Artificial Intelligence

For most people, the task of identifying an object, picking it up, and placing it somewhere else is trivial. For robots, it requires the latest in machine intelligence and robotic manipulation.

That’s what MIT spinoff RightHand Robotics has incorporated into its robotic piece-picking systems, which combine unique gripper designs with artificial intelligence and machine vision to help companies sort products and get orders out the door.

“If you buy something at the store, you push the cart down the aisle and pick it yourself. When you order online, there is an equivalent operation inside a fulfillment center,” says RightHand Robotics co-founder Lael Odhner ’04, SM ’06, PhD ’09. “The retailer typically needs to pick up single items, run them through a scanner, and put them into a sorter or conveyor belt to complete the order. It sounds easy until you imagine tens of thousands of orders a day and more than 100,000 unique products stored in a facility the size of 10 or 20 football fields, with the delivery expectation clock ticking.”

RightHand Robotics is helping companies respond to two broad trends that have transformed retail operations. One is the explosion of e-commerce, which only accelerated during the Covid-19 pandemic. The other is a shift to just-in-time inventory fulfillment, in which pharmacies, grocery stores, and apparel companies restock items based on what’s been purchased that day or week to improve efficiency.

The robot fleet also collects data that help RightHand Robotics improve its system over time and enable it to learn new skills, such as more gentle or precise placement. Process and performance data feed into the company’s fleet management software, which can help customers understand how their inventory moves through the warehouse and identify bottlenecks or quality problems.

“The idea is that rather than looking at just the performance of a single operation, e-commerce firms can modify or overhaul the operational flow throughout the warehouse,” Odhner says. “The goal is to eliminate variability as far upstream as is feasible, making a simpler, streamlined process.”

Pushing the limit

Odhner completed his PhD in the lab of Harry Asada, MIT’s Ford Professor of Engineering in the Department of Mechanical Engineering, who Odhner says encouraged students to develop a broad familiarity with robotics research. Colleagues also frequently shared their work in seminars, giving Odhner a well-rounded view of the field.

“Asada is a very well-known robotics researcher, and his early work, as well as the projects I worked on with him, are very much fundamental to what we’re doing at RightHand Robotics,” Odhner says.

In 2009, Odhner was part of the winning team in the DARPA Autonomous Robotic and Manipulation Challenge. Many of the competing teams had MIT connections, and the entire program was eventually run by former MIT associate professor Gill Pratt. After making the semifinals of the MIT 100K competition in 2013 as “Manus Robotics,” the team was introduced to Mick Mountz ’87, founder of Kiva Systems (later acquired by Amazon), who encouraged the team to look at applications in supply chain and logistics.

Today, a significant amount of RightHand Robotics employees and leadership come from MIT. MIT researchers also accounted for many early customers, buying components Odhner’s team had invented during the DARPA program.

“Generally, we’ve been in such close proximity to MIT that it’s hard to avoid circling back there,” Odhner says. “It’s kind of a family. You don’t ever really leave MIT.”

At the core of the RightH and Robotics solution is the idea of using machine vision and intelligent grippers to make piece-picking robots more adaptable. The combination also limits the amount of training needed to run the robots, equipping each machine with what the company equates to hand-eye coordination.

“The technical part of what we do is we have to look at an unstructured presentation of consumer goods and semantically understand what’s in there,” Odhner says.

RightHand Robotics also utilizes an end-of-arm tool that combines suction with novel underactuated fingers, which Odhner says gives the robots more flexibility than robots relying solely on suction cups or simple pinching grippers.

“Sometimes it actually helps you to have passive degrees of freedom in your hand, passive motions that it can make and can’t actively control,” Odhner says of the robots. “Very often those simplify the control task. They take problems from being heavily over-constrained and make them tractable to run through a motion planning algorithm.”

The data the robots collect are also used to improve reliability over time and shed light on warehouse operations for customers.

“We can give people insights into their inventory, insights into how they’re storing their inventory, how they’re structuring tasks both upstream and downstream of any picking we’re doing,” Odhner says. “We have very good insight as to what may be a source of future problems, and we can feed that back to customers.”

Odhner notes that warehouse fulfillment could grow to be a much larger industry if throughput were improved.

“As consumers increasingly value the option of shopping online, more and more items need to get into a growing number of ‘virtual’ carts. The availability of people near order fulfillment centers tends to be a limiting factor for e-commerce growth. All of that is really indicative of a massive economic inefficiency, and that’s essentially what we’re trying to address,” Odhner says. “We are taking the least engaging tasks in the warehouse — things like sorter induction, where you’re just picking, scanning, and putting something on a belt all day long — and we’re working to automate those tasks to the point where you can take your people and you can direct them to things that are going to be more directly felt by the customer.”

Odhner also says more automated fulfillment centers offer improved measures to protect worker health and safety, such as ergonomic stations where goods are brought to workers for specialized tasks and increased social distancing. Rather than reducing the number of people employed in a warehouse, he says, “Ultimately, what you want is a system with people working in roles like quality control, overseeing the robots.”

Robots made easy

This year, the company is introducing the third version of its picking robot, which ships with standardized integration and safety features in an attempt to make deploying piece-picking robots easier for warehouse operators.

“People may not necessarily grasp the enormity of our progress in productizing this autonomous system, in terms of ease of integration, configuration, safety, and reliability, but it is huge because it means that our robot systems can be drop-shipped pretty much worldwide and get up and running with minimal customization,” Odhner says. “There is no reason why this can’t just come in a box or on a pallet and be set up by anyone. That’s our big vision.” More

MIT’s Hockfield Court is bordered on the west by the ultramodern Stata Center, with its reflective, silver alcoves that jut off at odd angles, and on the east by Building 68, which is a simple, window-lined, cement rectangle. At first glance, Bonnie Berger’s mathematics lab in the Stata Center and Joey Davis’s biology lab in Building 68 are as different as the buildings that house them. And yet, a recent collaboration between these two labs shows how their disciplines complement each other. The partnership started when Ellen Zhong, a graduate student from the Computational and Systems Biology (CSB) Program, decided to use a computational pattern-recognition tool called a neural network to study the shapes of molecular machines. Three years later, Zhong’s project is letting scientists see patterns that run beneath the surface of their data, and deepening their understanding of the molecules that shape life.

Zhong’s work builds on a technique from the 1970s called cryo-electron microscopy (cryo-EM), which lets researchers take high-resolution images of frozen protein complexes. Over the past decade, better microscopes and cameras have led to a “resolution revolution” in cryo-EM that’s allowed scientists to see individual atoms within proteins. But, as good as these images are, they’re still only static snapshots. In reality, many of these molecular machines are constantly changing shape and composition as cells carry out their normal functions and adjust to new situations.

Along with former Berger lab member Tristan Belper, Zhong devised software called cryoDRGN. The tool uses neural nets to combine hundreds of thousands of cryo-EM images, and shows scientists the full range of three-dimensional conformations that protein complexes can take, letting them reconstruct the proteins’ motion as they carry out cellular functions. Understanding the range of shapes that protein complexes can take helps scientists develop drugs that block viruses from entering cells, study how pests kill crops, and even design custom proteins that can cure disease. Covid-19 vaccines, for example, work partly because they include a mutated version of the virus’s spike protein that’s stuck in its active conformation, so vaccinated people produce antibodies that block the virus from entering human cells. Scientists needed to understand the variety of shapes that spike proteins can take in order to figure out how to force spike into its active conformation.

Getting off the computer and into the lab

Zhong’s interest in computational biology goes back to 2011 when, as a chemical engineering undergrad at the University of Virginia, she worked with Professor Michael Shirts to simulate how proteins fold and unfold. After college, Zhong took her skills to a company called D. E. Shaw Research, where, as a scientific programmer, she took a computational approach to studying how proteins interact with small-molecule drugs.

“The research was very exciting,” Zhong says, “but all based on computer simulations. To really understand biological systems, you need to do experiments.”

This goal of combining computation with experimentation motivated Zhong to join MIT’s CSB PhD program, where students often work with multiple supervisors to blend computational work with bench work. Zhong “rotated” in both the Davis and Berger labs, then decided to combine the Davis lab’s goal of understanding how protein complexes form with the Berger lab’s expertise in machine learning and algorithms. Davis was interested in building up the computational side of his lab, so he welcomed the opportunity to co-supervise a student with Berger, who has a long history of collaborating with biologists.

Davis himself holds a dual bachelor’s degree in computer science and biological engineering, so he’s long believed in the power of combining complementary disciplines. “There are a lot of things you can learn about biology by looking in a microscope,” he says. “But as we start to ask more complicated questions about entire systems, we’re going to require computation to manage the high-dimensional data that come back.”

Reconstructing Molecules in Motion

Before rotating in the Davis lab, Zhong had never performed bench work before — or even touched a pipette. She was fascinated to find how streamlined some very powerful molecular biology techniques can be. Still, Zhong realized that physical limitations mean that biology is much slower when it’s done at the bench instead of on a computer. “With computational research, you can automate experiments and run them super quickly, whereas in the wet lab, you only have two hands, so you can only do one experiment at a time,” she says.

Zhong says that synergizing the two different cultures of the Davis and Berger labs is helping her become a well-rounded, adaptable scientist. Working around experimentalists in the Davis lab has shown her how much labor goes into experimental results, and also helped her to understand the hurdles that scientists face at the bench. In the Berger lab, she enjoys having coworkers who understand the challenges of computer programming.

“The key challenge in collaborating across disciplines is understanding each other’s ‘languages,’” Berger says. “Students like Ellen are fortunate to be learning both biology and computing dialects simultaneously.”

Bringing in the community

Last spring revealed another reason for biologists to learn computational skills: these tools can be used anywhere there’s a computer and an internet connection. When the Covid-19 pandemic hit, Zhong’s colleagues in the Davis lab had to wind down their bench work for a few months, and many of them filled their time at home by using cryo-EM data that’s freely available online to help Zhong test her cryoDRGN software. The difficulty of understanding another discipline’s language quickly became apparent, and Zhong spent a lot of time teaching her colleagues to be programmers. Seeing the problems that nonprogrammers ran into when they used cryoDRGN was very informative, Zhong says, and helped her create a more user-friendly interface.

Although the paper announcing cryoDRGN was just published in February, the tool created a stir as soon as Zhong posted her code online, many months prior. The cryoDRGN team thinks this is because leveraging knowledge from two disciplines let them visualize the full range of structures that protein complexes can have, and that’s something researchers have wanted to do for a long time. For example, the cryoDRGN team recently collaborated with researchers from Harvard and Washington universities to study locomotion of the single-celled organism Chlamydomonas reinhardtii. The mechanisms they uncovered could shed light on human health conditions, like male infertility, that arise when cells lose the ability to move. The team is also using cryoDRGN to study the structure of the SARS-CoV-2 spike protein, which could help scientists design treatments and vaccines to fight coronaviruses.

Zhong, Berger, and Davis say they’re excited to continue using neural nets to improve cryo-EM analysis, and to extend their computational work to other aspects of biology. Davis cited mass spectrometry as “a ripe area to apply computation.” This technique can complement cryo-EM by showing researchers the identities of proteins, how many of them are bound together, and how cells have modified them.

“Collaborations between disciplines are the future,” Berger says. “Researchers focused on a single discipline can take it only so far with existing techniques. Shining a different lens on the problem is how advances can be made.”

Zhong says it’s not a bad way to spend a PhD, either. Asked what she’d say to incoming graduate students considering interdisciplinary projects, she says: “Definitely do it.” More

MIT has pioneered many online learning solutions, and the U.S. Department of Defense (DoD) has taken note. MIT and the DoD have teamed up to launch a new edX learning platform, manufacturingworkforce.org.

In the past decade, the DoD has launched nine public-private manufacturing institutes to spur U.S. advanced manufacturing industry forward in areas such as additive manufacturing, robotics, photonics, functional fabrics, and bio-fabrication. An important part of the institutes’ mission is workforce development, which includes online learning. To that end, the DoD has tasked MIT’s Initiative for Knowledge and Innovation in Manufacturing (IKIM) to stand up an Open edX platform for the DoD’s nine institutes and the larger advanced technologies community.

IKIM leads the education and workforce effort of the manufacturing institute AIM Photonics, and just launched the first two courses on the new platform, on photonic integrated circuit (PIC) sensors and on integrated photonics passive device testing. Principal Research Scientist Anu Agarwal and Professor Juejun Hu’s courses are what you might expect from MIT; they cover technical cutting-edge material. MIT IKIM will release five more courses this summer on the new platform, all tied to integrated photonics, and all courses that would fit into MIT’s course catalog.

The DoD’s mission for the new learning platform, however, is to reach far beyond hosting MIT-like classes. The Commonwealth of Massachusetts, in partnership with MIT and others, is building an advanced manufacturing awareness course for high school students exploring potential careers that will go on the platform, tied to at least five of the manufacturing institute technologies. That project is part of a $3.2 million grant announced last October. MIT IKIM also plans to create technician and technologist edX training programs for students seeking careers in advanced technologies, but not necessarily interested in pursuing bachelor’s degrees. Many institutes are planning their online offerings, targeting students at all levels, even starting in elementary school.

Although some people might not associate the DoD with STEM education, it invests heavily in innovative STEM initiatives. MIT IKIM received DoD funding from the Manufacturing Engineering Education Program to build technician programs in robotics and photonics, and to launch a Virtual Manufacturing Lab — a suite of virtual reality simulations in photonics and other advanced manufacturing technologies. The DoD’s investment in the Open edX platform is consistent with its goal of making top-notch education more accessible for students at all levels. 

“The Department of Defense is eager to help build a robust domestic manufacturing industry. To do this, we need cutting-edge advanced manufacturing education and training available to more Americans,” says Michael Britt-Crane, education and workforce lead for the DoD’s Manufacturing Technology Program Office. “This platform is an important way to do this, and to bring these resources to the DoD workforce.”

The Advanced Robotics for Manufacturing (ARM) institute recently received funding to create a virtual manufacturing environment on the Open edX platform, where students can train on virtual equipment. The environment could become a place to demonstrate competency and receive credentials. ARM recognizes the vast potential of virtual and augmented realities to quickly scale its manufacturing workforce in the use of robotics and automation. More

The sentient Magic Carpet from “Aladdin” might have a new competitor. While it can’t fly or speak, a new tactile sensing carpet from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) can estimate human poses without using cameras, in a step toward improving self-powered personalized health care, smart homes, and gaming.

Many of our daily activities involve physical contact with the ground: walking, exercising, or resting. These embedded interactions contain a wealth of information that help us better understand people’s movements. 

Previous research has leveraged use of single RGB cameras, (think Microsoft Kinect), wearable omnidirectional cameras, and even plain old off-the-shelf webcams, but with the inevitable byproducts of camera occlusions and privacy concerns. 

The CSAIL team’s system only used cameras to create the dataset the system was trained on, and only captured the moment of the person performing the activity. To infer the 3D pose, a person would simply have to get on the carpet, perform an action, and then the team’s deep neural network, using just the tactile information, could determine if the person was doing situps, stretching, or doing another action. 

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Intelligent Carpet: Estimating a person’s 3D pose using only tactile sensors

“You can imagine leveraging this model to enable a seamless health-monitoring system for high-risk individuals, for fall detection, rehab monitoring, mobility, and more,” says Yiyue Luo, a lead author on a paper about the carpet. 

The carpet itself, which is low-cost and scalable, was made of commercial, pressure-sensitive film and conductive thread, with over 9,000 sensors spanning 36-by-2 feet. (Most living room rug sizes are 8-by-10 and 9-by-12.) 

Each of the sensors on the carpet converts the human’s pressure into an electrical signal, through the physical contact between people’s feet, limbs, torso, and the carpet. The system was specifically trained on synchronized tactile and visual data, such as a video and corresponding heat map of someone doing a pushup. 

The model takes the pose extracted from the visual data as the ground truth, uses the tactile data as input, and finally outputs the 3D human pose.

This might look something like when, after stepping onto the carpet and doing a set up of pushups, the system is able to produce an image or video of someone doing a pushup. 

In fact, the model was able to predict a person’s pose with an error margin (measured by the distance between predicted human body key points and ground truth key points) by less than 10 centimeters. For classifying specific actions, the system was accurate 97 percent of the time. 

“You could envision using the carpet for workout purposes. Based solely on tactile information, it can recognize the activity, count the number of reps, and calculate the amount of burned calories,” says MIT CSAIL PhD student Yunzhu Li, a co-author on the paper.

Since much of the pressure distributions were prompted by movement of the lower body and torso, that information was more accurate than the upper-body data. Also, the model was unable to predict poses without more explicit floor contact, like free-floating legs during situps, or a twisted torso while standing up. 

While the system can understand a single person, the scientists, down the line, want to improve the metrics for multiple users, where two people might be dancing or hugging on the carpet. They also hope to gain more information from the tactical signals, such as a person’s height or weight. 

Luo wrote the paper alongside Li and MIT CSAIL PhD student Yunzhu Pratyusha Sharma, MIT CSAIL mechanical engineer Michael Foshey, MIT CSAIL postdoc Wan Shou, and MIT professors Tomas Palacios, Antonio Torralba, and Wojciech Matusik. The work is funded by the Toyota Research Institute.  More

The U.S. Department of Energy (DoE) recently announced the names of 83 scientists who have been selected for their 2021 Early Career Research Program. The list includes four faculty members from MIT: Riccardo Comin of the Department of Physics; Netta Engelhardt of the Department of Physics and Center for Theoretical Physics; Philip Harris of the Department of Physics and Laboratory for Nuclear Science; and Mingda Li of the Department of Nuclear Science and Engineering.

Each year, the DoE selects researchers for significant funding the “nation’s scientific workforce by providing support to exceptional researchers during crucial early career years, when many scientists do their most formative work.”

Resonant coherent diffractive imaging of quantum solids

The quantum technologies of tomorrow –– more powerful computing, better navigation systems, and more precise imaging and magnetic sensing devices –– rely on understanding the properties of quantum materials. Quantum materials contain unique physical characteristics, and can lead to phenomena like superconductivity. Detecting and visualizing these materials at the nanoscale will enable scientists to understand and harness the properties of quantum materials.

Riccardo Comin, the Class of 1947 Career Development Assistant Professor of Physics, leads the Comin Photon Scattering Lab at MIT. The group uses high-energy electromagnetic waves, or X-rays, to observe how new collective states emerge at the nanoscale in quantum materials. This is a difficult feat, as the lenses in cameras and in the human eye do not work for X-rays as they do for visible light. Conventional microscopy techniques are not well-suited for visualizing these complex phenomena.

To overcome this technical limitation, the Comin group has worked on a “lensless” X-ray microscopy approach to image these electronic textures.

“These new imaging techniques are really fascinating and deeply challenge our traditional ways of performing X-ray microscopy,” Comin says. “We now rely on special algorithms that can perform computationally the task of image reconstruction that is normally taken care of by a lens.”

The support from the DoE Early Career Research program will be instrumental to the group’s work developing and applying these novel techniques to study the nanoscale organization of quantum materials of interest. Looking beyond the horizon of quantum materials, the availability of lensless X-ray imaging methods provides a new powerful tool set for the characterization of catalysts, batteries, data storage devices, soft matter, and biological systems.

Spacetime emergence from quantum gravity

Few phenomena in modern physics remain as mysterious as the black hole interior. Black holes seem to wreck the objects that fall into them, as well as information about what those objects once were. Yet according to basic principles of quantum mechanics (the study of subatomic particle behavior), knowing the current state of a given system should mean knowing everything about its past and future.

General relativity and quantum mechanics are two highly tested theories. When it comes to black holes, general relativity and quantum mechanics disagree on a fundamental point: whether information about the region behind the event horizon can escape and be decoded by an observer outside of the black hole. The clash between general relativity and quantum mechanics on this matter results in what is termed the “black hole information paradox.” In recent years, scientists have drawn numerous connections between gravity and quantum information.

Netta Engelhardt, the Biedenharn Career Development Assistant Professor of physics and member of the Center for Theoretical Physics, researches quantum gravity and the black hole information paradox.

“With a recent leap in our understanding of the black hole information paradox, the connection between gravity, quantum computational complexity, and black holes has newfound potential to shed light on some of the most foundational questions about quantum gravity, starting with ‘What really happens inside a black hole?’” Engelhardt says.

With support from the DoE award, her project aims to move toward resolving the black hole information paradox using some of the novel tools and insights at the intersection of the two theories.

Harnessing the Large Hadron Collider with new insights in real-time data processing and artificial intelligence

Particle accelerators help scientists learn more about the particles that comprise matter.

Nearly 17 miles in circumference, the Large Hadron Collider (LHC) at the European Center for Nuclear Research is the largest and most powerful particle accelerator in the world, producing valuable information for researchers.

Researchers have spent much time using LHC data to investigate novel particle interactions at the highest energies. But over the next two decades, they anticipate shifting their focus, directing their efforts toward precision measurements that target physics processes with small interaction strengths and extensive background rates.

As a result of these more detailed observations, physicists expect additional rare and hidden processes within the Standard Model (SM) of particle physics, and potentially beyond the SM, to emerge as more data mounts.

Philip Harris, assistant professor of physics and researcher in the Laboratory for Nuclear Science, is working on a physics program to measure those smaller, more inconspicuous processes. Specifically, with the support of DoE funding, his research aims to exploit a new measurement technique he created to identify light resonances that decay into quarks –– the particles that eventually combine to create subatomic particles.

“In conjunction with advanced artificial intelligence algorithms, this new technique can open up a wealth of unique measurements and searches,” Harris says. “The fully developed state-of-the-art system will empower new measurements of the Higgs boson, new searches for dark matter, and analyses of a multitude of unexplored scientific phenomena.”

Machine learning-augmented multimodal neutron scattering for emergent topological materials

Topological materials are a class of quantum materials whose electronic properties have robust protection against outside influences. This robustness enables a wide range of promising applications, such as next-generation electronics without energy loss, and error-tolerant quantum computers.

But it’s difficult to directly test materials for their topological properties. Rather, scientists usually use methods that measure manifestations of topology. One such method is neutron scattering, or neutron spectroscopy, a process used by scientists to assess materials.

Neutron scattering has particular advantages when it comes to evaluating topological quantum materials, but more information is needed to understand exactly how massive amounts of data gathered during neutron spectroscopy map onto topology.

The DoE Early Career Research Program Award will support Mingda Li, the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering, in his machine-learning approach to analyzing high-dimensional neutron scattering spectra in quantum materials.

“The new approach will augment existing neutron scattering probes by measuring things that were not measurable before,” Li says. By doing so, “it will enable a broader discovery of hidden materials states that may have electronics applications, and identify topological solutions that can be used for computer memory.”  More

Hovering 100 meters above a densely populated urban residential area, the drone takes a quiet breath. Its goal is singular: to systematically measure air quality across the metropolitan landscape, providing regular updates to a central communication module where it docks after its patrol, awaiting a new set of instructions. The central module integrates each new data point provided by a small drone fleet, processing them against wind and traffic patterns and historical pollution hot spot information. Then the fleet is assigned new sampling waypoints and relaunched.

This simulated autonomous air pollution monitoring system is the capstone project of recently graduated New Engineering Education Transformation (NEET) program alumni Chloe Nelson-Arzuaga ’21, Jeana Choi ’21, Daniel Gonzalez-Diaz ’21, Leilani Trautman ’21, Rima Rebei ’21, and Berke Saat ’21, who have together studied autonomous robotics since they entered NEET during their sophomore year at MIT.

“We all converged on this, because we felt that the social impact would be more powerful than any of the other projects we were looking at,” says Trautman, who majored in electrical engineering and computer science.

Their system represents a fundamentally different approach to air quality monitoring compared with the stationary systems routinely used in urban areas, which the group says often fail to detect spatial heterogeneity in pollution levels across a landscape. Given their limited distribution and lack of mobility, these systems are really only a reliable indicator of the air quality directly surrounding each monitoring point, but their data are reported as though they were representative of air quality across the entire city, say the recent graduates.

“So even though they might say that your air quality is somewhat good, that may not be the case for the park right next to your home,” says Gonzalez-Diaz.

The NEET cohort’s drone system is designed to provide real-time air quality data with a 15-meter resolution that is publicly accessible through a user-friendly interface.

The NEET program was launched in 2017 in an effort to fundamentally reconceptualize the way that engineering is taught at MIT. It emphasizes interdisciplinary scholarship, cross-departmental community, and project-based learning to prepare students to engage the major engineering challenges of the 21st century. The program is actively growing and is the fourth-largest undergraduate academic community, with more than 186 students participating. Twenty-three majors from 13 departments are represented. Sixty-four percent of the students are women and 28 percent are members of underrepresented groups. This year over 39% of first-years who applied to this program heard about it from current NEET students.

Nelson-Arzuaga, Choi, Gonzalez-Diaz, Trautman, Rebei, and Saat graduated from MIT with a certificate from NEET’s Autonomous Machines “thread,” or area of concentration. The NEET program has five threads that students can choose from, each emphasizing a class of contemporary or futuristic engineering problems. For example, the Advanced Materials thread explores the future of materials technologies and manufacturing, while the Digital Cities thread integrates computer science with urban planning to prepare students to build more idealized cities. NEET also offers a biotechnology-focused Living Machines thread as well as the Renewable Energy Machines thread, which emphasizes green energy systems design. The Autonomous Machines thread teaches students to design, build, and program autonomous robots. “A common feature across all five threads is that NEET students want to create an impact while they are still students,” says NEET Executive Director Babi Mitra, “by doing projects that tackle critical societal problems.”

The NEET curriculum structure is progressive, building on the previous year’s lessons to ultimately prepare the students for real-world application.

“[Sophomore year] we give them an individual project … and then, during junior year, they have their first small group project. And then the senior year, they have a class project. So it progressively gets more complicated,” says NEET Lead Instructor Greg Long. “This senior project is supposed to mimic something they would do if they were going to do a startup company.”

It was during the cohort’s junior year, when they were tasked with building autonomous vehicles that could race other vehicles and avoid obstacles, that the pandemic forced the closure of MIT’s campus and the NEET cohort was scattered across the globe.

“[The curriculum] is so hands-on and such a huge time commitment that we really thought the classes would end at that point when we had to leave campus,” says Saat. “But then they kept going. They set up the simulation for us and everything, but the time commitment was still there and we were in five different time zones.”

The NEET program demands roughly 20 hours per week on top of the rest of the students’ course load and the students say that meeting this demand with Choi in Cambridge, Trautman and Nelson-Arzuaga in California, Saat in Turkey, Rebei in Illinois, Gonzalez-Diaz in Puerto Rico, and another teammate in Taiwan required creative and exhausting schedule coordination.

“Literally [for] some of us the sun was rising and [for] the others the sun was going down [while working together],” says Choi. “We’ve definitely bonded and learned so much, and I think it would not have been possible if even one of us was not very interested in robotics.”

A unique feature of the NEET Autonomous Machines thread is that students take a senior fall semester class during which they discuss and decide the senior spring project they want to work on. It was also the pandemic that helped inform the group’s specific choice to tackle air pollution as a final project their senior year, because it further exposed “the racial and economic disparities that air pollution causes in the United States,” says Rebei.  

The cohort’s drone project is designed to monitor a form of pollution called PM 2.5, which are essentially pollution particles small enough to enter the bloodstream when inhaled, potentially resulting in lung and heart disease over time, according to the students.

“Low-income communities of color, at the end of the day, are the ones who are disproportionately impacted by air pollution, and … air pollution is what contributes to a lot of these deadly respiratory illnesses … in these particular communities,” says Rebei. “People who already have these preexisting conditions … are at higher risk of getting very sick from Covid-19.”

In addition to designing a drone system capable of effectively capturing neighborhood-level variation in pollution exposure levels, the NEET cohort created a web interface that could layer this information with area socioeconomic data, such as income, race, household composition, disability status, housing types, and modes of transportation. This would make patterns and disparities in air pollution exposure public and easier to prove, something the students say could help affected communities advocate for change.

The complexity of the drone project, remote learning notwithstanding, required the entire cohort to step out of their comfort zone and learn new skills quickly.

“I’m a mechanical design student, so I do a lot of 3D modeling,” says Nelson-Arzuaga. However, for the drone project she was in charge of learning how to operate a cellular communications network so that the drones would be able to talk to the central communication module. “[It was] very different from anything that I learned in all of my other design classes.”

The recent graduates say that NEET prepared them to engage these challenges, but expressed that the most valuable part of the program for them was the community that they built and the experiences they shared working together.

“When we took the sophomore robot class, we were all like, ‘What is a robot? How do we do anything?’” jokes Trautman. “[And now we’re] doing very high-quality development work on this project. I think it’s been exciting to see, to see where everyone is going in the future and just seeing how everyone’s progressed. It’s been a really cool journey.”

The NEET program is continuing to develop and fine-tune its curriculum, learning environment, and community, and student engagement is an integral part of that process. Spaces are still available in the NEET Class of 2024 cohort. For more information about applying visit the NEET website. More

Four MIT-affiliated researchers are among 28 around the world to have been named to a competitive Schmidt Science Fellowship, an award created in 2017 to advance interdisciplinary studies among early-career researchers.

“An initiative of Schmidt Futures, delivered in partnership with the Rhodes Trust, the Schmidt Science Fellows program brings together the brightest minds who have completed a PhD in the natural sciences, mathematics, engineering, or computing, and places them in a postdoctoral fellowship in a field different from their existing expertise,” according to a recent announcement of the awards by benefactors Eric and Wendy Schmidt. “Fellows are supported for at least one and up to two years with a $100,000 per year stipend. The funding provides both training for the fellows and the research they undertake.” 

Álvaro Fernández Galiana is a PhD candidate in mechanical engineering. As a member of MIT’s Laser Interferometer Gravitational-Wave Observatory (LIGO) Laboratory, he has focused on developing precision instrumentation to improve the sensitivity of interferometers used to detect gravitational waves. Earlier in his doctoral studies, he worked on the vibration isolation platform of the “squeezer instrument,” which reduces quantum noise. This breakthrough contributed to a 40 percent increase in the detection rate of the LIGO observatories. He has since been working on a compact version of this instrument with applications in metrology and quantum information experiments. As a Schmidt Fellow, he will shift gears to focus on solutions for population health monitoring. He plans to combine vibrational spectroscopy and machine learning to create a low-cost platform for multi-pathogen detection. This technology could be used for mass population screening and may improve health outcomes in resource-constrained environments and during future pandemics.

“I feel truly honored to become a member of the Schmidt Science Fellows program and join this vibrant scientific community,” says Fernández Galiana. “It is a unique and exciting opportunity to step outside my comfort zone and apply the knowledge and skills that I have gained at MIT at the interface of physics and engineering to a new discipline.”

In her doctoral work at MIT, Fatima Hussain PhD ’20 studied the impact of phages — viruses that infect bacteria — on the ecology and evolution of marine microbes, with Professor Martin Polz. As a Schmidt fellow, Hussain will be applying her expertise in marine microbiology and phage biology to the vaginal microbiome. Hussain plans to study how the immune system interacts with pathogens and healthy bacteria in the vaginal mucosa and aims to understand the impacts of these interactions on HIV risk. Ultimately, she hopes her work will lay the foundation for designing ecologically-informed and women-centric therapies to improve the health of women globally.

“The fellowship’s focus on interdisciplinary research is most appealing to me,” she says. “Having studied environmental engineering, women’s and gender studies, and microbiology, I am thrilled with this opportunity to combine these longstanding interests with a new field, mucosal immunology, through the support of the Schmidt Fellowship.”

Sirma Orguc PhD ’21, a newly named Schmidt Fellow in the Institute for Medical Engineering and Science, earned her doctorate this year in the MIT Department of Electrical Engineering and Computer Science, advised by Anantha Chandrakasan, the Vannevar Bush Professor and dean of the School of Engineering, and Polina Anikeeva, associate professor in the departments of Brain and Cognitive Sciences and Materials Science and Engineering (IMES). Orguc’s doctoral studies blended electronics, materials science, and algorithm development in research on wearable and implantable interface technologies for biomedical and neuroscience applications. During her postdoc in the lab of Edwood Hood Taplin Professor Emery N. Brown, who is a member of IMES and The Picower Institute for Learning and Memory, Orguc will learn about computational neuroscience, machine learning, neurophysiology, and control theory with the aim of building closed-loop neuroscience systems in humans.

“Controlling the level of unconsciousness under general anesthesia, real-time prevention of epileptic seizures, and working towards treating disorders such as chronic depression are example applications of interest,” Orguc says. “The Schmidt Science Fellows community believes in the power of interdisciplinary science to drive innovation and discovery and make a positive impact in the world. I am beyond grateful and excited to be part of such a community. The fellowship gives incredible flexibility to researchers, and I will try to make the most of it.”

Rebecca Pinals earned her PhD in May from the University of California at Berkeley’s Chemical and Biomolecular Engineering Department after studying fundamentals of how engineered nanomaterials interact with biological environments. Leveraging her insights into designing nanosensors for biomedical applications, this month she will join the lab of Picower Professor and Picower Institute Director Li-Huei Tsai in MIT’s Department of Brain and Cognitive Sciences as a postdoc. Pinals will investigate the mechanistic underpinnings of Alzheimer’s disease by developing nanosensors for key disease biomarkers and applying them to probe the disease in human brain tissue models.

“Implementing the tools of nanotechnology to study Alzheimer’s will deepen our understanding of the underlying disease drivers by providing the requisite spatial, temporal, and chemical resolution information on biomarkers during disease onset and progression,” she says. “I am beyond excited for this opportunity to pursue impactful research at the Picower Institute in an orthogonal field to my own background, and to be a part of the Schmidt Science Fellows community.” More

The onset of the Covid-19 pandemic spurred an immediate need to develop new, innovative systems in supply chains and infrastructure. And for three Norwegian graduate students enrolled in the MIT Professional Education Advanced Study Program (ASP), spring 2020 was the moment when technology, innovation, and preparation met opportunity.

Lars Erik Matsson Fagernæs, Bernhard Paus Græsdal, and Herman Øie Kolden were all students at the Norwegian University of Science and Technology (NTNU) but only met after they arrived on the MIT campus for their ASP in 2019. Fagernæs came to MIT to study computer science, Græsdal focused on robotics, and Kolden came to study plasma physics, though he had prior experience with drones through a job at a defense contractor.

When the pandemic began in early 2020, Fagernæs, Græsdal, and Kolden were all still in Cambridge, Massachusetts. NTNU would eventually recall them home, but not for a few months. To pass the time, they read news from Norway and identified a problem that they thought they could solve.

Norway is not an easy country to traverse, with roads laid out circuitously around mountains and fjords. Small regional hospitals do not have easy access to the labs and testing facilities at larger university hospitals. “Some local governments don’t even test for Covid during weekends because they have issues with transportation,” says Fagernæs. “In some parts in the north, you have to drive for 10 or 15 hours just to transport tests to the hospital for analysis.”

The friends had already been working on a drone-related project and pivoted to the idea of making a drone to transport biological samples. They chose a fixed-wing quadcopter design that combines vertical takeoff and landing with efficient long-distance travel.

Long-duration drones for medical delivery

Their prototype drones were built at MIT and tested in the Johnson Athletic Center around its running track. They found inspiration in the work of MIT professors like Russ Tedrake, director of the Center for Robotics at the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a professor of electrical engineering and computer science.

“Bernhard and Lars took my graduate robotics class,” Tedrake says. “They were extremely engaged and regularly asked questions that made it clear they were not just listening to the lectures, but were actively experimenting with the ideas. My role was to introduce them to topics in dynamics, control, and optimization, and talk them through the projects, but the innovation and hard work was all theirs!”

In building their drone, Fagernæs, Græsdal, and Kolden had to overcome a number of technical issues, including icing, vibrations, and variable temperatures. Evolving EU drone regulations necessitated building redundant systems and a parachute in case of malfunction. However, the biggest challenge was the distance they needed to fly, 120 kilometers from start to end. An autonomous flight of that length had never been completed in Scandinavia before.

“People thought we were crazy,” Fagernæs recalls. “But we were lucky enough to speak to the right people at the hospital who were desperate for a solution, and they decided to give us a chance. So, we have been working ever since, day and night.”

This past March, the students achieved a proof-of-concept flight, making a 120-kilometer flight in just 80 minutes, cutting hours off ground transport times — all with minimal piloting. They believe this is the longest autonomous drone flight in Scandinavia, strong evidence to support the viability of a much-needed service that will extend far beyond the Covid era.

“The drone has both internal and external sensors, which give you information about the world. Then based on that information, it’s able to navigate and fly autonomously,” says Græsdal.

Given the number of sensors and automation built into the aircraft, a single pilot could conceivably back up 10 or more drones.

“Because of the current state of regulations, nobody in the world operates fully autonomous drones. It’s definitely coming, though,” Kolden adds. “We have what’s called a ‘back-backseat pilot’ so if there’s a warning then you can take control.”

Crediting MIT

In order to develop their technology further, Fagernæs, Græsdal, and Kolden have also launched a startup, Aviant. Publicity from their test flight has already led to interest from their Scandinavian neighbors. “We are now expanding into Sweden,” reports Fagernæs. “We are doing two projects in Sweden, helping with all sorts of logistics with drones, because [transportation infrastructure] is a huge problem in Sweden as well.”

The trio is effusive about their MIT experience. “We’re starting a company, changing Norwegian infrastructure — this never would have happened without MIT,” Græsdal says.

“As ASP students, everything at MIT was open to us. We had offices to work in and networking events sponsored by ASP, where we met other students, as well as people from industry,” adds Fagernæs.

Fagernæs, Græsdal, and Kolden count Bianca Sinausky, program administrator of ASP, as a personal friend for the guidance she provided throughout their time on the MIT campus, and for her assistance navigating pandemic-related disruption as they returned home and completed their program requirements from Norway.

According to Sinausky, the students were ideal candidates for the program. “The Advanced Study Program offers those with a bachelor degree the opportunity to enroll in MIT classes as a non-degree student, and provides maximum flexibility for working professionals and exceptional graduate students who want to enhance their knowledge and further their careers with an MIT education,” she says. “It’s gratifying when ASP students like Bernhard, Herman, and Lars Erik meet at MIT through their passion for engineering, technology, and science, and are able to quickly make a positive impact in their home country, and potentially around the world.”

Adds Bhaskar Pant, executive director of MIT Professional Education, “the success of these Norwegian students underscores the reason why we consider the Advanced Study Program the ‘jewel in the crown’ at MIT Professional Education. It is a very special boutique program that allows enrollees to access the full resources of MIT while networking with each other to realize their high aspirations, including building a startup to help meet human challenges during and after a pandemic!” More