MIT engineers create nanoparticles for delivering personalized cancer vaccines

A nanoscale particle with a unique structure, developed at MIT, holds potential as a customizable delivery vehicle for personalized cancer vaccines.

The backbone of the vaccine is a modified, harmless bacteriophage called M13, which forms a nanofiber that can be coated with cancer antigens. By precisely controlling the density of those cancer proteins, the researchers found that they could create a vaccine that prevents the development of colon tumors in mice.

This type of delivery particle could be adapted to carry many different antigens, allowing for the creation of personalized vaccines to treat individual patients’ tumors, the researchers say.

“The great thing about this is that it’s modular and we could switch out different antigens on demand, so you could design it to be patient-specific and target a known antigen for a particular kind of cancer,” says Angela Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering and a member of the Koch Institute.

Belcher is the senior author of the study, which appears today in the journal Advanced Healthcare Materials. Shengnan Huang, research scientist at the Koch Institute, is the paper’s lead author. Jack Rudrum, Huaiyao Peng, and Hao Nghi Luu, graduate students at the Koch Institute, are also authors of the paper.

Viral vaccine assembly

For more than 20 years, Belcher’s lab has been deploying the M13 bacteriophage in a variety of applications. This virus, which is harmless to humans, consists of a single strand of DNA coated in a protein shell, or capsid. 

Belcher has previously developed techniques for generating materials by engineering these viruses to self-assemble into nanoscale scaffolds. By coating these scaffolds with inorganic materials, they can create functional devices such as batteries, semiconductors, solar cells, and catalysts, with precise control over the electronic, optical, and magnetic properties of the materials.

More recently, Belcher’s lab has been developing methods to control features of the phage that could allow it to be repurposed for medical applications, including vaccine delivery. 

“We’re taking this toolkit that we’ve worked on and developed over decades and formatting it to be specific for vaccine therapeutics,” Belcher says. “Some of the lessons learned from making batteries and all these other things are now being fine-tuned to make personalized vaccines.”

The capsid that surrounds the M13 phage forms a long filament made from viral proteins. To create vaccines, the researchers modify one of these proteins, known as pVIII, by attaching an antigen to the end of the protein. This can be done by adding the gene for the antigen to the virus’s DNA.

Some of the lab’s previous work had suggested that changing the density of cancer antigens attached to the capsid could affect the strength of the resulting immune response. In the new study, Huang wanted to find a way to control that density. 

“The antigen is the specific target we want to initiate the immune response against, which means we have to ensure the right dose of antigen to make sure that we can have a strong immune response,” she says.

Huang discovered that she could increase the density of the modified pVIII proteins by adding one or two negatively charged amino acids to the protein sequence. The density could be further fine-tuned by adding a genetic mutation that slows down production of the antigen protein.

In this study, the researchers also figured out how to boost the phage’s own ability to act as a vaccine adjuvant, by changing the genetic sequence of the virus’s DNA. Most vaccines need to be delivered along with an adjuvant such as aluminum hydroxide, which helps to stimulate a strong immune response. However, the M13 phage can act as an adjuvant itself because single-stranded DNA activates a pathway called TLR9, which is part of the body’s innate immune system.

“We can control the exact length. We can control the exact antigen expression, density, pattern, the exact adjuvant amount. A lot of times with a vaccine, you’ll have to put a different adjuvant with it, but now it’s all genetically encoded,” Belcher says. 

Targeting tumors

Using these techniques, the researchers showed that they could design phages that express an antigen called SIINFEKL at densities ranging from 6 percent to 70 percent. SIINFEKL, a small protein that can be engineered into tumors, is often used as a model to test vaccine effectiveness.

The researchers tested two of these vaccines in mice, one with 6 percent density and one with 35 percent, and found that the one with 35 percent was the most protective. 

After vaccination with the higher-density particles, mice were much less likely to develop tumors when later exposed to melanoma cancer cells expressing the SIINFEKL antigen. These mice also generated more T cells targeting SIINFEKL. This is, in part, because dendritic cells, which help to activate T cells, were more likely to take up vaccines with higher antigen density, the researchers found.

In another set of experiments, the researchers created and tested a vaccine carrying a neoantigen, which is only present in the tumor cells. This protein, known as ADPGK, is specifically expressed by colon tumors in a mouse model of cancer. When given the higher density vaccine, these mice were much less likely to develop colon tumors.

In this study, the researchers tested the vaccine as a way to prevent cancer from developing, and in their previous work, they adapted it for use to treat mouse colon tumors that have already formed. In the future, they also hope to use it to combat other types of cancer.

“This interesting work adds another cog to improve the translational potential of the M13 phage platform for cancer immunotherapeutics,” says Alberto Danielli, an associate professor of molecular biology at the University of Bologna, who was not involved in the study. “I look forward to seeing whether this platform is also effective against established tumors and whether additional tumor antigens can be displayed with comparable molecular and immunological efficacy.”

This platform could be especially well-suited for personalized cancer vaccines, because many different antigens could be swapped into the vaccine.

“Because this is a modularly engineered platform, we can change the antigen we are interested in. For colon cancer, we use this specific antigen. For other types of cancer, we can just plug in other antigens and they can be assembled in this platform,” Huang says.

The research was funded, in part, by the Koch Institute Frontier Research Program, the Marble Center for Cancer Nanomedicine, and the U.S. Defense Advanced Research Projects Agency.

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Next Haunt: A scary “problem set” in the basement of Next House

You know it’s autumn when the scent of pumpkin spice latte begins to waft through the air at the Dunkin’ in the Stratton Student Center, and students in MIT’s Next House residence hall diligently begin to piece together the many parts needed to showcase Next Haunt — a combination of a Halloween haunted house and escape room, built in the basement of the hall.

Every fall since 2014, students — also known as hauntmasters — spend several weekends building the room and preparing puzzles, physical tasks, or word challenges to obtain a code to open locks that let participants escape the house. This year’s theme, titled “Where the Light Ends,” follows “Terror Under Destruction,” “Next Stop: MBTA,” and last year’s theme: “Get in the HOLE.”

Next Haunt is free and open to all MIT students, also known as “test subjects.” Participants typically spend around 20 minutes in small, dark, enclosed rooms, working with four to six teammates to find the clues they need to escape.

Along the way, they have to navigate zombies — played by student actors — who may shout at them or splash them with blood (fake) while they solve the puzzles. If a zombie touches a participant, they become “paralyzed” and can no longer move, but they can still communicate and relay information to their teammates.

Teams must work closely, strategize, delegate tasks, and communicate effectively as the clock counts down. The group with the quickest escape time gets bragging rights, and every group signs their names on a wooden board to show that they participated.

MIT junior Kiyoko Hayano, Next Haunt co-producer, says, “I became involved in Next Haunt during my first semester at MIT because I had experience with larger-scale painting. I split the build director’s role with two other freshmen, including Michelle Liang, our current assistant producer. I thought this would be a great opportunity to take a break from psets and work with my hands, and fell in love with the club.”

Safety first

Before the room is built and the puzzles and costumes are created, careful planning takes place to ensure the escape room is safe, fire-resistant, and passes an on-site inspection. MIT’s Campus Construction and Environment, Health, and Safety offices are involved from the start, and all students who partake in the build are required to attend safety orientation and tool training.

“We currently have 18 people on our executive team, and we have 12 outside volunteers come help over the first few weekends of Next Haunt build,” says Hayano. “We then run Next Haunt for six-and-a-half hours every night for two weekends at the end of October.”

Once the build is complete, several alumni volunteers conduct a “test run” to make sure the puzzles and clues are hard, but not impossible, and anyone who wants to leave the room before the allotted time can easily find the door out built with Velcro closures. 

Escaping by solving the puzzles is not easy, and not all participants are successful. Last year, only 16 percent of the 73 teams successfully got out of the room. The winning team escaped in just under 15 minutes.

Alumnus and previous Next Haunt producer Allen Lin ’26 notes, “I became involved with Next Haunt in my freshman year after being a test subject myself, and I loved the creative and building process so much I wanted to join. My favorite part was seeing all the teams go through the house, especially on the last day when we hosted past Haunt alumni. It was just as rewarding to see how much our test subjects enjoy the house as it was to share the many laughs and moments of camaraderie among my team. I’m excited to come back as an alum and see what the Next Haunt team built this year!”

Environmentally mindful

At the end of Next Haunt, the escape room is dismantled, and anything that can be repurposed safely is stored for the following year’s build. The executive crew then goes to a Boston-area escape room to celebrate their hard work.

“It’s a joy working collaboratively with students and connecting them to campus partners who lend their expertise as the build develops," says Milo Alto, manager of special projects, housing and residential services. “Students get very creative every year with the theme and costumes, but they also learn valuable leadership skills along the way — time management, delegation, working with peers, and implementation of a detailed plan. It’s also a great way to build community.”

Interested MIT students can sign up to help plan and work on the 2027 build.

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Magnetic sensors track muscle movement, for better prosthetic limb control

MIT researchers have shown that they can accurately track muscle movement using tiny magnetic beads implanted in the muscle. These measurements could make it easier for people with certain types of amputations to control their prosthetic limbs.

For decades, researchers have been working to develop neuroprosthetic limbs, which use neural information from muscles remaining after an amputation to guide the prosthetic limb. Measurements of a muscle’s length, velocity and activation can be fed into the control algorithms that guide the limbs, allowing individuals with leg amputation to walk with a more natural gait, or those with arm amputation to manipulate objects more effectively.

Until now, these devices have relied on muscle activity measurements from electrodes placed on the surface of the skin. However, those measurements have not proven reliable enough to ensure safety while walking with a neuroprosthetic leg. 

The MIT team hopes that their new magnetic tracking system, which offers more accurate measurements, will enable commercial development of such limbs.

“For four decades, the standard methodology for neurally controlling prostheses has been to use skin electrodes to measure muscle activation. However, to control a prosthetic limb, joint position, torque, or impedance has to be controlled, and one cannot directly determine such mechanical signals using electrical activation alone,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

In the new study, which appears today in Nature Sensors, the researchers demonstrated the accuracy of the magnetic measurements in three participants with below-knee amputations.

“This interface is minimally invasive, so it could be accessible to more people. You place two beads in the muscle and you have access to all of this rich information about the muscle dynamics,” says Christopher Shallal, an MIT graduate student in the Harvard-MIT Program in Health Sciences and Technology and one of the lead authors of the paper.

The paper’s other lead author is Cameron Taylor PhD ’20, an assistant professor of biomedical engineering at the University of North Carolina at Chapel Hill.

Tracking muscles

For many years, Herr’s lab has been developing human-machine interfaces that can be used to control an artificial limb in a way that makes it more like using a natural limb. Obtaining accurate measurements of how the user is moving the muscles at the amputation site is critical to the success of this type of prosthesis.

Electrodes on the surface of the skin can reveal the electrical activation of the muscle below, and those measurements can be converted to estimates of how much a muscle is contracting or flexing. Based on that input, a robotic controller generates commands that guide the prosthetic limb in the user’s intended direction. 

However, that measurement approach, known as electromyography, does not always provide accurate enough information. The electrical signal is not a perfect proxy for muscle dynamics; for example, when a muscle is held at a fixed length for several seconds, the signal fades. Additionally, the electrodes may pick up signals from muscles other than the intended target, and sweat can interfere with detection of the signal.

Last year, Herr and his colleagues reported a “bionic knee” prosthesis that uses electrodes implanted in the muscles to provide accurate measurements and feed them into a robotic controller. This approach proved successful in a small clinical study, but alongside that work, Herr’s team has also been working on a less invasive approach that relies on magnets instead of electrodes.

With this approach, known as magnetomicrometry, pairs of magnets are implanted into muscles. When a muscle contracts or stretches, external sensors placed on the skin can measure the change in distance between the magnets, allowing muscle length and velocity to be calculated.

“Magnets can be tracked wirelessly through the body, so you can get very specific muscle signals and measure the mechanical output of the muscle without needing to be extremely invasive,” Shallal says.

Better control

In a 2021 study led by Taylor, Herr’s lab first demonstrated the magnet-based technique in animal models. The new Nature Sensors study extends the approach to humans with long-term magnetic implants. For the study, the researchers recruited three participants who have below-knee amputations.

All of these participants had a type of amputation that creates what’s called an agonist-antagonist myoneural interface (AMI). With this surgical procedure, muscle pairs are reconnected during surgery so that they still dynamically communicate with each other within the residual limb. This sensory feedback, called proprioception, helps the wearer of the prosthesis to decide how to position and move their prosthetic limb. 

Participants had magnets implanted in up to four muscles of the ankle joint. The surgeries were performed at Brigham and Women’s Hospital in collaboration with plastic surgeon Matthew Carty, a co-author of the study. The beads are 3 millimeters in diameter, about the size of a sesame seed. The magnets are made from an alloy of neodymium, iron, and boron, and are coated with gold and a polymer called parylene C to make them biocompatible. The magnetic signals can be read from a depth of up to 6 centimeters from the skin’s surface, using a customized external sensor that is worn over the skin, positioned over the muscles that are being tracked. 

Each of the participants in this study also had electrodes implanted into the same muscles that received magnetic beads. This allowed the researchers to compare the accuracy of three different types of measurements: magnetomicrometry, electromyography with implanted electrodes, and electromyography with electrodes on the surface of the skin.

During the experiments, the participants were not wearing a prosthetic limb but were asked to perform tasks such as moving a cursor on a screen to a particular target using their muscle movements. Using each of the three measurement techniques, the signals generated by the muscle movements were translated into control signals that, in this case, moved the cursor but in the future could be fed into the controller of a robotic ankle-foot prosthesis.  

“The cursor on the screen represented the degrees of freedom that they would control on a robotic ankle-foot prosthesis. This virtual neuroprosthetic task allowed us to compare the performance across different neural interfaces, and our results suggest that the magnet-based approach can offer more accurate and stable control,” Shallal says.

The researchers are now performing a larger study in which they are evaluating how well people can control prosthetic limbs using measurements from the magnetic beads. 

The research was funded, in part, by the Yang Tan Collective at MIT as well as a National Science Foundation Graduate Research Fellowship.

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New system gives users more control over their online experience

A user who is frustrated with changes a social media company made to their favorite messaging platform, like the introduction of an annoying AI chatbot, can’t do much to address the situation, short of leaving the platform.

Now, MIT researchers are offering an alternative path.

They’ve created Social.Wiki, a system that allows users to easily edit any social application made within Social.Wiki, and choose which of the edits made by others they want to see. 

For example, a user could change a messaging site on Social.Wiki to remove an AI chatbot and instead add a restaurant voting feature so their group chat can finally decide where they are going out for dinner.

“The infrastructure that we are all dependent on online is very rigid. It is based on these one-size-fits-all applications that we don’t have a lot of power to change. This project is about trying to imagine what a different web would look like where anyone can participate,” says Theia Henderson, an electrical engineering and computer science (EECS) graduate student, and lead author of a new paper on Social.Wiki.

The researchers envision a world in which, if a user is unhappy with their social app, they can edit it to change it however they like — without getting cut off from their social connections.

The platform enables users with no coding expertise to create a range of websites, including social sites like photo sharing networks, discussion forums, and dating apps. It handles all back-end work, eliminating the need for creators to set up servers or arrange databases.

Social.Wiki employs a decentralized infrastructure that allows users to retain ownership of their data as they create, edit, and navigate social websites within the platform. It also includes a “data guard” that protects user data from malicious or buggy applications. 

Through case studies and user tests, the researchers demonstrated the breadth of applications people can create with Social.Wiki, including a rideshare app where riders propose the prices they are willing to pay and a collaborative canvas for creating art together.

While Social.Wiki is still in its early days, the researchers plan to continue refining the platform as they deploy it to a wider audience. 

“We want to empower the creation of far more social applications that meet the needs of unusual small groups. Each group is unusual, but everyone is in unusual groups. Everybody has their own needs for social applications that we want to be able to address,” says David Karger, professor of EECS, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of the paper.

Henderson and Karger are joined on the paper by EECS graduate students Carmel Schare and Ana Dodik; Clemens Klokmose, a professor of computer science at Aarhus University in Denmark; Ziv Epstein, a postdoc in the MIT Media Lab; and David D. Clark, an MIT research scientist. The paper will be presented at the ACM Symposium on User Interface Software and Technology in November.

Letting users choose

Social.Wiki is a Wikipedia-like system in which users can navigate to different web applications or create their own web application within the Social.Wiki ecosystem. Each website has an “edit” button that allows any user to change the content of the site, from the text color on a message board to the pricing algorithm in a ride-sharing app.

Clicking the “edit” button in Social.Wiki opens a page that lets users view and edit the source code manually or prompt an AI assistant to make changes to the site. 

This platform builds on the researchers’ prior work on Graffiti, a web framework with a decentralized infrastructure that makes building personalized applications easier. 

With Social.Wiki, the researchers sought a collaboratively editable system that would allow anyone to revise web applications to be ideally suited for their needs and the needs of groups they belong to. 

To enable this structure, Social.Wiki relies on a strategy the researchers call “plural governance.” Instead of a central moderator deciding which content users will see, each Social.Wiki user can choose the rules that determine which edits they will see on Social.Wiki webpages.

Users can also identify “trusted editors,” so they will see edits from individuals or organizations they consider reliable.

For instance, a user could customize their rules, or “governance policy,” so they only see edits to a site that have been endorsed by at least two of their friends. When the Social.Wiki browser loads a site for a user, it applies the governance policy that user selected to decide which version of the site to present.

For example, based on each user’s choices and edits, the system might show one user a ride-sharing app with a uniform pricing algorithm while another user might see the same app with a dynamic pricing algorithm.

“No website is tied to a particular machine. This means we have no centralized ownership over the websites or the underlying infrastructure,” Henderson explains.

Security and creativity

In this decentralized infrastructure, user data are stored separately from the websites that access it, which helps keep data secure.

Social.Wiki also employs a data guard, which prompts a user to review each request from a website that is attempting to access their data.

“In Social.Wiki, these websites are held in common and anybody can edit them, but the data guard makes sure that each user stays in control of what happens to their data,” Karger adds.

The researchers evaluated Social.Wiki through several case studies. For one, they developed a messaging platform that allows participants to modify the chat’s styling and interface without affecting the experience of others. In another, they built a collaborative ride-sharing app that enables riders to adjust the fare they want to pay using a transparent “living wage” calculator.

They also sponsored a hackathon in which users built Social.Wiki applications.

Participants developed a range of creative sites, including a housing co-op communication tool that organizes chats on a map of the co-op, as well as a garden-themed microblogging site where posts are “planted” as flowers.

“Even the little bit that has been done so far with Social.Wiki demonstrates that there is so much more that could be done with social apps if users could specialize them to meet a particular group’s needs,” Karger says.

Moving forward, the researchers are working to deploy Social.Wiki to a broader audience and gather feedback from more diverse user groups. They also want to make technical upgrades to enable end-to-end encryption on private message data and provide better developer tools.

This work was funded, in part, by Schmidt Sciences and the National Science Foundation (NSF). 

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A soft robot could help doctors diagnose and treat disorders of the small intestine

Disorders of the small intestine are often difficult to diagnose and treat. More than 20 feet long, an inch in diameter, and located deep within the body, this organ is harder to access than other parts of the digestive tract, such as the colon or stomach.

To overcome those challenges, MIT engineers have designed a new robotic enteroscope that can move easily through the narrow passage of the small intestine, allowing it to perform imaging, deliver drugs, and remove small polyps or tissue samples for biopsy.

In designing the new robot, the researchers were inspired by the locomotion of snakes, as well as the scaly skin that helps them gain traction on slippery surfaces. A special textured coating helps the robot remain attached to the mucosal lining of the intestine as it inches its way through the narrow tube. 

Similar to an endoscope, the robot contains a variety of tools to help detect and treat any abnormalities that it might encounter.

“When we evaluate the small intestine, we do it for either diagnosis or treatment. If we find a little polyp that needs to be removed, this robot allows us to take a biopsy or remove it,” says Giovanni Traverso, professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard.

Traverso is the senior author of the study, which appears today in Science Robotics. The paper’s lead authors are former MIT postdoc Binbin Ying, who is now an assistant professor at University of Texas Southwestern Medical Center; MIT graduate student Ahmad Mujtaba Jebran; and former MIT research scientist Sahab Babaee.

Snaking through the intestine

The most common problems that doctors need to evaluate within the small intestine include bleeding, lesions, and blockages.

Currently, the only way to diagnose those intestinal complications is with a procedure known as double balloon enteroscopy. During the procedure, an enteroscope advances through the small intestine as a health care worker alternately inflates and deflates two balloons that help propel it through the tract. 

This process is slow and laborious, so the MIT team wanted to design an alternative enteroscope that would be easier to control and could move faster through the intestine.

“The fundamental challenge that we’re trying to overcome is how to evaluate that 20-to-25-foot segment of the small intestine in a way that can be more automated,” Traverso says.

The new tube-like robot moves through the narrow small intestine using a locomotion strategy similar to that of the snake. When slithering along the ground, a snake can steer itself by first contracting muscles on one side of the body, then the other. 

To mimic this, the researchers designed multifunctional actuators that control the robot’s movement. Each actuator contains four chambers, which can be inflated in different combinations, prompting the tube to extend or bend in any direction. 

“By tuning the amount of air that goes into these chambers, you can tune the direction that the robot is moving in,” Traverso says.

The head and tail segments of the robot contain additional expandable anchoring balloons that take turns inflating and deflating, helping the robot move forward. These head and tail segments are coated with special patterned sleeves inspired by kirigami — a variation of origami that involves cutting paper as well as folding it.

Using this technique, the researchers created coatings that lie flat until they are stretched by the expansion of the balloon, which causes three-dimensional kirigami needles to pop out. Similar to the scales of a snake, these needles give the robot more traction within the slippery lining of the intestine. 

In previous work, Traverso’s lab has used this approach to create coatings for shoe soles that give them more traction, as well as stents that can temporarily lodge in the esophagus and release drugs. 

“By using kirigami sleeves to enhance surface friction, the robot can generate localized tissue engagement traction for locomotion without relying on excessive stretching of the delicate small intestinal tissue,” Jebran says.

Diagnosis and treatment

The system also includes electronics that allow for navigation, with a controller that the person operating the device can use to guide the robot through narrow passages and around tight corners. The soft robot is tethered to the external controllers by a cable and is designed so that it could enter the body through either the mouth or the rectum. 

The robot contains tools including a camera for imaging, a sprayer for drug delivery, and clips that can take tissue biopsies or remove polyps. 

In animal studies, the researchers showed that the robot could travel about six and a half feet through the small intestine. They also successfully demonstrated the drug sprayer, tissue clips, and camera. The team is now working on improving the navigation systems and testing this approach for additional applications in the small intestine.

“We hope we can continue to move it forward to eventually develop systems that can help achieve more automated evaluation of the small intestine,” Traverso says.

The research was funded, in part, by the Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, a Banting Postdoctoral Fellowship, an NSERC Postdoctoral Fellowship, the UT Southwestern Medical Center Startup Fund, Samsung Semiconductor and Eurnekian Nanotechnology Graduate Student Fellowships, the Cooke Undergraduate Transfer Scholarship, and the MIT Undergraduate Research Opportunities Program.

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3 Questions: Why infrastructure is entering a defining era

For centuries, infrastructure has evolved alongside technology. New materials and engineering techniques enabled taller buildings, longer bridges, and larger cities. Computing and digital technologies later transformed how infrastructure was designed, monitored, and operated.

Now, the pace of change is accelerating, and challenges are arriving simultaneously. Climate change is increasing pressure on water, energy, and other resources. Artificial intelligence is beginning to transform engineering and design, and its energy needs, along with aging grid infrastructure, is creating significant challenges, as well as new opportunities. In addition, geopolitical complexity is exposing vulnerabilities in global supply chains, and demand for infrastructure is rising, particularly in the developing world.

The result is a fundamental challenge: How can the world build the infrastructure it needs without simply repeating the extractive, carbon- and resource-intensive model of the 20th century?

In this interview, Ali Jadbabaie, JR East Professor and head of the MIT Department of Civil and Environmental Engineering, says the answer will require rethinking not only how infrastructure is designed and built, but how it is financed and operated, and bringing together the many stakeholders who shape those decisions. On Oct. 28, Jadbabaie will join experts from around the world in Madrid to explore these topics at the World Infrastructure Forum, which was co-founded by MIT’s Department of Civil and Environmental Engineering, UPM, the World Bank Group, and the University of Texas at Austin.

Q: Infrastructure has always evolved in response to technological advances. What makes this era different?

A: In the 18th century, civil engineering emerged as the civilian counterpart to military engineering, building the dams, bridges, and canals of its time. Industrialization in the 19th century brought railways and sewage systems. In the early 20th century, the focus scaled up to buildings, highways, tunnels, and metro systems, and by mid-century the infrastructure needed to support megacities and skyscrapers.

Every time technology has changed, with information technologies, computing, sensing, and, more recently, digital transformation, it has changed the shape of infrastructure. It has even changed the definition of what infrastructure is. Today, we consider the power grid, semiconductor manufacturing plants, and data centers part of infrastructure, along with wireless infrastructure like cellphone towers and the infrastructure for high-speed internet, just as much a part of our critical infrastructure as roads, water systems, and power grids. 

What is different now is the combination and speed of the changes we're facing. Climate change, resource constraints, and geopolitical instability are putting pressure on the way we designed infrastructure in the 20th century. At the same time, many people are moving out of poverty because of recent economic growth in the developing world. In fact, according to models, by 2028, for the first time in the history of humanity, the median human inhabitant of the planet will live off $10 a day. As more people move out of abject poverty, there will also be an increase in the demand for infrastructure.

If we try to meet that demand using the same resource-intensive model we used in the 20th century, we simply won't have the resources to do it. The question then becomes: How do we use the remaining resources more efficiently? We need to find a way to provide more infrastructure while using less water, energy, and materials, and produce less carbon.

This is where digital transformation and AI become potentially transformative. Faster and cheaper sensing, greater access to data, and better sensing, actuation, and control can help us make infrastructure decisions more efficiently. AI is slowly permeating the physical world, and we expect it to revolutionize the engineering design process, allowing us to explore many more possibilities, much faster and at a lower cost. 

But the story of that hasn't been written yet, and the outcomes could still be very positive, depending on what we do.

Q: Can you give an example of infrastructure systems becoming more interconnected in a way that changes how we build or manage them?

A: The revolution in information technology and digital transformation over the past 30 years has made infrastructure systems more interconnected. You see this in cities. If the power goes out, water and gas systems can stop functioning, internet fails, and transportation system comes to a halt. If communications networks fail, many other services are affected. We saw this during Covid, when disruptions in supply chains affected energy networks, critical materials, and manufacturing. We see it when geopolitical conflicts disrupt oil and energy markets and those disruptions propagate through other parts of the global economy.

Infrastructure systems that were once managed separately are increasingly dependent on one another. As we connect more things together for more efficiency, we also increase systemic risk and expose the systems to rare but catastrophic failures. They have become rare, but when they happen, they're much bigger and more consequential.

The really tricky thing is that developing infrastructure involves much more than designing and building it. The whole value chain includes financing and operating it, as well as understanding who will use it and what those users need and prefer.

So, designing infrastructure is a highly transdisciplinary, sociotechnical endeavor, involving social, technical, and economic considerations. Understanding how these systems are designed is increasingly important because they are becoming more complex, making decision-making particularly difficult.

There’s also so much uncertainty in large infrastructure projects. They’re very capital-intensive, the business is lower margin than many other industries, and projects take a long time. Combine all of that, and it has historically made the industry very conservative.

But the transformations coming from AI and digital transformation are potentially offering a new way forward. What that new way looks like, no one has quite delineated it yet.

Q: How are you and your colleagues working to change how these decisions get made?  

A: We realized that if we're going to develop a new approach to infrastructure, no single discipline, company, or country can do it alone. Infrastructure value chain is too interconnected, and the challenges are too large.

That's why MIT’s Department of Civil and Environmental Engineering co-founded the World Infrastructure Forum with the World Bank Group, the Universidad Politécnica de Madrid, and University of Texas at Austin. The goal is to bring together academia, industry, governments, and financial institutions across the infrastructure value chain and create an ongoing dialogue about how infrastructure should be designed, financed, built, and operated in the decades ahead.

This is particularly important because the biggest opportunities for new infrastructure development are not necessarily in the United States or other countries with mature infrastructure systems. Those countries still face significant needs for modernization and new investment. But over the coming decades, much of the world's population growth, and much of the infrastructure that will need to be built, will be in countries where infrastructure systems are still developing. 

Those countries have an opportunity to benefit from modern infrastructure without simply repeating the model of the 20th century. But capturing that opportunity will require new approaches to technology, financing, engineering, and governance.

We're trying to create an ecosystem rather than just a forum, a mechanism for continuing the conversation among the different stakeholders across the infrastructure value chain. We've established five working groups and an advisory group of more than 30 leaders from across the infrastructure value chain, including engineering and construction, materials, finance, multilateral institutions, and academia.

Ultimately, the question is not whether we need a new way of developing infrastructure. The question is how we create it and who is going to execute it. There is no single person, company, or country that can solve this problem, because infrastructure itself is interconnected. We need to build the new model together.

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Making machines that can listen

The crack of a bat accompanied by a roar from the crowd can capture a baseball fan’s attention, even if they miss seeing the home run. Humans may be visual creatures, but we also perceive the world around us by listening to it. 

To improve artificial perception, MIT Professor Paris Smaragdis SM ’97, PhD ’01 seeks to make machines that can hear and understand sound even better than humans.

A machine with super-human hearing could rapidly pinpoint the location of a traffic accident by listening for squealing tires and crunching metal, or identify when an industrial drill might break while it is boring through dense bedrock to dig a geothermal well.

“I like to say that I do the same thing the computer vision folks do, but instead I do it for hearing,” says Smaragdis, a professor with joint appointments in the Music and Theatre Arts program of the School of Humanities, Arts, and Social Sciences, and the Department of Electrical Engineering and Computer Science in the School of Engineering and the MIT Schwarzman College of Computing. 

His research group explores areas like machine learning for sound source separation, smart devices that can augment the audio surroundings of humans, and better tools to help people understand, manipulate, analyze, and create music.

“Having had a musical background, I’ve been interested in examining these ideas. What does it take to make machines understand sound? There is a huge domain of applications for machines that can listen,” says Smaragdis, who joined MIT in 2025.

A musical prelude

Smaragdis, who grew up in Greece, began playing classical guitar at a young age. He enjoyed playing traditional pieces but reached a turning point in his musical journey when his father brought home a small synthesizer.

“That little synthesizer had a function that allowed you to make your own sounds, and I became completely captivated by the possibilities,” he says.

Synthesizers were taking a more prominent place in pop music during the 1980s, adding to his fascination with the technology.

As he advanced through school, Smaragdis wanted to study music made with synthesizers. But this was not an educational path offered by the more traditional European conservatories at the time.

Instead, he enrolled at Berklee College of Music in Boston, into a program where he could explore the use of computers and synthesizers to shape and manipulate sound.

“With computers and synthesizers, I could make new sounds that have never been heard before,” he says.

At the same time, Smaragdis became immersed in jazz fusion. He declared bass as his major at Berklee since it would allow him more opportunities to play with jazz ensembles.

“I found the bass to be sonically more interesting than the guitar. It was a more unexplored domain, and I’ve always gravitated toward unexplored domains,” he says.

That interest in the unexplored carried over into his coursework. He and mentor Richard Boulanger, a professor of electronic production and design, created new tools to help people use computers to write, produce, and enhance music. 

Through his studies at Berklee, Smaragdis embarked on a collaborative project with Professor Barry Vercoe (who passed away in 2025) at the MIT Media Lab to create software synthesizers for music creation. Building on that project, Smaragdis pursued graduate studies in Vercoe’s group, where he completed his master’s degree and PhD, followed by a postdoc.

“In the Media Lab, I got exposed more to the computer part of computer music. That’s where my attention shifted a bit. Now I really wanted to make computers that could understand sound,” he says.

Tuning in to unexplored domains

At that time, many students in the Media Lab were using signal processing techniques to help machines handle sound. Smaragdis chose a less explored approach — machine learning — to separate himself from the pack.

He found that training machine-learning models to understand sound offered advantages that couldn’t be attained with traditional signal processing techniques.

“Over time, it became really clear to me that we need to teach computers how to listen. We can’t just tell them how to listen. If anything, we don’t even know how we humans listen,” he says. “We don’t have a vocabulary to explain what it means to listen, or how to tell the difference between a cat’s meow and the sound of a violin.”

After finishing his postdoc, Smaragdis began a career at Mitsubishi Electric Research Labs, where he worked on artificial intelligence systems that could scan video soundtracks and understand what is happening on screen. 

“What if you record an entire soccer game, but you only want to watch the highlights? In computer vision, it is very difficult to identify what a highlight is. But on the other hand, if you only listen to the sounds, then when the crowd goes crazy you would know that something interesting is happening,” he says.

That system was eventually incorporated into electronic devices, including a personal video recording system Mitsubishi was developing at the time, and into traffic monitoring systems and cell phones.

Smaragdis later joined Adobe, where he created the audio research team that built user-friendly tools to help artists, musicians, and producers manipulate sound without needing a lot of expertise.

“As we did that work, I kept coming up with deeper technical problems that needed to be resolved. I jumped into academia to address that itch,” he says.

Smaragdis joined the faculty at the University of Illinois at Urbana-Champaign, where he and his group incorporated the latest machine-learning techniques into audio-processing systems.

One of the technologies they developed utilized neural networks for separating individual sounds from audio mixtures. That project caught the attention of the team working on the 2021 documentary mini-series “The Beatles: Get Back.”

The production team enlisted Smaragdis to help clean up some archival Beatles recordings.

“That was a very different way of audio processing than what I was doing in academia. Now, I had to do something that sounded good but didn’t necessarily have to look good on paper. We didn’t have a formula we could optimize,” he says.

Every recording involved different challenges. For instance, in one clip John Lennon was playing music while Paul McCartney was horsing around, reading headlines from newspapers and making fun of George Harrison.

“We were trying to come up with a technological solution that could help us do everything, as opposed to addressing each problem on its own,” he says.

His contributions helped the team win an Emmy Award for sound editing for a nonfiction or reality program, in 2022.

A concerted effort

After 15 years at the University of Illinois, Smaragdis decided to join the faculty at MIT to tackle the thorny, interdisciplinary problems that were emerging in the field. 

His group now focuses on two main areas. For one, they develop systems that make computers more useful tools for musicians. 

One project in this area is enabling machines to understand how to listen to music, so they can provide constructive feedback to musicians.

“We want to enhance the toolbox a musician has. We don’t want to use computers to replace musicians,” he says.

On the other side, his group focuses on the fundamental tools scientists and artists alike can use to process, analyze, and manipulate audio.

For instance, his team has recently been exploring algorithms that allow sophisticated signal processing where traditional frames of reference, such as sampling rates, frame sizes, or context-windows, are not fixed — or even defined. 

That implies different computational structures that are yet unexplored, despite their potential for more efficient processing, Smaragdis says.

“That requires rethinking a lot of the basics, but once you have that ability, you can start getting much more flexible and more powerful systems,” he adds.

They also explore ways to incorporate the latest techniques, like large language models, into speech recognition and audio enhancement.

“AI is an immensely powerful tool, but I don’t think we have found the ideal way to use it for sound and music yet,” he says.

He also enjoys mentoring students and helping them materialize their visions for research projects.

Since few of his students have experience using computation in sound, he emphasizes hands-on lessons so they see and hear how to transform equations into something tangible.

Smaragdis still feels a thrill from making music, whether with a computer or a more traditional instrument. Off campus, he tries to play jazz bass whenever he can find the time, and enjoys exploring the rich musical community of Boston with his wife and 15-year-old son.

Outside the musical realm, he plays and coaches underwater hockey — a sport where players wear diving masks and use sticks to push a heavy puck across the bottom of a pool toward their opponent’s goal. He coached the underwater hockey team at the University of Illinois, and is hoping to revive the MIT underwater hockey club soon and start hosting tournaments on campus.

While underwater hockey may be a lesser-known sport, it fits with Smaragdis’ lifelong interest in pursuing the underexplored.

“I’ve always like doing things that were different from what everyone else was doing. If you can create a new sound that no one has heard before, what does that mean about your ability to create new things? I still find these questions completely fascinating,” he says.

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