New programmable materials can sense their own movements

MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment. The researchers create these sensing structures using just one material and a single run on a 3D printer.

To accomplish this, the researchers began with 3D-printed lattice materials and incorporated networks of air-filled channels into the structure during the printing process. By measuring how the pressure changes within these channels when the structure is squeezed, bent, or stretched, engineers can receive feedback on how the material is moving.

The method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition. Controlling the geometry of features in architected materials alters their mechanical properties, such as stiffness or toughness. For instance, in cellular structures like the lattices the researchers print, a denser network of cells makes a stiffer structure.

This technique could someday be used to create flexible soft robots with embedded sensors that enable the robots to understand their posture and movements. It might also be used to produce wearable smart devices that provide feedback on how a person is moving or interacting with their environment.

“The idea with this work is that we can take any material that can be 3D-printed and have a simple way to route channels throughout it so we can get sensorization with structure. And if you use really complex materials, then you can have motion, perception, and structure all in one,” says co-lead author Lillian Chin, a graduate student in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Joining Chin on the paper are co-lead author Ryan Truby, a former CSAIL postdoc who is now as assistant professor at Northwestern University; Annan Zhang, a CSAIL graduate student; and senior author Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of CSAIL. The paper is published today in Science Advances.

Architected materials

The researchers focused their efforts on lattices, a type of “architected material,” which exhibits customizable mechanical properties based solely on its geometry. For instance, changing the size or shape of cells in the lattice makes the material more or less flexible.

While architected materials can exhibit unique properties, integrating sensors within them is challenging given the materials’ often sparse, complex shapes. Placing sensors on the outside of the material is typically a simpler strategy than embedding sensors within the material. However, when sensors are placed on the outside, the feedback they provide may not provide a complete description of how the material is deforming or moving.

Instead, the researchers used 3D printing to incorporate air-filled channels directly into the struts that form the lattice. When the structure is moved or squeezed, those channels deform and the volume of air inside changes. The researchers can measure the corresponding change in pressure with an off-the-shelf pressure sensor, which gives feedback on how the material is deforming.

Because they are incorporated into the material, these “fluidic sensors” offer advantages over conventional sensor materials.

“Sensorizing” structures

The researchers incorporate channels into the structure using digital light processing 3D printing. In this method, the structure is drawn out of a pool of resin and hardened into a precise shape using projected light. An image is projected onto the wet resin and areas struck by the light are cured.

But as the process continues, the resin remains stuck inside the sensor channels. The researchers had to remove excess resin before it was cured, using a mix of pressurized air, vacuum, and intricate cleaning.

They used this process to create several lattice structures and demonstrated how the air-filled channels generated clear feedback when the structures were squeezed and bent.

“Importantly, we only use one material to 3D print our sensorized structures. We bypass the limitations of other multimaterial 3D printing and fabrication methods that are typically considered for patterning similar materials,” says Truby.

Building off these results, they also incorporated sensors into a new class of materials developed for motorized soft robots known as handed shearing auxetics, or HSAs. HSAs can be twisted and stretched simultaneously, which enables them to be used as effective soft robotic actuators. But they are difficult to “sensorize” because of their complex forms.

They 3D printed an HSA soft robot capable of several movements, including bending, twisting, and elongating. They ran the robot through a series of movements for more than 18 hours and used the sensor data to train a neural network that could accurately predict the robot’s motion. 

Chin was impressed by the results — the fluidic sensors were so accurate she had difficulty distinguishing between the signals the researchers sent to the motors and the data that came back from the sensors.

“Materials scientists have been working hard to optimize architected materials for functionality. This seems like a simple, yet really powerful idea to connect what those researchers have been doing with this realm of perception. As soon as we add sensing, then roboticists like me can come in and use this as an active material, not just a passive one,” she says.

“Sensorizing soft robots with continuous skin-like sensors has been an open challenge in the field. This new method provides accurate proprioceptive capabilities for soft robots and opens the door for exploring the world through touch,” says Rus.

In the future, the researchers look forward to finding new applications for this technique, such as creating novel human-machine interfaces or soft devices that have sensing capabilities within the internal structure. Chin is also interested in utilizing machine learning to push the boundaries of tactile sensing for robotics.

“The use of additive manufacturing for directly building robots is attractive. It allows for the complexity I believe is required for generally adaptive systems,” says Robert Shepherd, associate professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University, who was not involved with this work. “By using the same 3D printing process to build the form, mechanism, and sensing arrays, their process will significantly contribute to researcher’s aiming to build complex robots simply.”

This research was supported, in part, by the National Science Foundation, the Schmidt Science Fellows Program in partnership with the Rhodes Trust, an NSF Graduate Fellowship, and the Fannie and John Hertz Foundation.

Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing

Caspar Hare and Georgia Perakis have been appointed the new associate deans of the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Stephen A. Schwarzman College of Computing. Their new roles will take effect on Sept. 1.

“Infusing social and ethical aspects of computing in academic research and education is a critical component of the college mission,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I look forward to working with Caspar and Georgia on continuing to develop and advance SERC and its reach across MIT. Their complementary backgrounds and their broad connections across MIT will be invaluable to this next chapter of SERC.”

Caspar Hare

Hare is a professor of philosophy in the Department of Linguistics and Philosophy. A member of the MIT faculty since 2003, his main interests are in ethics, metaphysics, and epistemology. The general theme of his recent work has been to bring ideas about practical rationality and metaphysics to bear on issues in normative ethics and epistemology. He is the author of two books: “On Myself, and Other, Less Important Subjects” (Princeton University Press 2009), about the metaphysics of perspective, and “The Limits of Kindness” (Oxford University Press 2013), about normative ethics.

Georgia Perakis

Perakis is the William F. Pounds Professor of Management and professor of operations research, statistics, and operations management at the MIT Sloan School of Management, where she has been a faculty member since 1998. She investigates the theory and practice of analytics and its role in operations problems and is particularly interested in how to solve complex and practical problems in pricing, revenue management, supply chains, health care, transportation, and energy applications, among other areas. Since 2019, she has been the co-director of the Operations Research Center, an interdepartmental PhD program that jointly reports to MIT Sloan and the MIT Schwarzman College of Computing, a role in which she will remain. Perakis will also assume an associate dean role at MIT Sloan in recognition of her leadership.

Hare and Perakis succeed David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Julie Shah, the H.N. Slater Professor of Aeronautics and Astronautics, who will be stepping down from their roles at the conclusion of their three-year term on Aug. 31.

“My deepest thanks to Dave and Julie for their tremendous leadership of SERC and contributions to the college as associate deans,” says Huttenlocher.

SERC impact

As the inaugural associate deans of SERC, Kaiser and Shah have been responsible for advancing a mission to incorporate humanist, social science, social responsibility, and civic perspectives into MIT’s teaching, research, and implementation of computing. In doing so, they have engaged dozens of faculty members and thousands of students from across MIT during these first three years of the initiative.

They have brought together people from a broad array of disciplines to collaborate on crafting original materials such as active learning projects, homework assignments, and in-class demonstrations. A collection of these materials was recently published and is now freely available to the world via MIT OpenCourseWare.

In February 2021, they launched the MIT Case Studies in Social and Ethical Responsibilities of Computing for undergraduate instruction across a range of classes and fields of study. The specially commissioned and peer-reviewed cases are based on original research and are brief by design. Three issues have been published to date and a fourth will be released later this summer. Kaiser will continue to oversee the successful new series as editor.

Last year, 60 undergraduates, graduate students, and postdocs joined a community of SERC Scholars to help advance SERC efforts in the college. The scholars participate in unique opportunities throughout, such as the summer Experiential Ethics program. A multidisciplinary team of graduate students last winter worked with the instructors and teaching assistants of class 6.036 (Introduction to Machine Learning), MIT’s largest machine learning course, to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning through SERC.

Through efforts such as these, SERC has had a substantial impact at MIT and beyond. Over the course of their tenure, Kaiser and Shah have engaged about 80 faculty members, and more than 2,100 students took courses that included new SERC content in the last year alone. SERC’s reach extended well beyond engineering students, with about 500 exposed to SERC content through courses offered in the School of Humanities, Arts, and Social Sciences, the MIT Sloan School of Management, and the School of Architecture and Planning.

Solving a longstanding conundrum in heat transfer

It is a problem that has beguiled scientists for a century. But, buoyed by a $625,000 Distinguished Early Career Award from the U.S. Department of Energy (DoE), Matteo Bucci, an associate professor in the Department of Nuclear Science and Engineering (NSE), hopes to be close to an answer.

Tackling the boiling crisis

Whether you’re heating a pot of water for pasta or are designing nuclear reactors, one phenomenon — boiling — is vital for efficient execution of both processes.

“Boiling is a very effective heat transfer mechanism; it’s the way to remove large amounts of heat from the surface, which is why it is used in many high-power density applications,” Bucci says. An example use case: nuclear reactors.

To the layperson, boiling appears simple — bubbles form and burst, removing heat. But what if so many bubbles form and coalesce that they form a band of vapor that prevents further heat transfer? Such a problem is a known entity and is labeled the boiling crisis. It would lead to runaway heat, and a failure of fuel rods in nuclear reactors. So “understanding and determining under which conditions the boiling crisis is likely to happen is critical to designing more efficient and cost-competitive nuclear reactors,” Bucci says.

Early work on the boiling crisis dates back nearly a century ago, to 1926. And while much work has been done, “it is clear that we haven’t found an answer,” Bucci says. The boiling crisis remains a challenge because while models abound, the measurement of related phenomena to prove or disprove these models has been difficult. “[Boiling] is a process that happens on a very, very small length scale and over very, very short times,” Bucci says. “We are not able to observe it at the level of detail necessary to understand what really happens and validate hypotheses.”

But, over the past few years, Bucci and his team have been developing diagnostics that can measure the phenomena related to boiling and thereby provide much-needed answers to a classic problem. Diagnostics are anchored in infrared thermometry and a technique using visible light. “By combining these two techniques I think we’re going to be ready to answer standing questions related to heat transfer, we can make our way out of the rabbit hole,” Bucci says. The grant award from the U.S. DoE for Nuclear Energy Projects will aid in this and Bucci’s other research efforts.

An idyllic Italian childhood

Tackling difficult problems is not new territory for Bucci, who grew up in the small town of Città di Castello near Florence, Italy. Bucci’s mother was an elementary school teacher. His father used to have a machine shop, which helped develop Bucci’s scientific bent. “I liked LEGOs a lot when I was a kid. It was a passion,” he adds.

Despite Italy going through a severe pullback from nuclear engineering during his formative years, the subject fascinated Bucci. Job opportunities in the field were uncertain but Bucci decided to dig in. “If I have to do something for the rest of my life, it might as well be something I like,” he jokes. Bucci attended the University of Pisa for undergraduate and graduate studies in nuclear engineering.

His interest in heat transfer mechanisms took root during his doctoral studies, a research subject he pursued in Paris at the French Alternative Energies and Atomic Energy Commission (CEA). It was there that a colleague suggested work on the boiling water crisis. This time Bucci set his sights on NSE at MIT and reached out to Professor Jacopo Buongiorno to inquire about research at the institution. Bucci had to fundraise at CEA to conduct research at MIT. He arrived just a couple of days before the Boston Marathon bombing in 2013 with a round-trip ticket. But Bucci has stayed ever since, moving on to become a research scientist and then associate professor at NSE.

Bucci admits he struggled to adapt to the environment when he first arrived at MIT, but work and friendships with colleagues — he counts NSE’s Guanyu Su and Reza Azizian as among his best friends — helped conquer early worries.

The integration of artificial intelligence

In addition to diagnostics for boiling, Bucci and his team are working on ways of integrating artificial intelligence and experimental research. He is convinced that “the integration of advanced diagnostics, machine learning, and advanced modeling tools will blossom in a decade.”

Bucci’s team is developing an autonomous laboratory for boiling heat transfer experiments. Running on machine learning, the setup decides which experiments to run based on a learning objective the team assigns. “We formulate a question and the machine will answer by optimizing the kinds of experiments that are necessary to answer those questions,” Bucci says, “I honestly think this is the next frontier for boiling,” he adds.

“It’s when you climb a tree and you reach the top, that you realize that the horizon is much more vast and also more beautiful,” Bucci says of his zeal to pursue more research in the field.

Even as he seeks new heights, Bucci has not forgotten his origins. Commemorating Italy’s hosting of the World Cup in 1990, a series of posters showcasing a soccer field fitted into the Roman Colosseum occupies pride of place in his home and office. Created by Alberto Burri, the posters are of sentimental value: The (now deceased) Italian artist also hailed from Bucci’s hometown — Città di Castello.

New algorithm aces university math course questions

Multivariable calculus, differential equations, linear algebra — topics that many MIT students can ace without breaking a sweat — have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.

The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.

This work could be used to streamline content generation for courses, which could be especially useful in large residential courses and massive open online courses (MOOCs) that have thousands of students. The system could also be used as an automated tutor that shows students the steps involved in solving undergraduate math problems.

“We think this will improve higher education,” says Drori, the work’s lead author who is also an adjunct associate professor in the Department of Computer Science at Columbia University, and who will join the faculty at Boston University this summer. “It will help students improve, and it will help teachers create new content, and it could help increase the level of difficulty in some courses. It also allows us to build a graph of questions and courses, which helps us understand the relationship between courses and their pre-requisites, not just by historically contemplating them, but based on data.”

The work is a collaboration including students, researchers, and faculty at MIT, Columbia University, Harvard University, and the University of Waterloo. The senior author is Gilbert Strang, a professor of mathematics at MIT. The research appears this week in the Proceedings of the National Academy of Sciences.

A “eureka” moment

Drori and his students and colleagues have been working on this project for nearly two years. They were finding that models pretrained using text only could not do better than 8 percent accuracy on high school math problems, and those using graph neural networks could ace machine learning course questions but would take a week to train.

Then Drori had what he describes as a “eureka” moment: He decided to try taking questions from undergraduate math courses offered by MIT and one from Columbia University that had never been seen before by a model, turning them into programming tasks, and applying techniques known as program synthesis and few-shot learning. Turning a question into a programming task could be as simple as rewriting the question “find the distance between two points” as “write a program that finds the difference between two points,” or providing a few question-program pairs as examples.

Before feeding those programming tasks to a neural network, however, the researchers added a new step that enabled it to vastly outperform their previous attempts.

In the past, they and others who’ve approached this problem have used a neural network, such as GPT-3, that was pretrained on text only, meaning it was shown millions of examples of text to learn the patterns of natural language. This time, they used a neural network pretrained on text that was also “fine-tuned” on code. This network, called Codex, was produced by OpenAI. Fine-tuning is essentially another pretraining step that can improve the performance of a machine-learning model.

The pretrained model was shown millions of examples of code from online repositories. Because this model’s training data included millions of natural language words as well as millions of lines of code, it learns the relationships between pieces of text and pieces of code.

Many math problems can be solved using a computational graph or tree, but it is difficult to turn a problem written in text into this type of representation, Drori explains. Because this model has learned the relationships between text and code, however, it can turn a text question into code, given just a few question-code examples, and then run the code to answer the problem.

“When you just ask a question in text, it is hard for a machine-learning model to come up with an answer, even though the answer may be in the text,” he says. “This work fills in the that missing piece of using code and program synthesis.”

This work is the first to solve undergraduate math problems and moves the needle from 8 percent accuracy to over 80 percent, Drori adds.

Adding context

Turning math questions into programming tasks is not always simple, Drori says. Some problems require researchers to add context so the neural network can process the question correctly. A student would pick up this context while taking the course, but a neural network doesn’t have this background knowledge unless the researchers specify it.

For instance, they might need to clarify that the “network” in a question’s text refers to “neural networks” rather than “communications networks.” Or they might need to tell the model which programming package to use. They may also need to provide certain definitions; in a question about poker hands, they may need to tell the model that each deck contains 52 cards.

They automatically feed these programming tasks, with the included context and examples, to the pretrained and fine-tuned neural network, which outputs a program that usually produces the correct answer. It was correct for more than 80 percent of the questions.

The researchers also used their model to generate questions by giving the neural network a series of math problems on a topic and then asking it to create a new one.

“In some topics, it surprised us. For example, there were questions about quantum detection of horizontal and vertical lines, and it generated new questions about quantum detection of diagonal lines. So, it is not just generating new questions by replacing values and variables in the existing questions,” Drori says.

Human-generated vs. machine-generated questions

The researchers tested the machine-generated questions by showing them to university students. The researchers gave students 10 questions from each undergraduate math course in a random order; five were created by humans and five were machine-generated.

Students were unable to tell whether the machine-generated questions were produced by an algorithm or a human, and they gave human-generated and machine-generated questions similar marks for level of difficulty and appropriateness for the course.

Drori is quick to point out that this work is not intended to replace human professors.

“Automation is now at 80 percent, but automation will never be 100 percent accurate. Every time you solve something, someone will come up with a harder question. But this work opens the field for people to start solving harder and harder questions with machine learning. We think it will have a great impact on higher education,” he says.

The team is excited by the success of their approach, and have extended the work to handle math proofs, but there are some limitations they plan to tackle. Currently, the model isn’t able to answer questions with a visual component and cannot solve problems that are computationally intractable due to computational complexity.

In addition to overcoming these hurdles, they are working to scale the model up to hundreds of courses. With those hundreds of courses, they will generate more data that can enhance automation and provide insights into course design and curricula.

Using artificial intelligence to control digital manufacturing

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.

Often, an expert operator must use manual trial-and-error — possibly making thousands of prints — to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.

MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer. Their system printed objects more accurately than all the other 3D printing controllers they compared it to.

The work avoids the prohibitively expensive process of printing thousands or millions of real objects to train the neural network. And it could enable engineers to more easily incorporate novel materials into their prints, which could help them develop objects with special electrical or chemical properties. It could also help technicians make adjustments to the printing process on-the-fly if material or environmental conditions change unexpectedly.

“This project is really the first demonstration of building a manufacturing system that uses machine learning to learn a complex control policy,” says senior author Wojciech Matusik, professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group (CDFG) within the Computer Science and Artificial Intelligence Laboratory (CSAIL). “If you have manufacturing machines that are more intelligent, they can adapt to the changing environment in the workplace in real-time, to improve the yields or the accuracy of the system. You can squeeze more out of the machine.”

The co-lead authors on the research are Mike Foshey, a mechanical engineer and project manager in the CDFG, and Michal Piovarci, a postdoc at the Institute of Science and Technology in Austria. MIT co-authors include Jie Xu, a graduate student in electrical engineering and computer science, and Timothy Erps, a former technical associate with the CDFG.

Picking parameters

Determining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required. And once a technician finds a combination that works well, those parameters are only ideal for one specific situation. She has little data on how the material will behave in other environments, on different hardware, or if a new batch exhibits different properties.

Using a machine-learning system is fraught with challenges, too. First, the researchers needed to measure what was happening on the printer in real-time.

To do this, they developed a machine-vision system using two cameras aimed at the nozzle of the 3D printer. The system shines light at material as it is deposited and, based on how much light passes through, calculates the material’s thickness.

“You can think of the vision system as a set of eyes watching the process in real-time,” Foshey says.

The controller would then process images it receives from the vision system and, based on any error it sees, adjust the feed rate and the direction of the printer.

But training a neural network-based controller to understand this manufacturing process is data-intensive, and would require making millions of prints. So, the researchers built a simulator instead.

Successful simulation

To train their controller, they used a process known as reinforcement learning in which the model learns through trial-and-error with a reward. The model was tasked with selecting printing parameters that would create a certain object in a simulated environment. After being shown the expected output, the model was rewarded when the parameters it chose minimized the error between its print and the expected outcome.

In this case, an “error” means the model either dispensed too much material, placing it in areas that should have been left open, or did not dispense enough, leaving open spots that should be filled in. As the model performed more simulated prints, it updated its control policy to maximize the reward, becoming more and more accurate.

However, the real world is messier than a simulation. In practice, conditions typically change due to slight variations or noise in the printing process. So the researchers created a numerical model that approximates noise from the 3D printer. They used this model to add noise to the simulation, which led to more realistic results.

“The interesting thing we found was that, by implementing this noise model, we were able to transfer the control policy that was purely trained in simulation onto hardware without training with any physical experimentation,” Foshey says. “We didn’t need to do any fine-tuning on the actual equipment afterwards.”

When they tested the controller, it printed objects more accurately than any other control method they evaluated. It performed especially well at infill printing, which is printing the interior of an object. Some other controllers deposited so much material that the printed object bulged up, but the researchers’ controller adjusted the printing path so the object stayed level.

Their control policy can even learn how materials spread after being deposited and adjust parameters accordingly.

“We were also able to design control policies that could control for different types of materials on-the-fly. So if you had a manufacturing process out in the field and you wanted to change the material, you wouldn’t have to revalidate the manufacturing process. You could just load the new material and the controller would automatically adjust,” Foshey says.

Now that they have shown the effectiveness of this technique for 3D printing, the researchers want to develop controllers for other manufacturing processes. They’d also like to see how the approach can be modified for scenarios where there are multiple layers of material, or multiple materials being printed at once. In addition, their approach assumed each material has a fixed viscosity (“syrupiness”), but a future iteration could use AI to recognize and adjust for viscosity in real-time.

Additional co-authors on this work include Vahid Babaei, who leads the Artificial Intelligence Aided Design and Manufacturing Group at the Max Planck Institute; Piotr Didyk, associate professor at the University of Lugano in Switzerland; Szymon Rusinkiewicz, the David M. Siegel ’83 Professor of computer science at Princeton University; and Bernd Bickel, professor at the Institute of Science and Technology in Austria.

The work was supported, in part, by the FWF Lise-Meitner program, a European Research Council starting grant, and the U.S. National Science Foundation.

New hardware offers faster computation for artificial intelligence, with much less energy

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

“With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

“The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

“The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

“Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

Accelerating deep learning

Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

Surprising speed

PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

“The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

“The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

“Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

“The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

“Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

“This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

This research is funded, in part, by the MIT-IBM Watson AI Lab.

Q&A: Warehouse robots that feel by sight

More than a decade ago, Ted Adelson set out to create tactile sensors for robots that would give them a sense of touch. The result? A handheld imaging system powerful enough to visualize the raised print on a dollar bill. The technology was spun into GelSight, to answer an industry need for low-cost, high-resolution imaging.

An expert in both human and machine vision, Adelson was pleased to have created something useful. But he never lost sight of his original dream: to endow robots with a sense of touch. In a new Science Hub project with Amazon, he’s back on the case. He plans to build out the GelSight system with added capabilities to sense temperature and vibrations. A professor in MIT’s Department of Brain and Cognitive Sciences, Adelson recently sat down to talk about his work.

Q: What makes the human hand so hard to recreate in a robot?

A: A human finger has soft, sensitive skin, which deforms as it touches things. The question is how to get precise sensing when the sensing surface itself is constantly moving and changing during manipulation.

Q: You’re an expert on human and computer vision. How did touch grab your interest?

A: When my daughters were babies, I was amazed by how skillfully they used their fingers and hands to explore the world. I wanted to understand the way they were gathering information through their sense of touch. Being a vision researcher, I naturally looked for a way to do it with cameras.

Q: How does the GelSight robot finger work? What are its limitations?

A: A camera captures an image of the skin from inside, and a computer vision system calculates the skin’s 3D deformation. GelSight fingers offer excellent tactile acuity, far exceeding that of human fingers. However, the need for an inner optical system limits the sizes and shapes we can achieve today.

Q: How did you come up with the idea of giving a robot finger a sense of touch by, in effect, giving it sight?

A: A camera can tell you about the geometry of the surface it is viewing. By putting a tiny camera inside the finger, we can measure how the skin geometry is changing from point to point. This tells us about tactile properties like force, shape, and texture.

Q: How did your prior work on cameras figure in?

A: My prior research on the appearance of reflective materials helped me engineer the optical properties of the skin. We create a very thin matte membrane and light it with grazing illumination so all the details can be seen.

Q: Did you know there was a market for measuring 3D surfaces?

A: No. My postdoc Kimo Johnson posted a YouTube video showing GelSight’s capabilities about a decade ago. The video went viral, and we got a flood of email with interesting suggested applications. People have since used the technology for measuring the microtexture of shark skin, packed snow, and sanded surfaces. The FBI uses it in forensics to compare spent cartridge casings.

Q: What’s GelSight’s main application?  

A: Industrial inspection. For example, an inspector can press a GelSight sensor against a scratch or bump on an airplane fuselage to measure its exact size and shape in 3D. This application may seem quite different from the original inspiration of baby fingers, but it shows that tactile sensing can have many uses. As for robotics, tactile sensing is mainly a research topic right now, but we expect it to increasingly be useful in industrial robots.

Q: You’re now building in a way to measure temperature and vibrations. How do you do that with a camera? How else will you try to emulate human touch?

A: You can convert temperature to a visual signal that a camera can read by using liquid crystals, the molecules that make mood rings and forehead thermometers change color. For vibrations we will use microphones. We also want to extend the range of shapes a finger can have. Finally, we need to understand how to use the information coming from the finger to improve robotics.

Q: Why are we sensitive to temperature and vibrations, and why is that useful for robotics?

A: Identifying material properties is an important aspect of touch. Sensing temperature helps you tell whether something is metal or wood, and whether it is wet or dry. Vibrations can help you distinguish a slightly textured surface, like unvarnished wood, from a perfectly smooth surface, like wood with a glossy finish.

Q: What’s next?

A: Making a tactile sensor is the first step. Integrating it into a useful finger and hand comes next. Then you have to get the robot to use the hand to perform real-world tasks.

Q: Evolution gave us five fingers and two hands. Will robots have the same?

A: Different robots will have different kinds of hands, optimized for different situations. Big hands, small hands, hands with three fingers or six fingers, and hands we can’t even imagine today. Our goal is to provide the sensing capability, so that the robot can skillfully interact with the world.

Explained: How to tell if artificial intelligence is working the way we want it to

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer.

These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. 

As the field of machine learning has grown, artificial neural networks have grown along with it.

Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data. But because the models are so enormously complex, even the researchers who design them don’t fully understand how they work. This makes it hard to know whether they are working correctly.

For instance, maybe a model designed to help physicians diagnose patients correctly predicted that a skin lesion was cancerous, but it did so by focusing on an unrelated mark that happens to frequently occur when there is cancerous tissue in a photo, rather than on the cancerous tissue itself. This is known as a spurious correlation. The model gets the prediction right, but it does so for the wrong reason. In a real clinical setting where the mark does not appear on cancer-positive images, it could result in missed diagnoses.

With so much uncertainty swirling around these so-called “black-box” models, how can one unravel what’s going on inside the box?

This puzzle has led to a new and rapidly growing area of study in which researchers develop and test explanation methods (also called interpretability methods) that seek to shed some light on how black-box machine-learning models make predictions.

What are explanation methods?

At their most basic level, explanation methods are either global or local. A local explanation method focuses on explaining how the model made one specific prediction, while global explanations seek to describe the overall behavior of an entire model. This is often done by developing a separate, simpler (and hopefully understandable) model that mimics the larger, black-box model.

But because deep learning models work in fundamentally complex and nonlinear ways, developing an effective global explanation model is particularly challenging. This has led researchers to turn much of their recent focus onto local explanation methods instead, explains Yilun Zhou, a graduate student in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) who studies models, algorithms, and evaluations in interpretable machine learning.

The most popular types of local explanation methods fall into three broad categories.

The first and most widely used type of explanation method is known as feature attribution. Feature attribution methods show which features were most important when the model made a specific decision.

Features are the input variables that are fed to a machine-learning model and used in its prediction. When the data are tabular, features are drawn from the columns in a dataset (they are transformed using a variety of techniques so the model can process the raw data). For image-processing tasks, on the other hand, every pixel in an image is a feature. If a model predicts that an X-ray image shows cancer, for instance, the feature attribution method would highlight the pixels in that specific X-ray that were most important for the model’s prediction.

Essentially, feature attribution methods show what the model pays the most attention to when it makes a prediction.

“Using this feature attribution explanation, you can check to see whether a spurious correlation is a concern. For instance, it will show if the pixels in a watermark are highlighted or if the pixels in an actual tumor are highlighted,” says Zhou.

A second type of explanation method is known as a counterfactual explanation. Given an input and a model’s prediction, these methods show how to change that input so it falls into another class. For instance, if a machine-learning model predicts that a borrower would be denied a loan, the counterfactual explanation shows what factors need to change so her loan application is accepted. Perhaps her credit score or income, both features used in the model’s prediction, need to be higher for her to be approved.

“The good thing about this explanation method is it tells you exactly how you need to change the input to flip the decision, which could have practical usage. For someone who is applying for a mortgage and didn’t get it, this explanation would tell them what they need to do to achieve their desired outcome,” he says.

The third category of explanation methods are known as sample importance explanations. Unlike the others, this method requires access to the data that were used to train the model.

A sample importance explanation will show which training sample a model relied on most when it made a specific prediction; ideally, this is the most similar sample to the input data. This type of explanation is particularly useful if one observes a seemingly irrational prediction. There may have been a data entry error that affected a particular sample that was used to train the model. With this knowledge, one could fix that sample and retrain the model to improve its accuracy.

How are explanation methods used?

One motivation for developing these explanations is to perform quality assurance and debug the model. With more understanding of how features impact a model’s decision, for instance, one could identify that a model is working incorrectly and intervene to fix the problem, or toss the model out and start over.

Another, more recent, area of research is exploring the use of machine-learning models to discover scientific patterns that humans haven’t uncovered before. For instance, a cancer diagnosing model that outperforms clinicians could be faulty, or it could actually be picking up on some hidden patterns in an X-ray image that represent an early pathological pathway for cancer that were either unknown to human doctors or thought to be irrelevant, Zhou says.

It’s still very early days for that area of research, however.

Words of warning

While explanation methods can sometimes be useful for machine-learning practitioners when they are trying to catch bugs in their models or understand the inner-workings of a system, end-users should proceed with caution when trying to use them in practice, says Marzyeh Ghassemi, an assistant professor and head of the Healthy ML Group in CSAIL.

As machine learning has been adopted in more disciplines, from health care to education, explanation methods are being used to help decision makers better understand a model’s predictions so they know when to trust the model and use its guidance in practice. But Ghassemi warns against using these methods in that way.

“We have found that explanations make people, both experts and nonexperts, overconfident in the ability or the advice of a specific recommendation system. I think it is very important for humans not to turn off that internal circuitry asking, ‘let me question the advice that I am
given,’” she says.

Scientists know explanations make people over-confident based on other recent work, she adds, citing some recent studies by Microsoft researchers.

Far from a silver bullet, explanation methods have their share of problems. For one, Ghassemi’s recent research has shown that explanation methods can perpetuate biases and lead to worse outcomes for people from disadvantaged groups.

Another pitfall of explanation methods is that it is often impossible to tell if the explanation method is correct in the first place. One would need to compare the explanations to the actual model, but since the user doesn’t know how the model works, this is circular logic, Zhou says.

He and other researchers are working on improving explanation methods so they are more faithful to the actual model’s predictions, but Zhou cautions that, even the best explanation should be taken with a grain of salt.

“In addition, people generally perceive these models to be human-like decision makers, and we are prone to overgeneralization. We need to calm people down and hold them back to really make sure that the generalized model understanding they build from these local explanations are balanced,” he adds.

Zhou’s most recent research seeks to do just that.

What’s next for machine-learning explanation methods?

Rather than focusing on providing explanations, Ghassemi argues that more effort needs to be done by the research community to study how information is presented to decision makers so they understand it, and more regulation needs to be put in place to ensure machine-learning models are used responsibly in practice. Better explanation methods alone aren’t the answer.

“I have been excited to see that there is a lot more recognition, even in industry, that we can’t just take this information and make a pretty dashboard and assume people will perform better with that. You need to have measurable improvements in action, and I’m hoping that leads to real guidelines about improving the way we display information in these deeply technical fields, like medicine,” she says.

And in addition to new work focused on improving explanations, Zhou expects to see more research related to explanation methods for specific use cases, such as model debugging, scientific discovery, fairness auditing, and safety assurance. By identifying fine-grained characteristics of explanation methods and the requirements of different use cases, researchers could establish a theory that would match explanations with specific scenarios, which could help overcome some of the pitfalls that come from using them in real-world scenarios.

A technique to improve both fairness and accuracy in artificial intelligence

For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

“Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

To predict or not to predict

Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

“It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

Focus on fairness

The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

“We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation.

Four researchers with MIT ties earn Schmidt Science Fellowships

Four researchers with MIT ties — Juncal Arbelaiz, Xiangkun (Elvis) Cao, Sandya Subramanian, and Hannah Zlotnick ’17 — have been honored with competitive Schmidt Science Fellowships.

Created in 2017, the fellows program aims to bring together the world’s brightest minds “to solve society’s toughest challenges.”

The four MIT-affiliated researchers are among 29 Schmidt Science Fellows from around the world who will receive postdoctoral support for either one or two years with an annual stipend of $100,000, along with individualized mentoring and participation in the program’s Global Meeting Series. The fellows will also have opportunities to engage with thought-leaders from science, business, policy, and society. According to the award announcement, the fellows are expected to pursue research that shifts from the focus of their PhDs, to help expand and enhance their futures as scientific leaders.

Juncal Arbelaiz is a PhD candidate in applied mathematics at MIT, who is completing her doctorate this summer. Her doctoral research at MIT is advised by Ali Jadbabaie, the JR East Professor of Engineering and head of the Department of Civil and Environmental Engineering; Anette Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of the School of Engineering; and Bassam Bamieh, professor of mechanical engineering and associate director of the Center for Control, Dynamical Systems, and Computation at the University of California at Santa Barbara. Arbelaiz’s research revolves around the design of optimal decentralized intelligence for spatially-distributed dynamical systems.

“I cannot think of a better way to start my independent scientific career. I feel very excited and grateful for this opportunity,” says Arbelaiz. With her fellowship, she will enlist systems biology to explore how the nervous system encodes and processes sensory information to address future safety-critical artificial intelligence applications. “The Schmidt Science Fellowship will provide me with a unique opportunity to work at the intersection of biological and machine intelligence for two years and will be a steppingstone towards my longer-term objective of becoming a researcher in bio-inspired machine intelligence,” she says.

Xiangkun (Elvis) Cao is currently a postdoc in the lab of T. Alan Hatton, the Ralph Landau Professor in Chemical Engineering, and an Impact Fellow at the MIT Climate and Sustainability Consortium. Cao received his PhD in mechanical engineering from Cornell University in 2021, during which he focused on microscopic precision in the simultaneous delivery of light and fluids by optofluidics, with advances relevant to health and sustainability applications. As a Schmidt Science Fellow, he plans to be co-advised by Hatton on carbon capture, and Ted Sargent, professor of chemistry at Northwestern University, on carbon utilization. Cao is passionate about integrated carbon capture and utilization (CCU) from molecular to process levels, machine learning to inspire smart CCU, and the nexus of technology, business, and policy for CCU.

“The Schmidt Science Fellowship provides the perfect opportunity for me to work across disciplines to study integrated carbon capture and utilization from molecular to process levels,” Cao explains. “My vision is that by integrating carbon capture and utilization, we can concurrently make scientific discoveries and unlock economic opportunities while mitigating global climate change. This way, we can turn our carbon liability into an asset.”

Sandya Subramanian, a 2021 PhD graduate of the Harvard-MIT Program in Health Sciences and Technology (HST) in the area of medical engineering and medical physics, is currently a postdoc at Stanford Data Science. She is focused on the topics of biomedical engineering, statistics, machine learning, neuroscience, and health care. Her research is on developing new technologies and methods to study the interactions between the brain, the autonomic nervous system, and the gut. “I’m extremely honored to receive the Schmidt Science Fellowship and to join the Schmidt community of leaders and scholars,” says Subramanian. “I’ve heard so much about the fellowship and the fact that it can open doors and give people confidence to pursue challenging or unique paths.”

According to Subramanian, the autonomic nervous system and its interactions with other body systems are poorly understood but thought to be involved in several disorders, such as functional gastrointestinal disorders, Parkinson’s disease, diabetes, migraines, and eating disorders. The goal of her research is to improve our ability to monitor and quantify these physiologic processes. “I’m really interested in understanding how we can use physiological monitoring technologies to inform clinical decision-making, especially around the autonomic nervous system, and I look forward to continuing the work that I’ve recently started at Stanford as Schmidt Science Fellow,” she says. “A huge thank you to all of the mentors, colleagues, friends, and leaders I had the pleasure of meeting and working with at HST and MIT; I couldn’t have done this without everything I learned there.”

Hannah Zlotnick ’17 attended MIT for her undergraduate studies, majoring in biological engineering with a minor in mechanical engineering. At MIT, Zlotnick was a student-athlete on the women’s varsity soccer team, a UROP student in Alan Grodzinsky’s laboratory, and a member of Pi Beta Phi. For her PhD, Zlotnick attended the University of Pennsylvania, and worked in Robert Mauck’s laboratory within the departments of Bioengineering and Orthopaedic Surgery.

Zlotnick’s PhD research focused on harnessing remote forces, such as magnetism or gravity, to enhance engineered cartilage and osteochondral repair both in vitro and in large animal models. Zlotnick now plans to pivot to the field of biofabrication to create tissue models of the knee joint to assess potential therapeutics for osteoarthritis. “I am humbled to be a part of the Schmidt Science Fellows community, and excited to venture into the field of biofabrication,” Zlotnick says. “Hopefully this work uncovers new therapies for patients with inflammatory joint diseases.“

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