Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Widespread news reports indicate that President Donald Trump’s administration has prepared an executive order to punish states that have passed laws attempting to address harms from artificial intelligence (AI) systems. According to a draft published by news outlets, this order would direct federal agencies to bring legal challenges to state AI regulations that the administration deems “onerous,”  to restrict funding to those states that have these laws, and to adopt new federal law that overrides state AI laws.

This approach is deeply misguided.

As we’ve said before, the fact that states are regulating AI is often a good thing. Left unchecked, company and government use of automated decision-making systems in areas such as housing, health care, law enforcement, and employment have already caused discriminatory outcomes based on gender, race, and other protected statuses.

While state AI laws have not been perfect, they are genuine attempts to address harms that people across the country face from certain uses of AI systems right now. Given the tone of the Trump Administration’s draft order, it seems clear that the preemptive federal legislation backed by this administration will not stop ways that automated decision making systems can result in discriminatory decisions.

For example, a copy of the draft order published by Politico specifically names the Colorado AI Act as an example of supposedly “onerous” legislation. As we said in our analysis of Colorado’s law, it is a limited but crucial step—one that needs to be strengthened to protect people more meaningfully from AI harms. It is possible to guard against harms and support innovation and expression. Ignoring the harms that these systems can cause when used in discriminatory ways is not the way to do that.

Again: stopping states from acting on AI will stop progress. Proposals such as the executive order, or efforts to put a broad moratorium on state AI laws into the National Defense Authorization Act (NDAA), will hurt us all. Companies that produce AI and automated decision-making software have spent millions in state capitals and in Congress to slow or roll back legal protections regulating artificial intelligence. If reports about the Trump administration’s executive order are true, those efforts are about to get a supercharged ally in the federal government.

And all of us will pay the price.

Potential Government Coercion Raises First Amendment Concerns

SAN FRANCISCO – The Electronic Frontier Foundation (EFF) sued the departments of Justice (DOJ) and Homeland Security (DHS) today to uncover information about the federal government demanding that tech companies remove apps that document immigration enforcement activities in communities throughout the country. 

Tech platforms took down several such apps (including ICE Block, Red Dot, and DeICER) and webpages (including ICE Sighting-Chicagoland) following communications with federal officials this year, raising important questions about government coercion to restrict protected First Amendment activity.

"We're filing this lawsuit to find out just what the government told tech companies," said EFF Staff Attorney F. Mario Trujillo. "Getting these records will be critical to determining whether federal officials crossed the line into unconstitutional coercion and censorship of protected speech."

In October, Apple removed ICEBlock, an app that allows users to report Immigration and Customs Enforcement (ICE) activity in their area, from its App Store. Attorney General Pamela Bondi publicly took credit for the takedown, telling reporters, “We reached out to Apple today demanding they remove the ICEBlock app from their App Store—and Apple did so.” In the days that followed, Apple removed several similar apps from the App Store. Google and Meta removed similar apps and webpages from platforms they own as well. Bondi vowed to “continue engaging tech companies” on the issue. 

People have a protected First Amendment right to document and share information about law enforcement activities performed in public. If government officials coerce third parties into suppressing protected activity, this can be unconstitutional, as the government cannot do indirectly what it is barred from doing directly.

Last month, EFF submitted Freedom of Information Act (FOIA) requests to the DOJ, DHS and its component agencies ICE and Customs and Border Protection. The requests sought records and communications about agency demands that technology companies remove apps and pages that document immigration enforcement activities. So far, none of the agencies have provided these records. EFF's FOIA lawsuit demands their release.

For the complaint: https://www.eff.org/document/complaint-eff-v-doj-dhs-ice-tracking-apps

For more about the litigation: https://www.eff.org/cases/eff-v-doj-dhs-ice-tracking-apps

Tags: ICEContact:  F. Mario TrujilloStaff Attorneymario@eff.org

Google has filed a complaint in court that details the scam:

In a complaint filed Wednesday, the tech giant accused “a cybercriminal group in China” of selling “phishing for dummies” kits. The kits help unsavvy fraudsters easily “execute a large-scale phishing campaign,” tricking hordes of unsuspecting people into “disclosing sensitive information like passwords, credit card numbers, or banking information, often by impersonating well-known brands, government agencies, or even people the victim knows.”

These branded “Lighthouse” kits offer two versions of software, depending on whether bad actors want to launch SMS and e-commerce scams. “Members may subscribe to weekly, monthly, seasonal, annual, or permanent licenses,” Google alleged. Kits include “hundreds of templates for fake websites, domain set-up tools for those fake websites, and other features designed to dupe victims into believing they are entering sensitive information on a legitimate website.”...

The rule to end most climate regulations is not expected until January, slipping from a planned December deadline.

Rich nations and international funding agencies need to make it easier to obtain money for adapting to the ills of a warming planet, advocates for poorer governments say at COP30.

The report comes as the Trump administration pushes to expand LNG sales abroad and Democrats ramp up affordability messaging.

Most of the at-risk sites are clustered in just seven states: Louisiana, Florida, New Jersey, Texas, California, New York and Massachusetts.

Progressive groups say Supreme Court justices need a more transparent ethics policy.

Two Democrats sought to challenge the rule under the Congressional Review Act.

As Gov. Kathy Hochul considers changes to the state's 2019 climate targets, Democratic lawmakers are split amid affordability concerns.

But Australia will hold the summit's presidency — and therefore control the diplomacy, Climate Minister Chris Bowen told reporters.

The EU did not join 82 countries calling for a phase-out of oil, coal and natural gas.

India, China and other countries are challenging CBAM in climate talks.

Protesters, including potentially affected Indigenous populations, took to streets and rivers this month to oppose the project.

Hosting the bloc's first summit in Africa, South Africa wants to prioritize issues affecting poor countries, including responses to disasters made worse by climate change.

Nature Climate Change, Published online: 20 November 2025; doi:10.1038/s41558-025-02505-9

Objective assessments indicate that extreme heat is increasing health risks; however, many of the most exposed populations do not perceive extreme heat as risky. This misperception may undermine public awareness of the need for effective cooling strategies, leaving a dangerous blind spot in adaptation and protection.

Some 200 light years from Earth, the core of a dead star is circling a larger star in a macabre cosmic dance. The dead star is a type of white dwarf that exerts a powerful magnetic field as it pulls material from the larger star into a swirling, accreting disk. The spiraling pair is what’s known as an “intermediate polar” — a type of star system that gives off a complex pattern of intense radiation, including X-rays, as gas from the larger star falls onto the other one.

Now, MIT astronomers have used an X-ray telescope in space to identify key features in the system’s innermost region — an extremely energetic environment that has been inaccessible to most telescopes until now. In an open-access study published in the Astrophysical Journal, the team reports using NASA’s Imaging X-ray Polarimetry Explorer (IXPE) to observe the intermediate polar, known as EX Hydrae.

The team found a surprisingly high degree of X-ray polarization, which describes the direction of an X-ray wave’s electric field, as well as an unexpected direction of polarization in the X-rays coming from EX Hydrae. From these measurements, the researchers traced the X-rays back to their source in the system’s innermost region, close to the surface of the white dwarf.

What’s more, they determined that the system’s X-rays were emitted from a column of white-hot material that the white dwarf was pulling in from its companion star. They estimate that this column is about 2,000 miles high — about half the radius of the white dwarf itself and much taller than what physicists had predicted for such a system. They also determined that the X-rays are reflected off the white dwarf’s surface before scattering into space — an effect that physicists suspected but hadn’t confirmed until now.

The team’s results demonstrate that X-ray polarimetry can be an effective way to study extreme stellar environments such as the most energetic regions of an accreting white dwarf.

“We showed that X-ray polarimetry can be used to make detailed measurements of the white dwarf's accretion geometry,” says Sean Gunderson, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research, who is the study’s lead author. “It opens the window into the possibility of making similar measurements of other types of accreting white dwarfs that also have never had predicted X-ray polarization signals.”

 

Gunderson’s MIT Kavli co-authors include graduate student Swati Ravi and research scientists Herman Marshall and David Huenemoerder, along with Dustin Swarm of the University of Iowa, Richard Ignace of East Tennessee State University, Yael Nazé of the University of Liège, and Pragati Pradhan of Embry Riddle Aeronautical University.

A high-energy fountain

All forms of light, including X-rays, are influenced by electric and magnetic fields. Light travels in waves that wiggle, or oscillate, at right angles to the direction in which the light is traveling. External electric and magnetic fields can pull these oscillations in random directions. But when light interacts and bounces off a surface, it can become polarized, meaning that its vibrations tighten up in one direction. Polarized light, then, can be a way for scientists to trace the source of the light and discern some details about the source’s geometry.

The IXPE space observatory is NASA’s first mission designed to study polarized X-rays that are emitted by extreme astrophysical objects. The spacecraft, which launched in 2021, orbits the Earth and records these polarized X-rays. Since launch, it has primarily focused on supernovae, black holes, and neutron stars.

The new MIT study is the first to use IXPE to measure polarized X-rays from an intermediate polar — a smaller system compared to black holes and supernovas, that nevertheless is known to be a strong emitter of X-rays.

“We started talking about how much polarization would be useful to get an idea of what’s happening in these types of systems, which most telescopes see as just a dot in their field of view,” Marshall says.

An intermediate polar gets its name from the strength of the central white dwarf’s magnetic field. When this field is strong, the material from the companion star is directly pulled toward the white dwarf’s magnetic poles. When the field is very weak, the stellar material instead swirls around the dwarf in an accretion disk that eventually deposits matter directly onto the dwarf’s surface.

In the case of an intermediate polar, physicists predict that material should fall in a complex sort of in-between pattern, forming an accretion disk that also gets pulled toward the white dwarf’s poles. The magnetic field should lift the disk of incoming material far upward, like a high-energy fountain, before the stellar debris falls toward the white dwarf’s magnetic poles, at speeds of millions of miles per hour, in what astronomers refer to as an “accretion curtain.” Physicists suspect that this falling material should run up against previously lifted material that is still falling toward the poles, creating a sort of traffic jam of gas. This pile-up of matter forms a column of colliding gas that is tens of millions of degrees Fahrenheit and should emit high-energy X-rays.

An innermost picture

By measuring any polarized X-rays emitted by EX Hydrae, the team aimed to test the picture of intermediate polars that physicists had hypothesized. In January 2025, IXPE took a total of about 600,000 seconds, or about seven days’ worth, of X-ray measurements from the system.

“With every X-ray that comes in from the source, you can measure the polarization direction,” Marshall explains. “You collect a lot of these, and they’re all at different angles and directions which you can average to get a preferred degree and direction of the polarization.”

Their measurements revealed an 8 percent polarization degree that was much higher than what scientists had predicted according to some theoretical models. From there, the researchers were able to confirm that the X-rays were indeed coming from the system’s column, and that this column is about 2,000 miles high.

“If you were able to stand somewhat close to the white dwarf’s pole, you would see a column of gas stretching 2,000 miles into the sky, and then fanning outward,” Gunderson says.

The team also measured the direction of EX Hydrae’s X-ray polarization, which they determined to be perpendicular to the white dwarf’s column of incoming gas. This was a sign that the X-rays emitted by the column were then bouncing off the white dwarf’s surface before traveling into space, and eventually into IXPE’s telescopes.

“The thing that’s helpful about X-ray polarization is that it’s giving you a picture of the innermost, most energetic portion of this entire system,” Ravi says. “When we look through other telescopes, we don’t see any of this detail.”

The team plans to apply X-ray polarization to study other accreting white dwarf systems, which could help scientists get a grasp on much larger cosmic phenomena.

“There comes a point where so much material is falling onto the white dwarf from a companion star that the white dwarf can’t hold it anymore, the whole thing collapses and produces a type of supernova that’s observable throughout the universe, which can be used to figure out the size of the universe,” Marshall offers. “So understanding these white dwarf systems helps scientists understand the sources of those supernovae, and tells you about the ecology of the galaxy.”

This research was supported, in part, by NASA.

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a lot better at these things.

A new generation of LLMs known as reasoning models are being trained to solve complex problems. Like humans, they need some time to think through problems like these — and remarkably, scientists at MIT’s McGovern Institute for Brain Research have found that the kinds of problems that require the most processing from reasoning models are the very same problems that people need take their time with. In other words, they report today in the journal PNAS, the “cost of thinking” for a reasoning model is similar to the cost of thinking for a human.

The researchers, who were led by Evelina Fedorenko, an associate professor of brain and cognitive sciences and an investigator at the McGovern Institute, conclude that in at least one important way, reasoning models have a human-like approach to thinking. That, they note, is not by design. “People who build these models don’t care if they do it like humans. They just want a system that will robustly perform under all sorts of conditions and produce correct responses,” Fedorenko says. “The fact that there’s some convergence is really quite striking.”

Reasoning models

Like many forms of artificial intelligence, the new reasoning models are artificial neural networks: computational tools that learn how to process information when they are given data and a problem to solve. Artificial neural networks have been very successful at many of the tasks that the brain’s own neural networks do well — and in some cases, neuroscientists have discovered that those that perform best do share certain aspects of information processing in the brain. Still, some scientists argued that artificial intelligence was not ready to take on more sophisticated aspects of human intelligence.

“Up until recently, I was among the people saying, ‘These models are really good at things like perception and language, but it’s still going to be a long ways off until we have neural network models that can do reasoning,” Fedorenko says. “Then these large reasoning models emerged and they seem to do much better at a lot of these thinking tasks, like solving math problems and writing pieces of computer code.”

Andrea Gregor de Varda, a K. Lisa Yang ICoN Center Fellow and a postdoc in Fedorenko’s lab, explains that reasoning models work out problems step by step. “At some point, people realized that models needed to have more space to perform the actual computations that are needed to solve complex problems,” he says. “The performance started becoming way, way stronger if you let the models break down the problems into parts.”

To encourage models to work through complex problems in steps that lead to correct solutions, engineers can use reinforcement learning. During their training, the models are rewarded for correct answers and penalized for wrong ones. “The models explore the problem space themselves,” de Varda says. “The actions that lead to positive rewards are reinforced, so that they produce correct solutions more often.”

Models trained in this way are much more likely than their predecessors to arrive at the same answers a human would when they are given a reasoning task. Their stepwise problem-solving does mean reasoning models can take a bit longer to find an answer than the LLMs that came before — but since they’re getting right answers where the previous models would have failed, their responses are worth the wait.

The models’ need to take some time to work through complex problems already hints at a parallel to human thinking: if you demand that a person solve a hard problem instantaneously, they’d probably fail, too. De Varda wanted to examine this relationship more systematically. So he gave reasoning models and human volunteers the same set of problems, and tracked not just whether they got the answers right, but also how much time or effort it took them to get there.

Time versus tokens

This meant measuring how long it took people to respond to each question, down to the millisecond. For the models, Varda used a different metric. It didn’t make sense to measure processing time, since this is more dependent on computer hardware than the effort the model puts into solving a problem. So instead, he tracked tokens, which are part of a model’s internal chain of thought. “They produce tokens that are not meant for the user to see and work on, but just to have some track of the internal computation that they’re doing,” de Varda explains. “It’s as if they were talking to themselves.”

Both humans and reasoning models were asked to solve seven different types of problems, like numeric arithmetic and intuitive reasoning. For each problem class, they were given many problems. The harder a given problem was, the longer it took people to solve it — and the longer it took people to solve a problem, the more tokens a reasoning model generated as it came to its own solution.

Likewise, the classes of problems that humans took longest to solve were the same classes of problems that required the most tokens for the models: arithmetic problems were the least demanding, whereas a group of problems called the “ARC challenge,” where pairs of colored grids represent a transformation that must be inferred and then applied to a new object, were the most costly for both people and models.

De Varda and Fedorenko say the striking match in the costs of thinking demonstrates one way in which reasoning models are thinking like humans. That doesn’t mean the models are recreating human intelligence, though. The researchers still want to know whether the models use similar representations of information to the human brain, and how those representations are transformed into solutions to problems. They’re also curious whether the models will be able to handle problems that require world knowledge that is not spelled out in the texts that are used for model training.

The researchers point out that even though reasoning models generate internal monologues as they solve problems, they are not necessarily using language to think. “If you look at the output that these models produce while reasoning, it often contains errors or some nonsensical bits, even if the model ultimately arrives at a correct answer. So the actual internal computations likely take place in an abstract, non-linguistic representation space, similar to how humans don’t use language to think,” he says.

As an undergraduate majoring in architecture, Dong Nyung Lee ’21 wasn’t sure how to respond when friends asked him what the study of architecture was about.

“I was always confused about how to describe it myself,” he says with a laugh. “I would tell them that it wasn’t just about a building, or a city, or a community. It’s a balance across different scales, and it has to touch everything all at once.”

As a graduate student enrolled in a design studio course last spring — 4.154 (Territory as Interior) — Lee and his classmates had to design a building that would serve a specific community in a specific location. The course, says Lee, gave him clarity as to “what architecture is all about.”

Designed by Roi Salgueiro Barrio, a lecturer in the MIT School of Architecture and Planning’s Department of Architecture, the coursework combines ecological principles, architectural design, urban economics, and social considerations to address real-world problems in marginalized or degraded areas.

“When we build, we always impact economies, mostly by the different types of technologies we use and their dependence on different types of labor and materials,” says Salgueiro Barrio. “The intention here was to think at both levels: the activities that can be accommodated, and how we can actually build something.”

Research first

Students were tasked with repurposing an abandoned fishing industry building on the Barbanza Peninsula in Galicia, Spain, and proposing a new economic activity for the building that would help regenerate the local economy. Working in groups, they researched the region’s material resources and fiscal sectors and designed detailed maps. This approach to constructing a building was new for Vincent Jackow a master's student in architecture.

“Normally in architecture, we work at the scale of one-to-100 meters,” he says. But this process allowed me to connect the dots between what the region offered and what could be built to support the economy.”

The aim of revitalizing this area is also a goal of Fundación RIA (FRIA), a nonprofit think tank established by Pritzker Prize-winning architect David Chipperfield. FRIA generates research and territorial planning with the goal of long-term sustainability of the built and natural environment in the Galicia region. During their spring break in March, the students traveled to Galicia, met with Chipperfield, business owners, fishermen, and farmers, and explored a variety of sites. They also consulted with the owner of the building they were to repurpose.

Returning to MIT, the students constructed nine detailed models. Master’s student Aleks Banaś says she took the studio because it required her to explore the variety of scales in an architectural project from territorial analysis to building detail, all while keeping the socio-economic aspect of design decisions in mind.

“I’m interested in how architecture can support local economies,” says Banaś. “Visiting Galicia was very special because of the communities we interacted with. We were no longer looking at articles and maps of the region; we were learning about day-to-day life. A lot of people shared with us the value of their work, which is not economically feasible.”

Banaś was impressed by the region’s strong maritime history and the generations of craftspeople working on timber boat-making. Inspired by the collective spirit of the region, she designed “House of Sea,” transforming the former cannery into a hub for community gathering and seafront activities. The reimagined building would accommodate a variety of functions including a boat-building workshop for the Ribeira carpenters’ association, a restaurant, and a large, covered section for local events such as the annual barnacle festival.

“I wanted to demonstrate how we can create space for an alternative economy that can host and support these skills and traditions,” says Banaś. 

Jackow’s building — “La Nueva Cordelería,” or “New Rope Making” — was a facility using hemp to produce rope and hempcrete blocks (a construction material). The production of both “is very on-trend in the E.U.” and provides an alternative to petrochemical-based ropes for the region’s marine uses, says Jackow. The building would serve as a cultural hub, incorporating a café, worker housing, and offices. Even its very structure would also make use of the rope by joining timber with knots allowing the interior spaces to be redesigned.

Lee’s building was designed to engage with the forestry and agricultural industries.

“What intrigued me was that Galicia is heavily dependent on pulp production and wood harvesting,” he says. “I wanted to give value to the post-harvest residue.”

Lee designed a biochar plant using some of the concrete and terra cotta blocks on site. Biochar is made by heating the harvested wood residue through pyrolysis — thermal decomposition in an environment with little oxygen. The resulting biochar would be used by farmers for soil enhancement.

“The work demonstrated an understanding of the local resources and using them to benefit the revitalization of the area,” says Salgueiro Barrio, who was pleased with the results. 

FRIA was so impressed with the work that they held an exhibition at their gallery in Santiago de Compostela in August and September to highlight the importance of connecting academic research with the territory through student projects. Banaś interned with FRIA over the summer working on multiple projects, including the plan and design for the exhibition. The challenge here, she says, was to design an exhibition of academic work for a general audience. The final presentation included maps, drawings, and photographs by the students.

For Lee, the course was more meaningful than any he has taken to date. Moving between the different scales of the project illustrated, for him, “the biggest challenge for a designer and an architect. Architecture is universal, and very specific. Keeping those dualities in focus was the biggest challenge and the most interesting part of this project. It hit at the core of what architecture is.”