arts and sciences graduate student Bianca Datta, and materials science and engineering postdoc Michael Chon.
The Materials Research Laboratory serves interdisciplinary groups of faculty, staff, and students, supported by industry, foundations, and government agencies to carry out fundamental engineering research on materials. Research topics include energy conversion and storage, quantum materials, spintronics, photonics, metals, integrated microsystems, materials sustainability, solid-state ionics, complex oxide electronic properties, biogels, and functional fibers.
November 5, 2019 | More
Technique helps robots find the front door
In the not too distant future, robots may be dispatched as last-mile delivery vehicles to drop your takeout order, package, or meal-kit subscription at your doorstep — if they can find the door.
Standard approaches for robotic navigation involve mapping an area ahead of time, then using algorithms to guide a robot toward a specific goal or GPS coordinate on the map. While this approach might make sense for exploring specific environments, such as the layout of a particular building or planned obstacle course, it can become unwieldy in the context of last-mile delivery.
Imagine, for instance, having to map in advance every single neighborhood within a robot’s delivery zone, including the configuration of each house within that neighborhood along with the specific coordinates of each house’s front door. Such a task can be difficult to scale to an entire city, particularly as the exteriors of houses often change with the seasons. Mapping every single house could also run into issues of security and privacy.
Now MIT engineers have developed a navigation method that doesn’t require mapping an area in advance. Instead, their approach enables a robot to use clues in its environment to plan out a route to its destination, which can be described in general semantic terms, such as “front door” or “garage,” rather than as coordinates on a map. For example, if a robot is instructed to deliver a package to someone’s front door, it might start on the road and see a driveway, which it has been trained to recognize as likely to lead toward a sidewalk, which in turn is likely to lead to the front door.
The new technique can greatly reduce the time a robot spends exploring a property before identifying its target, and it doesn’t rely on maps of specific residences.
“We wouldn’t want to have to make a map of every building that we’d need to visit,” says Michael Everett, a graduate student in MIT’s Department of Mechanical Engineering. “With this technique, we hope to drop a robot at the end of any driveway and have it find a door.”
Everett will present the group’s results this week at the International Conference on Intelligent Robots and Systems. The paper, which is co-authored by Jonathan How, professor of aeronautics and astronautics at MIT, and Justin Miller of the Ford Motor Company, is a finalist for “Best Paper for Cognitive Robots.”
“A sense of what things are”
In recent years, researchers have worked on introducing natural, semantic language to robotic systems, training robots to recognize objects by their semantic labels, so they can visually process a door as a door, for example, and not simply as a solid, rectangular obstacle.
“Now we have an ability to give robots a sense of what things are, in real-time,” Everett says.
Everett, How, and Miller are using similar semantic techniques as a springboard for their new navigation approach, which leverages pre-existing algorithms that extract features from visual data to generate a new map of the same scene, represented as semantic clues, or context.
In their case, the researchers used an algorithm to build up a map of the environment as the robot moved around, using the semantic labels of each object and a depth image. This algorithm is called semantic SLAM (Simultaneous Localization and Mapping).
While other semantic algorithms have enabled robots to recognize and map objects in their environment for what they are, they haven’t allowed a robot to make decisions in the moment while navigating a new environment, on the most efficient path to take to a semantic destination such as a “front door.”
“Before, exploring was just, plop a robot down and say ‘go,’ and it will move around and eventually get there, but it will be slow,” How says.
The cost to go
The researchers looked to speed up a robot’s path-planning through a semantic, context-colored world. They developed a new “cost-to-go estimator,” an algorithm that converts a semantic map created by preexisting SLAM algorithms into a second map, representing the likelihood of any given location being close to the goal.
“This was inspired by image-to-image translation, where you take a picture of a cat and make it look like a dog,” Everett says. “The same type of idea happens here where you take one image that looks like a map of the world, and turn it into this other image that looks like the map of the world but now is colored based on how close different points of the map are to the end goal.”
This cost-to-go map is colorized, in gray-scale, to represent darker regions as locations far from a goal, and lighter regions as areas that are close to the goal. For instance, the sidewalk, coded in yellow in a semantic map, might be translated by the cost-to-go algorithm as a darker region in the new map, compared with a driveway, which is progressively lighter as it approaches the front door — the lightest region in the new map.
The researchers trained this new algorithm on satellite images from Bing Maps containing 77 houses from one urban and three suburban neighborhoods. The system converted a semantic map into a cost-to-go map, and mapped out the most efficient path, following lighter regions in the map, to the end goal. For each satellite image, Everett assigned semantic labels and colors to context features in a typical front yard, such as grey for a front door, blue for a driveway, and green for a hedge.
During this training process, the team also applied masks to each image to mimic the partial view that a robot’s camera would likely have as it traverses a yard.
“Part of the trick to our approach was [giving the system] lots of partial images,” How explains. “So it really had to figure out how all this stuff was interrelated. That’s part of what makes this work robustly.”
The researchers then tested their approach in a simulation of an image of an entirely new house, outside of the training dataset, first using the preexisting SLAM algorithm to generate a semantic map, then applying their new cost-to-go estimator to generate a second map, and path to a goal, in this case, the front door.
The group’s new cost-to-go technique found the front door 189 percent faster than classical navigation algorithms, which do not take context or semantics into account, and instead spend excessive steps exploring areas that are unlikely to be near their goal.
Everett says the results illustrate how robots can use context to efficiently locate a goal, even in unfamiliar, unmapped environments.
“Even if a robot is delivering a package to an environment it’s never been to, there might be clues that will be the same as other places it’s seen,” Everett says. “So the world may be laid out a little differently, but there’s probably some things in common.”
This research is supported, in part, by the Ford Motor Company.
November 4, 2019 | More
From 3D-printed limbs to semi-autonomous race cars
In early October, the MIT International Design Center and the MIT Edgerton Center hosted a panel discussion on “Envisioning the Future of Technology-Enabled Mobility.”
Moderated by Edgerton Center Director and Professor of Ocean and Mechanical Engineering J. Kim Vandiver, panelists included Robert Bond, chief technology officer of MIT Lincoln Laboratory; Dan Frey, professor of mechanical engineering and MIT D-Lab faculty research director; Neville Hogan, the Sun Jae Professor of Mechanical Engineering; and Jaya Narain, PhD candidate in mechanical engineering at the Fluid Interfaces Group in the MIT Media Lab.
Also on the panel was Sam Schmidt, a professional IndyCar driver paralyzed from the shoulders down after a racing crash. He thought he’d never drive again. But he did.
Schmidt attained a top speed of 192 mph on a jet runway driving a modified Chevrolet Corvette, the SAM car (Semi-Autonomous Motorcar), developed by Arrow Electronics. Wearing a headset connected to infrared cameras that detected his head rotation, Schmidt steered. He used a sip-and-puff device to accelerate and brake.
Harmonizing human and robotic movement
According to Bond, one of the exoskeleton technology breakthroughs may soon be the integration of machine learning and microelectronics. “But it’s also going to require a new actuator and new sensing technologies so that we can use machine learning to anticipate motion of the human, and then have the exoskeleton move in harmony with the human,” Bond said.
“Earlier skeletal types of technologies were very awkward for people to use. And they almost worked against the human as they were trying to use it,” Bond added.
One of the challenges “in developing these technologies — for example, with exoskeletal devices — they have to respect what the human does, the natural cadences of human movement,” said Hogan. Exoskeletal devices, for instance, need to get the right mix of technology and human movement. To demonstrate, Hogan asked the audience to move their arm from point A to point B in exactly 60 seconds, essentially “roboticizing” their arm movement. It was impossible.
Narain, who as an undergraduate co-founded ATHack, a two-week annual hackathon focused on assistive technologies, noted that the movement toward “do-it-yourself” technologies has played a valuable role in creating solutions for very specific needs.
Simple projects — rearview cameras for electric wheelchairs, braking mechanisms for walkers — have been built in hackathons, but “with 3D printers and Arduinos and things like Google’s core app for machine learning, brain-computer interfaces, I think it’s going to become a lot more feasible for people to kind of start taking technology and developing it for themselves and people they know,” said Narain.
What Narain finds inspiring is when students build relationships with the assistive-technology user. Students meet co-designers who propose a project. Students visit them at home, at work, where they’re going to use the technology. “Maybe it’s a basketball court. Maybe it’s work. And when they have that rapport and that emotional connection, we found that those are the students who tend to stay in the space and continue with the project and other similar projects,” Narain said.
Frey confirmed the sentiment. “If you present problems to students that are technologically challenging and socially relevant, the rest takes care of itself,” he said.
“Basically, you have to beat them off with sticks if there’s social relevance there … there’s no problem attracting students,” said Hogan.
Schmidt notes that “maybe only 10 percent or 15 percent of the population of people with disabilities can afford a $60,000 [semi-autonomous] minivan … There’s a lot of people not getting out of their houses because of the limitations.”
Vandiver, who had visited Jaipur Foot in India, a maker of prostheses with a reported 1.78 million beneficiaries, asked, “How do we see that people who live on the extreme affordability side of the world benefit from some of the things that we’re thinking about here?”
Frey pointed out “that a huge proportion of this planet cannot pay a lot for the technologies. And the vast majority of all commercial engineering is focused on relatively few people.”
One of the Lincoln Laboratory projects was challenging people to build prosthetics using 3D printers, “feet and hands and things of that sort. And what they kind of stumbled onto was, for young children who are growing up that need a limb, basically, they’re growing and growing and growing. And they can’t afford to continually replace that limb. But if you can codify a scalable and quickly manufacturable [one] with a 3D-printed prosthetic, they can just go print a new one a month later that fits them again,” said Bond.
“It’s not the best. It doesn’t perform as well as the really high-tech ones,” Bond added. “But you get to refit it every month if you need to. So we should be thinking about how these new manufacturing technologies can just help us in doing things that might seem rather simple, but I think could have huge impact.”
Toward the future
Frey suggested using a related technology with a large market, such as cellphones, as the core of assistive technology. “As in, find something that already has a big market and kind of piggyback onto it,” he said.
The event gave everyone an opportunity to network and consider ways to collaborate further; many numbers were exchanged. And, at the end, everyone had the chance to look under the hood of semi-autonomous technology in action — the SAM car parked in the Edgerton Center’s Area 51 garage.
November 1, 2019 | More
Two-legged robot mimics human balance while running and jumping
Rescuing victims from a burning building, a chemical spill, or any disaster that is inaccessible to human responders could one day be a mission for resilient, adaptable robots. Imagine, for instance, rescue-bots that can bound through rubble on all fours, then rise up on two legs to push aside a heavy obstacle or break through a locked door.
Engineers are making strides on the design of four-legged robots and their ability to run, jump and even do backflips. But getting two-legged, humanoid robots to exert force or push against something without falling has been a significant stumbling block.
Now engineers at MIT and the University of Illinois at Urbana-Champaign have developed a method to control balance in a two-legged, teleoperated robot — an essential step toward enabling a humanoid to carry out high-impact tasks in challenging environments.
The team’s robot, physically resembling a machined torso and two legs, is controlled remotely by a human operator wearing a vest that transmits information about the human’s motion and ground reaction forces to the robot.
Through the vest, the human operator can both direct the robot’s locomotion and feel the robot’s motions. If the robot is starting to tip over, the human feels a corresponding pull on the vest and can adjust in a way to rebalance both herself and, synchronously, the robot.
In experiments with the robot to test this new “balance feedback” approach, the researchers were able to remotely maintain the robot’s balance as it jumped and walked in place in sync with its human operator.
“It’s like running with a heavy backpack — you can feel how the dynamics of the backpack move around you, and you can compensate properly,” says Joao Ramos, who developed the approach as an MIT postdoc. “Now if you want to open a heavy door, the human can command the robot to throw its body at the door and push it open, without losing balance.”
Ramos, who is now an assistant professor at the University of Illinois at Urbana-Champaign, has detailed the approach in a study appearing today in Science Robotics. His co-author on the study is Sangbae Kim, associate professor of mechanical engineering at MIT.
More than motion
Previously, Kim and Ramos built the two-legged robot HERMES (for Highly Efficient Robotic Mechanisms and Electromechanical System) and developed methods for it to mimic the motions of an operator via teleoperation, an approach that the researchers say comes with certain humanistic advantages.
“Because you have a person who can learn and adapt on the fly, a robot can perform motions that it’s never practiced before [via teleoperation],” Ramos says.
In demonstrations, HERMES has poured coffee into a cup, wielded an ax to chop wood, and handled an extinguisher to put out a fire.
All these tasks have involved the robot’s upper body and algorithms to match the robot’s limb positioning with that of its operator’s. HERMES was able to carry out high-impact motions because the robot was rooted in place. Balance, in these cases, was much simpler to maintain. If the robot were required to take any steps, however, it would have likely tipped over in attempting to mimic the operator’s motions.
“We realized in order to generate high forces or move heavy objects, just copying motions wouldn’t be enough, because the robot would fall easily,” Kim says. “We needed to copy the operator’s dynamic balance.”
Enter Little HERMES, a miniature version of HERMES that is about a third the size of an average human adult. The team engineered the robot as simply a torso and two legs, and designed the system specifically to test lower-body tasks, such as locomotion and balance. As with its full-body counterpart, Little HERMES is designed for teleoperation, with an operator suited up in a vest to control the robot’s actions.
For the robot to copy the operator’s balance rather than just their motions, the team had to first find a simple way to represent balance. Ramos eventually realized that balance could be stripped down to two main ingredients: a person’s center of mass and their center of pressure — basically, a point on the ground where a force equivalent to all supporting forces is exerted.
The location of the center of mass in relation to the center of pressure, Ramos found, relates directly to how balanced a person is at any given time. He also found that the position of these two ingredients could be physically represented as an inverted pendulum. Imagine swaying from side to side while staying rooted to the same spot. The effect is similar to the swaying of an upside-down pendulum, the top end representing a human’s center of mass (usually in the torso) and the bottom representing their center of pressure on the ground.
To define how center of mass relates to center of pressure, Ramos gathered human motion data, including measurements in the lab, where he swayed back and forth, walked in place, and jumped on a force plate that measured the forces he exerted on the ground, as the position of his feet and torso were recorded. He then condensed this data into measurements of the center of mass and the center of pressure, and developed a model to represent each in relation to the other, as an inverted pendulum.
He then developed a second model, similar to the model for human balance but scaled to the dimensions of the smaller, lighter robot, and he developed a control algorithm to link and enable feedback between the two models.
The researchers tested this balance feedback model, first on a simple inverted pendulum that they built in the lab, in the form of a beam about the same height as Little HERMES. They connected the beam to their teleoperation system, and it swayed back and forth along a track in response to an operator’s movements. As the operator swayed to one side, the beam did likewise — a movement that the operator could also feel through the vest. If the beam swayed too far, the operator, feeling the pull, could lean the other way to compensate, and keep the beam balanced.
The experiments showed that the new feedback model could work to maintain balance on the beam, so the researchers then tried the model on Little HERMES. They also developed an algorithm for the robot to automatically translate the simple model of balance to the forces that each of its feet would have to generate, to copy the operator’s feet.
In the lab, Ramos found that as he wore the vest, he could not only control the robot’s motions and balance, but he also could feel the robot’s movements. When the robot was struck with a hammer from various directions, Ramos felt the vest jerk in the direction the robot moved. Ramos instinctively resisted the tug, which the robot registered as a subtle shift in the center of mass in relation to center of pressure, which it in turn mimicked. The result was that the robot was able to keep from tipping over, even amidst repeated blows to its body.
Little HERMES also mimicked Ramos in other exercises, including running and jumping in place, and walking on uneven ground, all while maintaining its balance without the aid of tethers or supports.
“Balance feedback is a difficult thing to define because it’s something we do without thinking,” Kim says. “This is the first time balance feedback is properly defined for the dynamic actions. This will change how we control a teleoperated humanoid.”
Kim and Ramos will continue to work on developing a full-body humanoid with similar balance control, to one day be able to gallop through a disaster zone and rise up to push away barriers as part of rescue or salvage missions.
“Now we can do heavy door opening or lifting or throwing heavy objects, with proper balance communication,” Kim says.
This research was supported, in part, by Hon Hai Precision Industry Co. Ltd. and Naver Labs Corporation.
October 30, 2019 | More
Eric Alm and Peter Dedon receive NIH Transformative Research Award
Two MIT faculty members from the School of Engineering, Professor Eric Alm and Professor Peter Dedon, are among the recipients of the 2019 Transformative Research Award for studying how DNA modifications — the epigenome — affect microbial populations in the gut. Their work could pave the way for future developments in disease diagnosis and treatment.
The National Institute of Health Director’s Transformative Research Award is part of the Common Fund’s High-Risk, High-Reward Research program, which was created to accelerate the pace of biomedical, behavioral, and social science discoveries by supporting exceptionally creative scientists with highly innovative research. The Transformative Research Award promotes cross-cutting, interdisciplinary approaches and is open to individuals and teams of investigators who propose research that could potentially create or challenge existing paradigms.
Eric Alm, professor of biological engineering and of civil and environmental engineering; director of MIT’s Center for Microbiome Informatics and Therapeutics; and associate member of the Broad Institute of MIT and Harvard, has developed many of the standard tools and algorithms used to identify bacteria in the human microbiome. His research group has focused on translating microbiome science into new therapeutic options for patients.
Peter Dedon, the Singapore Professor of Biological Engineering, lead principal investigator in the Singapore-MIT Alliance for Research and Technology Antimicrobial Drug Resistance Program, and a member of the MIT Center for Environmental Health Sciences, has pioneered the development of systems-level bioanalytical and informatic tools for discovering epigenetic and epitranscriptomic mechanisms in infectious disease and cancer.
The Transformative Research Award will allow Dedon and Alm to combine their expertise on a project that spans five years. In 2007, the Dedon lab discovered that many human gut bacteria contain special DNA modifications known as phosphorothioates. Now, Dedon and Alm will define how bacteria with these modifications are affected by inflammatory bowel disease. The team will use new tools to first identify all of the gut bacteria containing phosphorothioates. They will then determine how these modifications affect bacterial populations in people suffering from Crohn’s disease and ulcerative colitis. This understanding could help scientists develop new medical treatments. Dedon and Alm also propose to use these technologies to explore the diversity of other DNA modifications in microbiome bacteria and bacterial viruses, and their associations with disease.
This year, the National Institute of Health awarded 93 grants through its High-Risk, High-Reward Research Program, totaling $267 million over five years. The awards support innovative research projects that have the potential to result in major scientific breakthroughs.
October 24, 2019 | More
What a little more computing power can do
Neural networks have given researchers a powerful tool for looking into the future and making predictions. But one drawback is their insatiable need for data and computing power (“compute”) to process all that information. At MIT, demand for compute is estimated to be five times greater than what the Institute can offer. To help ease the crunch, industry has stepped in. An $11.6 million supercomputer recently donated by IBM comes online this fall, and in the past year, both IBM and Google have provided cloud credits to MIT Quest for Intelligence for distribution across campus. Four projects made possible by IBM and Google cloud donations are highlighted below.
Smaller, faster, smarter neural networks
To recognize a cat in a picture, a deep learning model may need to see millions of photos before its artificial neurons “learn” to identify a cat. The process is computationally intensive and carries a steep environmental cost, as new research attempting to measure artificial intelligence’s (AI’s) carbon footprint has highlighted.
But there may be a more efficient way. New MIT research shows that models only a fraction of the size are needed. “When you train a big network there’s a small one that could have done everything,” says Jonathan Frankle, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS).
With study co-author and EECS Professor Michael Carbin, Frankle estimates that a neural network could get by with on-tenth the number of connections if the right subnetwork is found at the outset. Normally, neural networks are trimmed after the training process, with irrelevant connections removed then. Why not train the small model to begin with, Frankle wondered?
Experimenting with a two-neuron network on his laptop, Frankle got encouraging results and moved to larger image-datasets like MNIST and CIFAR-10, borrowing GPUs where he could. Finally, through IBM Cloud, he secured enough compute power to train a real ResNet model. “Everything I’d done previously was toy experiments,” he says. “I was finally able to run dozens of different settings to make sure I could make the claims in our paper.”
Frankle spoke from Facebook’s offices, where he worked for the summer to explore ideas raised by his Lottery Ticket Hypothesis paper, one of two picked for a best paper award at this year’s International Conference on Learning Representations. Potential applications for the work go beyond image classification, Frankle says, and include reinforcement learning and natural language processing models. Already, researchers at Facebook AI Research, Princeton University, and Uber have published follow-on studies.
“What I love about neural networks is we haven’t even laid the foundation yet,” says Frankle, who recently shifted from studying cryptography and tech policy to AI. “We really don’t understand how it learns, where it’s good and where it fails. This is physics 1,000 years before Newton.”
Distinguishing fact from fake news
Networking platforms like Facebook and Twitter have made it easier than ever to find quality news. But too often, real news is drowned out by misleading or outright false information posted online. Confusion over a recent video of U.S. House Speaker Nancy Pelosi doctored to make her sound drunk is just the latest example of the threat misinformation and fake news pose to democracy.
“You can put just about anything up on the internet now, and some people will believe it,” says Moin Nadeem, a senior and EECS major at MIT.
If technology helped create the problem, it can also help fix it. That was Nadeem’s reason for picking a superUROP project focused on building an automated system to fight fake and misleading news. Working in the lab of James Glass, a researcher at MIT’s Computer Science and Artificial Intelligence Laboratory, and supervised by Mitra Mohtarami, Nadeem helped train a language model to fact-check claims by searching through Wikipedia and three types of news sources rated by journalists as high-quality, mixed-quality or low-quality.
To verify a claim, the model measures how closely the sources agree, with higher agreement scores indicating the claim is likely true. A high disagreement score for a claim like, “ISIS infiltrates the United States,” is a strong indicator of fake news. One drawback of this method, he says, is that the model doesn’t identify the independent truth so much as describe what most people think is true.
With the help of Google Cloud Platform, Nadeem ran experiments and built an interactive website that lets users instantly assess the accuracy of a claim. He and his co-authors presented their results at the North American Association of Computational Linguistics (NAACL) conference in June and are continuing to expand on the work.
“The saying used to be that seeing is believing,” says Nadeem, in this video about his work. “But we’re entering a world where that isn’t true. If people can’t trust their eyes and ears it becomes a question of what can we trust?”
Visualizing a warming climate
From rising seas to increased droughts, the effects of climate change are already being felt. A few decades from now, the world will be a warmer, drier, and more unpredictable place. Brandon Leshchinskiy, a graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro), is experimenting with generative adversarial networks, or GANs, to imagine what Earth will look like then.
GANs produce hyper-realistic imagery by pitting one neural network against another. The first network learns the underlying structure of a set of images and tries to reproduce them, while the second decides which images look implausible and tells the first network to try again.
Inspired by researchers who used GANs to visualize sea-level rise projections from street-view images, Leshchinskiy wanted to see if satellite imagery could similarly personalize climate projections. With his advisor, AeroAstro Professor Dava Newman, Leshchinskiy is currently using free IBM Cloud credits to train a pair of GANs on images of the eastern U.S. coastline with their corresponding elevation points. The goal is to visualize how sea-level rise projections for 2050 will redraw the coastline. If the project works, Leshinskiy hopes to use other NASA datasets to imagine future ocean acidification and changes in phytoplankton abundance.
“We’re past the point of mitigation,” he says. “Visualizing what the world will look like three decades from now can help us adapt to climate change.”
Identifying athletes from a few gestures
A few moves on the field or court are enough for a computer vision model to identify individual athletes. That’s according to preliminary research by a team led by Katherine Gallagher, a researcher at MIT Quest for Intelligence.
The team trained computer vision models on video recordings of tennis matches and soccer and basketball games and found that the models could recognize individual players in just a few frames from key points on their body providing a rough outline of their skeleton.
The team used a Google Cloud API to process the video data, and compared their models’ performance against models trained on Google Cloud’s AI platform. “This pose information is so distinctive that our models can identify players with accuracy almost as good as models provided with much more information, like hair color and clothing,” she says.
Their results are relevant for automated player identification in sports analytics systems, and they could provide a basis for further research on inferring player fatigue to anticipate when players should be swapped out. Automated pose detection could also help athletes refine their technique by allowing them to isolate the precise moves associated with a golfer’s expert drive or a tennis player’s winning swing.
September 16, 2019 | More